WO2024246710A1

WO2024246710A1 - Method of and system for pre-deployment and post-deployment optimization of reconfigurable intelligent surfaces

Info

Publication number: WO2024246710A1
Application number: PCT/IB2024/055101
Authority: WO
Inventors: Gursimran Singh SETHI; Mohamed Ibrahim; Shahin SHEIKH; Paul TORNATTA Jr.
Original assignee: Latys Intelligence Inc
Current assignee: Latys Intelligence Inc
Priority date: 2023-05-26
Filing date: 2024-05-24
Publication date: 2024-12-05
Anticipated expiration: 2025-11-26

Abstract

There are provided methods and systems for performing pre-deployment and post- deployment optimization of parameters of reconfigurable intelligent surfaces (RISs) for propagation of electromagnetic (EM) signals between transmitters and receivers in wireless environments comprising a plurality of objects. The pre-deployment optimization includes use of wave propagation models based on a layout of the environment to determine propagation paths of the EM signals between the transmitter and the receiver. An optimization algorithm is used to determine locations and parameters of at least one RIS. The post deployment optimization includes receiving RIS parameter of at least one RIS, receiving an angle of arrival (AoA) of incident EM signals on the RIS and determining updated RIS parameters to maximize EM signals received by the receiver.

Description

METHOD OF AND SYSTEM FOR PRE-DEPLOYMENT AND POST- DEPLOYMENT OPTIMIZATION OF RECONFIGURABLE INTELLIGENT SURFACES CROSS-REFERENCE TO RELATED APPLICATIONS [0001] The present application claims priority from U.S. Provisional Patent Application No.63/504,636 filed on May 26, 2023. FIELD [0002] The present technology relates to wireless communication in general and more specifically to methods and systems for pre-deployment and post-deployment optimization of one or more reconfigurable intelligent surfaces (RISs). BACKGROUND [0003] Reconfigurable intelligent surfaces (RISs) have been identified as a promising technology for future wireless communication networks due to their numerous advantages, including ease of development, low cost, and improved spectral and power efficiencies. The concept of RIS is based on using small, low-cost, and active/passive elements to control the propagation of an incident signal. Each RIS element can adjust its amplitude and phase shift to effectively manipulate the signal direction and strength without requiring any signal processing. RISs can be designed and fabricated to conform to various infrastructural surfaces, such as walls, ceilings, and buildings, making it an easy and practical solution. RISs can be classified into two types based on their functionality: reflective and transmissive. Reflective RISs reflect the incident signals in a desired direction by altering the phase or amplitude of the reflected signals. On the other hand, transmissive RISs can transmit signals by reconfiguring the phase or amplitude of the transmitted signals at certain direction. [0004] RIS can play an important role in improving Quality of Service (QoS) for wireless communication systems, including data rate, latency, and other performance metrics. RIS can help to improve the data rate by enhancing the strength and reliability of wireless signals. By optimizing the coefficients of RIS elements, RIS can improve signal-to-noise ratio (SNR), reduce fading, and increase spectral efficiency, resulting in higher data rates. RISs can also be used to reduce latency in wireless communication networks by relaying signals directly on the physical layer. Traditional wireless communication systems often suffer from high latency due to signal processing, interference, signal attenuation, and other factors. However, by optimizing the signal propagation path using RISs, it is possible to reduce the distance between the transmitter and receiver, thereby reducing the time it takes for the signal to travel which leads to less overall latency. [0005] In addition to reducing latency, RISs can also be used to reduce interference in wireless communication networks where RIS establish a more dependable communication channel and prevent disruptions from external sources by carefully managing signals. This is important in crowded wireless environments where multiple devices are competing for bandwidth and signals can easily become distorted or blocked. RIS can be used to generate reconfigurable directive radiation which can be used to dynamically manage the QoS in wireless communication networks. By adjusting the RIS elements complex weights, including phase and amplitude, in real- time, RIS can be adapted to changes in network conditions and optimize the signal path to ensure the best possible QoS. This is particularly important in environments where network conditions can be unpredictable, such as in moving vehicles or in outdoor environments subject to weather conditions. Therefore, RIS technology has the potential to significantly improve QoS in wireless communication networks by reducing latency, improving signal strength, reducing interference, and providing dynamic QoS management. SUMMARY [0006] It is an object of the present technology to ameliorate at least some of the inconveniences present in existing technologies. One or more implementations of the present technology may provide and/or broaden the scope of approaches to and/or methods of achieving the aims and objects of the present technology. [0007] One or more implementations of the present technology have been developed based on developers^ appreciation that the performance of RIS technology depends on a multitude of RIS parameters and environmental factors. One of the parameters is the number of RISs deployed in the environment, which affects the signal strength and coverage area of the network. [0008] The more RISs and RIS elements deployed, the higher the potential for signal strength and coverage. The placement of RISs in the environment affects the signal propagation and coverage area. However, developers have appreciated that factors such as cost, power consumption, and deployment constraints do not allow for an arbitrary placement of RISs in a wireless network. Thus, developers of the present technology have appreciated that RISs should be placed strategically to ensure optimal coverage. [0009] Besides the parameters of RIS, the reflection and scattering properties of the environment in which the RIS is deployed can impact the signal propagation and coverage area. The presence of objects or obstacles, such as walls or buildings, can cause signal degradation, which can impact performance. Moreover, the interference from other wireless devices operating in the same frequency band can affect the performance of the system. [0010] Deploying RIS effectively requires careful planning and consideration of various parameters, such as the type of RIS (reflective or transmissive), radiative characteristics of the RIS, size, placement, and number of RIS units. [0011] A planning tool that considers those parameters could help in optimizing RIS deployment, ensure maximum benefits and help in allocating resources efficiently by predicting RIS performance under various conditions and identifying the most effective RIS configurations. [0012] Incorporating environmental factors, such as reflection, multipath propagations, absorption, and others into a planning tool for RIS technology would be needed to optimize RIS configuration, enhance energy efficiency, simulate behavior in complex environments, and accurately predict RIS performance. [0013] On the other hand, RIS technology is dynamic and requires continuous monitoring and adaptation to changing environmental conditions and user requirements. A planning tool that considers RIS parameters could support real-time adaptation by providing accurate and up-to-date information on RIS performance and by enabling automatic adjustment of RIS configurations. [0014] The computational and physical complexity of predicting the path of wireless signals in a crowded and radio-hostile environment may be significant in some instances. [0015] In a crowded radio environment, there may be obstacles and sources of interference that can affect the propagation of wireless signals. This includes buildings, trees, other wireless devices, and even people. Predicting the path of signals in such an environment requires detailed knowledge of the physical layout and characteristics of the obstacles. [0016] A radio-hostile environment is one in which there is a high level of electromagnetic interference. This can be caused by other wireless devices, electrical equipment, and even natural sources like lightning. In such an environment, it can be difficult to accurately predict the path of wireless signals due to the unpredictable nature of the interference. [0017] Predicting the path of wireless signals in a crowded and radio-hostile environment requires mathematical models and algorithms. These models must consider factors such as the frequency of the signal, the physical characteristics of the transmitter and receiver, and the presence of obstacles and interference. These calculations can be very computationally intensive, especially if the environment is large or complex. [0018] In addition to the computational complexity, predicting the path of wireless signals in a crowded and radio-hostile environment can also be physically complex. This is because it may require the deployment of multiple sensors or antennas to accurately measure the signal strength and direction at different points in the environment. This can be time-consuming and expensive, especially if the environment is large or difficult to access. [0019] Overall, predicting the path of wireless signals in a crowded and radio- hostile environment is a complex task that requires a combination of advanced mathematical models, sophisticated algorithms, and/or specialized software/hardware. [0020] One or more implementations of the present technology provide methods, systems and non-transitory computer-readable storage mediums to accurately model and optimize the performance of RIS-enable network that can be implemented before the deployment of the network (^RIS pre-deployment optimization^) as well as used as a post-deployment dynamic optimization tool (^RIS post-deployment optimization^) to accurately model the wireless propagation environment and adapt to changes in the environment dynamically. [0021] One or more implementations of the present technology provide pre- deployment optimization techniques to model a wireless propagation environment and outputs the number of RISs needed to provide optimum customized performance as a function of coverage, cost, data rate and latency in an environment (e.g., a facility). The inputs to the pre-deployment optimization techniques include inter alia the geometrical layout, and material characteristics of objects in the environment, and the outputs include inter alia the number, characteristics, and optimal placement of reconfigurable intelligent surfaces (RISs). Furthermore, the pre-deployment optimization may be used to optimize the required number of transmitters (e.g., access points or base stations) when combined with reconfigurable intelligent surfaces (RISs) to achieve the desired performance. [0022] One or more implementations of the present technology provide post- deployment optimization techniques, which enable optimizing, predicting and tracking the changes in the wireless propagation environment and dynamically reconfigures the RISs to maintain robust connectivity after deployment of the network. [0023] One or more implementations of the present technology can be deployed by network providers, system integrators and facility owners who provide wireless connectivity in complex radio-hostile environments, including exterior and/or interior environments such as, but not limited to: warehouses, smart-cities, stadiums, and the like. [0024] Optimizing the placement and number of reconfigurable intelligent surfaces (RISs) in a network deployment may also provide for one or more of: [0025] Improved network coverage: The placement of RISs can be optimized to improve network coverage and fill coverage gaps, thereby reducing dead zones and improving the quality of service for end-users. [0026] Enhanced network capacity: RISs can be used to improve network capacity by increasing signal strength and reducing interference in areas with high traffic, allowing more devices to connect to the network simultaneously. [0027] Reduced network congestion: By selectively redirecting signals, RISs can help to reduce network congestion, improving the performance and reliability of the network. [0028] Lower energy consumption: Optimized placement of RISs can help to reduce the power consumption of wireless devices, as they do not have to use as much power to reach the intended destination. Overall network power consumption can be reduced and optimized to allow for efficient power distribution in wireless networks. [0029] An optimization algorithm can create value for customers and OEMs in several ways, including: [0030] Improved network performance: By optimizing the placement and number of RISs, an optimization algorithm can improve network performance, leading to faster data transfer rates, fewer dropped connections, and better overall network reliability. [0031] Reduced costs: by minimizing the number of RISs needed to achieve the desired network performance, an optimization algorithm can help to reduce deployment and maintenance costs, making the network more cost-effective for customers and OEMs. [0032] Faster time-to-market: by automating the process of optimizing RIS placement and number, an optimization algorithm can help OEMs to deploy networks more quickly and efficiently, reducing time-to-market and increasing revenue potential. [0033] Competitive advantage: by optimizing network performance, reducing costs, and improving time-to-market, an optimization algorithm can help OEMs to gain a competitive advantage in the marketplace, attracting more customers and increasing market share. [0034] Overall, the advantages of optimizing the placement and number of RISs in a network deployment, combined with the value created by an optimization algorithm, enable improving wireless network performance and reliability. [0035] Thus, one or more implementations of the present technology are directed to methods and systems for pre-deployment and post-deployment optimization of reconfigurable intelligent surfaces (RISs). [0036] In accordance with a broad aspect of the present technology, there is provided a method for determining parameters of at least one reconfigurable intelligent surface (RIS) for propagating electromagnetic (EM) signals received from a transmitter in a wireless environment. The method is executed by at least one processor. The method comprises: receiving a geometrical layout of the environment, the geometrical layout of the environment comprising: a set of objects associated with respective object locations, and the transmitter associated with a transmitter location, receiving, for each object of the set of objects in the geometrical layout of the environment, respective material characteristics influencing propagation of the EM signals in the environment, receiving wave transmission parameters indicative of a signal strength of the EM signals transmitted by the transmitter, simulating, using a wave propagation model, based on the geometrical layout of the environment, the wave transmission parameters, and the respective material characteristics of the set of objects, possible propagation paths of the EM signals, each propagation path being associated with a respective received EM signal power, determining, using an optimization algorithm, based on: the transmitter location, the wave transmission parameters and the respective received EM signal power, a set of locations for the at least one RIS, and determining parameters of the at least one RIS, the at least one RIS comprising a plurality of elements, the parameters comprising: a size of the RIS and spacing between the plurality of elements of the at least one RIS. [0037] In accordance with a broad aspect of the present technology, there is provided a method for determining parameters of at least one reconfigurable intelligent surface (RIS) for propagating electromagnetic (EM) signals received from a transmitter in a wireless environment. The method is executed by at least one processor, the method comprises: receiving a geometrical layout of the wireless environment, the geometrical layout of the wireless environment comprising: a set of objects associated with respective object locations, and the transmitter associated with a transmitter location, receiving, for each object of the set of objects in the geometrical layout of the wireless environment, respective material characteristics influencing propagation of the EM signals in the wireless environment, receiving wave transmission parameters indicative of a signal strength of the EM signals transmitted by the transmitter, simulating, using a wave propagation model, based on the geometrical layout of the wireless environment, the wave transmission parameters, and the respective material characteristics of the set of objects, possible propagation paths of the EM signals, each propagation path being associated with a respective received EM signal power, determining, using an optimization algorithm, based on: the transmitter location, the wave transmission parameters and the respective received EM signal power of each propagation path, a set of locations for the at least one RIS, and determining parameters of the at least one RIS, the at least one RIS comprising a plurality of elements, the parameters comprising: a size of the at least one RIS and spacing between the plurality of elements of the at least one RIS. [0038] In one or more implementations of the method, the respective material characteristics of each object comprise: a respective size, a respective permeability, a respective conductivity, and a respective permittivity thereof. [0039] In one or more implementations of the method, the wave transmission parameters comprise at least one of: transmission power, antenna gain, antenna height, frequency band and channel bandwidth. [0040] In one or more implementations of the method, the at least one RIS comprises a plurality of RISs, and said determining parameters of the at least one RIS comprises determining a number of the plurality of RISs. [0041] In one or more implementations of the method, the parameters of the at least one RIS comprise: a beam directionality, an amplitude control unit type, a power consumption, a phase shifter type, and a radiator type. [0042] In one or more implementations of the method, said determining, using the optimization algorithm, the set of locations of the RIS comprises: solving an objective problem, the objective problem comprising maximizing a signal to interference and noise ratio (SINR) of the received signal power. [0043] In one or more implementations of the method, said determining, using the optimization algorithm, based on: the transmitter location, the wave transmission parameters and the respective received EM signal power, the set of locations for the at least one RIS further comprises determining a new transmitter location and new transmitter parameters for the transmitter. [0044] In one or more implementations of the method, the objective problem further comprises: minimizing a power consumption of the at least one RIS. [0045] In one or more implementations of the method, the objective problem further comprises at least one of: minimizing a power consumption of the transmitter, minimizing a deployment cost of the at least one RIS, maximizing a received power by a given receiver in the wireless environment, and minimizing an average power leakage from at least an area of the wireless environment. [0046] In one or more implementations of the method, the at least one RIS comprises at least one of: a reflective RIS and a transmissive RIS. [0047] In one or more implementations of the method, the wireless environment comprises at least one receiver associated with a respective receiver location, said determining, using the optimization algorithm, based on: the transmitter location, the wave transmission parameters and the respective received EM signal power, the set of locations of the at least one RIS is further based on the respective receiver location. [0048] In one or more implementations of the method, the wave propagation model comprises a ray tracing propagation model. [0049] In one or more implementations of the method, said simulating, using the wave propagation model comprising the ray tracing propagation model, based on the geometrical layout of the wireless environment, the wave transmission parameters and the respective material characteristics of the set of objects, possible propagation paths of the EM signals in the wireless environment: calculating at least one feasible region representative of reception positions at which a given propagation path of the EM signals is valid. [0050] In one or more implementations of the method, said calculating the at least one feasible region comprises determining a plurality of order of reflections of the EM signals. [0051] In one or more implementations of the method, the wave propagation model comprises a COST Hata wave propagation model. [0052] In one or more implementations of the method, the optimization algorithm comprises a metaheuristic algorithm. [0053] In one or more implementations of the method, the metaheuristic algorithm comprises one of: a genetic algorithm, Particle Swarm optimization, Brainstorming optimization, Tabu Search, Simulated Annealing, and Hill Climbing. [0054] In one or more implementations of the method, the method further comprises: training a machine learning model to determine the set of locations of the at least one RIS based on: the transmitter location, the wave transmission parameters and the possible propagation paths of the EM signals, each propagation path being associated with the respective received EM signal power. [0055] In one or more implementations of the method, the set of objects comprises a plurality of objects. [0056] In accordance with a broad aspect of the present technology, there is provided a method for determining optimal parameters of at least one reconfigurable intelligence surface (RIS) propagating electromagnetic (EM) signals between a transmitter and at least one receiver in a wireless environment, the at least one RIS comprising an array of a plurality of elements. The method is executed by at least one processor connected to the at least one RIS. The method comprises: receiving respective RIS parameters of the at least one RIS, the respective RIS parameters comprising a phase of each of the plurality of elements, receiving an angle of arrival (AoA) of incident EM signals received by each of the plurality of elements of the RIS, and determining, based on the AoA, updated RIS parameters to maximize EM signals received by the at least one receiver, the updated RIS parameters comprising at least an updated phase of each of the plurality of elements. [0057] In one or more implementations of the method, the method further comprises, prior to receiving the AOA received by each of the plurality of elements of the RIS: receiving incident EM signal parameters, and estimating, based on the incident EM signal parameters, the at least one respective position and the RIS parameters of the at least one RIS, an angle of arrival (AoA) received by each of the plurality of elements. [0058] In one or more implementations of the method, the method further comprises, prior to said determining the updated RIS parameters to maximize EM signals received by the at least one receiver: determining the respective positions of the at least one receiver. [0059] In one or more implementations of the method, the method further comprises: transmitting the updated RIS parameters to cause the at least one RIS to adjust the phase of the plurality of elements. [0060] In one or more implementations of the method, the at least one processor executes at least one trained machine learning (ML) model, the at least one trained ML model being configured for determining, based on the AoA, the updated RIS parameters to maximize the EM signals received by the at least one receiver, the updated RIS parameters comprising the updated phase of each of the plurality of elements. [0061] In one or more implementations of the method, the at least one trained ML model comprises at least one of: reinforcement learning (RL), deep learning, convolutional neural network (CNN), and support vector machines (SVMs). [0062] The methods may be implemented in the form of computer-readable instructions stored on a non-transitory storage medium executable by at least one processing device. [0063] In accordance with a broad aspect of the present technology, there is provided a system for determining parameters of at least one reconfigurable intelligent surface (RIS) for propagating electromagnetic (EM) signals received from a transmitter in a wireless environment. The system comprises: a non-transitory storage medium storing computer-readable instructions thereon, and at least one processor operatively connected to the non-transitory storage medium. The at least one processor, upon executing the computer-readable instructions, is configured for: receiving a geometrical layout of the wireless environment, the geometrical layout of the wireless environment comprising: a set of objects associated with respective object locations, and the transmitter associated with a transmitter location, receiving, for each object of the set of objects in the geometrical layout of the wireless environment, respective material characteristics influencing propagation of the EM signals in the wireless environment, receiving wave transmission parameters indicative of a signal strength of the EM signals transmitted by the transmitter, simulating, using a wave propagation model, based on the geometrical layout of the wireless environment, the wave transmission parameters, and the respective material characteristics of the set of objects, possible propagation paths of the EM signals, each propagation path being associated with a respective received EM signal power, determining, using an optimization algorithm, based on: the transmitter location, the wave transmission parameters and the respective received EM signal power of each propagation path, a set of locations for the at least one RIS, and determining parameters of the at least one RIS, the at least one RIS comprising a plurality of elements, the parameters comprising: a size of the at least one RIS and spacing between the plurality of elements of the at least one RIS. [0064] In one or more implementations of the system, the respective material characteristics of each object comprise: a respective size, a respective permeability, a respective conductivity, and a respective permittivity thereof. [0065] In one or more implementations of the system, the wave transmission parameters comprise at least one of: transmission power, antenna gain, antenna height, frequency band and channel bandwidth. [0066] In one or more implementations of the system, the at least one RIS comprises a plurality of RISs, and said determining parameters of the at least one RIS comprises determining a number of the plurality of RISs. [0067] In one or more implementations of the system, the parameters of the at least one RIS comprise: a beam directionality, an amplitude control unit type, a power consumption, a phase shifter type, and a radiator type. [0068] In one or more implementations of the system, said determining, using the optimization algorithm, the set of locations of the RIS comprises: solving an objective problem, the objective problem comprising maximizing a signal to interference and noise ratio (SINR) of the received signal power. [0069] In one or more implementations of the system, said determining, using the optimization algorithm, based on: the transmitter location, the wave transmission parameters and the respective received EM signal power, the set of locations for the at least one RIS further comprises determining a new transmitter location and new transmitter parameters for the transmitter. [0070] In one or more implementations of the system, the objective problem further comprises: minimizing a power consumption of the at least one RIS. [0071] In one or more implementations of the system, the objective problem further comprises at least one of: minimizing a power consumption of the transmitter, minimizing a deployment cost of the at least one RIS, maximizing a received power by a given receiver in the wireless environment, and minimizing an average power leakage from at least an area of the wireless environment. [0072] In one or more implementations of the system, the at least one RIS comprises at least one of: a reflective RIS and a transmissive RIS. [0073] In one or more implementations of the system, the wireless environment comprises at least one receiver associated with a respective receiver location, said determining, using the optimization algorithm, based on: the transmitter location, the wave transmission parameters and the respective received EM signal power, the set of locations of the at least one RIS is further based on the respective receiver location. [0074] In one or more implementations of the system, the wave propagation model comprises a ray tracing propagation model. In one or more implementations of the system, said simulating, using the wave propagation model comprising the ray tracing propagation model, based on the geometrical layout of the wireless environment, the wave transmission parameters and the respective material characteristics of the set of objects, possible propagation paths of the EM signals in the wireless environment: calculating at least one feasible region representative of reception positions at which a given propagation path of the EM signals is valid. [0075] In one or more implementations of the system, said calculating the at least one feasible region comprises determining a plurality of order of reflections of the EM signals. [0076] In one or more implementations of the system, the wave propagation model comprises a COST Hata wave propagation model. [0077] In one or more implementations of the system, the optimization algorithm comprises a metaheuristic algorithm. [0078] In one or more implementations of the system, the metaheuristic algorithm comprises one of: a genetic algorithm, Particle Swarm optimization, Brainstorming optimization, Tabu Search, Simulated Annealing, and Hill Climbing. [0079] In one or more implementations of the system, the at least one processor is further configured for: training a machine learning model to determine the set of locations of the at least one RIS based on: the transmitter location, the wave transmission parameters and the possible propagation paths of the EM signals, each propagation path being associated with the respective received EM signal power. [0080] In one or more implementations of the system, the set of objects comprises a plurality of objects. [0081] In accordance with a broad aspect of the present technology, there is provided a system for determining optimal parameters of at least one reconfigurable intelligence surface (RIS) propagating electromagnetic (EM) signals between a transmitter and at least one receiver in a wireless environment, the at least one RIS comprising an array of a plurality of elements. The system comprises: a non-transitory storage medium storing computer-readable instructions thereon, and at least one processor operatively connected to the non-transitory storage medium. The at least one processor, upon executing the computer-readable instructions, is configured for: receiving respective RIS parameters of the at least one RIS, the respective RIS parameters comprising a phase of each of the plurality of elements, receiving an angle of arrival (AoA) of incident EM signals received by each of the plurality of elements of the RIS, and determining, based on the AoA, updated RIS parameters to maximize EM signals received by the at least one receiver, the updated RIS parameters comprising at least an updated phase of each of the plurality of elements. [0082] In one or more implementations of the system, the at least one processor is further configured for, prior to receiving the AOA received by each of the plurality of elements of the RIS: receiving incident EM signal parameters, and estimating, based on the incident EM signal parameters, the at least one respective position and the RIS parameters of the at least one RIS, an angle of arrival (AoA) received by each of the plurality of elements. [0083] In one or more implementations of the system, the at least one processor is further configured for, prior to said determining the updated RIS parameters to maximize EM signals received by the at least one receiver: determining the respective positions of the at least one receiver. [0084] In one or more implementations of the system, the at least one processor is further configured for: transmitting the updated RIS parameters to cause the at least one RIS to adjust the phase of the plurality of elements. [0085] In one or more implementations of the system, the at least one processor executes at least one trained machine learning (ML) model, the at least one trained ML model being configured for determining, based on the AoA, the updated RIS parameters to maximize the EM signals received by the at least one receiver, the updated RIS parameters comprising the updated phase of each of the plurality of elements. [0086] In one or more implementations of the system, the at least one trained ML model comprises at least one of: reinforcement learning (RL), deep learning, convolutional neural network (CNN), and support vector machines (SVMs). [0087] Terms and Definitions [0088] In the context of the present specification, a ^server^ is a computer program that is running on appropriate hardware and is capable of receiving requests (e.g., from electronic devices) over a network (e.g., a communication network), and carrying out those requests, or causing those requests to be carried out. The hardware may be one physical computer or one physical computer system, but neither is required to be the case with respect to the present technology. In the present context, the use of the expression a ^server^ is not intended to mean that every task (e.g., received instructions or requests) or any particular task will have been received, carried out, or caused to be carried out, by the same server (i.e., the same software and/or hardware); it is intended to mean that any number of software elements or hardware devices may be involved in receiving/sending, carrying out or causing to be carried out any task or request, or the consequences of any task or request; and all of this software and hardware may be one server or multiple servers, both of which are included within the expressions ^at least one server^ and ^a server^. [0089] In the context of the present specification, ^computing device^ or ^electronic device^ is any computing apparatus or computer hardware that is capable of running software appropriate to the relevant task at hand. Thus, some (non-limiting) examples of computing devices include general purpose personal computers (desktops, laptops, netbooks, etc.), mobile computing devices, smartphones, and tablets, and network equipment such as routers, switches, and gateways. It should be noted that an electronic device in the present context is not precluded from acting as a server to other electronic devices. The use of the expression ^an electronic device^ does not preclude multiple electronic devices being used in receiving/sending, carrying out or causing to be carried out any task or request, or the consequences of any task or request, or steps of any method described herein. In the context of the present specification, a ^client device^ refers to any of a range of end-user client electronic devices, associated with a user, such as personal computers, tablets, smartphones, and the like. [0090] In the context of the present specification, the expression "computer readable storage medium" (also referred to as "storage medium^ and ^storage^) is intended to include non-transitory media of any nature and kind whatsoever, including without limitation RAM, ROM, disks (CD-ROMs, DVDs, floppy disks, hard drivers, etc.), USB keys, solid state-drives, tape drives, etc. A plurality of components may be combined to form the computer information storage media, including two or more media components of a same type and/or two or more media components of different types. [0091] In the context of the present specification, a "database" is any structured collection of data, irrespective of its particular structure, the database management software, or the computer hardware on which the data is stored, implemented or otherwise rendered available for use. A database may reside on the same hardware as the process that stores or makes use of the information stored in the database or it may reside on separate hardware, such as a dedicated server or plurality of servers. [0092] In the context of the present specification, the expression ^information^ includes information of any nature or kind whatsoever capable of being stored in a database. Thus, information includes, but is not limited to audiovisual works (images, movies, sound records, presentations etc.), data (location data, numerical data, etc.), text (opinions, comments, questions, messages, etc.), documents, spreadsheets, lists of words, etc. [0093] In the context of the present specification, unless expressly provided otherwise, an ^indication^ of an information element may be the information element itself or a pointer, reference, link, or other indirect mechanism enabling the recipient of the indication to locate a network, memory, database, or other computer-readable medium location from which the information element may be retrieved. For example, an indication of a document could include the document itself (i.e. its contents), or it could be a unique document descriptor identifying a file with respect to a particular file system, or some other means of directing the recipient of the indication to a network location, memory address, database table, or other location where the file may be accessed. As one skilled in the art would recognize, the degree of precision required in such an indication depends on the extent of any prior understanding about the interpretation to be given to information being exchanged as between the sender and the recipient of the indication. For example, if it is understood prior to a communication between a sender and a recipient that an indication of an information element will take the form of a database key for an entry in a particular table of a predetermined database containing the information element, then the sending of the database key is all that is required to effectively convey the information element to the recipient, even though the information element itself was not transmitted as between the sender and the recipient of the indication. [0094] In the context of the present specification, the words ^first^, ^second^, ^third^, etc. have been used as adjectives only for the purpose of allowing for distinction between the nouns that they modify from one another, and not for the purpose of describing any particular relationship between those nouns. Thus, for example, it should be understood that the use of the terms ^first server^ and ^third server^ is not intended to imply any particular order, type, chronology, hierarchy or ranking (for example) of/between the server, nor is their use (by itself) intended imply that any ^second server^ must necessarily exist in any given situation. Further, as is discussed herein in other contexts, reference to a ^first^ element and a ^second^ element does not preclude the two elements from being the same actual real-world element. Thus, for example, in some instances, a ^first^ server and a ^second^ server may be the same software and/or hardware, in other cases they may be different software and/or hardware. [0095] Implementations of the present technology each have at least one of the above-mentioned objects and/or aspects, but do not necessarily have all of them. It should be understood that some aspects of the present technology that have resulted from attempting to attain the above-mentioned object may not satisfy this object and/or may satisfy other objects not specifically recited herein. [0096] Additional and/or alternative features, aspects and advantages of implementations of the present technology will become apparent from the following description, the accompanying drawings and the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS [0097] For a better understanding of the present technology, as well as other aspects and further features thereof, reference is made to the following description which is to be used in conjunction with the accompanying drawings, where: [0098] FIG. 1 illustrates a schematic diagram of a computing device in accordance with one or more non-limiting implementations of the present technology. [0099] FIG. 2 illustrates a schematic diagram of a computing system and environment including a RIS environment in accordance with one or more non-limiting implementations of the present technology. [0100] FIG. 3 illustrates a schematic diagram of a RIS pre-deployment and post-deployment optimization procedure which may be executed within the computing system and environment of FIG. 2 in accordance with one or more non-limiting implementations of the present technology. [0101] FIG. 4A illustrates a schematic diagram of a RIS pre-deployment optimization procedure in accordance with one or more non-limiting implementations of the present technology. [0102] FIG. 4B illustrates a schematic diagram of a RIS post-deployment optimization procedure in accordance with one or more non-limiting implementations of the present technology. [0103] FIG.5A illustrates a schematic diagram of an example of a generated image for a transmitter in an environment for all single and double reflected paths in the context of the ray tracing computation procedure in accordance with one or more non-limiting implementations of the present technology. [0104] FIG.5B illustrates a schematic diagram of an example of a generated image for a single reflected path between a transmitter (TX) and a receiver (RX) in the environment in the context of the ray tracing computation procedure in accordance with one or more non-limiting implementations of the present technology. [0105] FIG.5C illustrates a schematic diagram of an example of a generated image for a double reflected path between a transmitter (TX) and a receiver (RX) in the environment in the context of the ray tracing computation procedure in accordance with one or more non-limiting implementations of the present technology. [0106] FIG. 6A illustrates a schematic diagram of an example of a feasible region for a first order of reflection of rays in accordance with one or more non-limiting implementations of the present technology. [0107] FIG.6B illustrates a schematic diagram of then example of the feasible region for a second order of reflection of rays in accordance with one or more non- limiting implementations of the present technology. [0108] FIG.6C illustrates a schematic diagram of the example of the feasible region for a third order of reflection of rays in accordance with one or more non-limiting implementations of the present technology. [0109] FIG.6D illustrates a schematic diagram of an example of valid reflection points using the feasible regions of the first order, second order and third order of reflection of FIG. 6A, 6B, and 6C in accordance with one or more non-limiting implementations of the present technology. [0110] FIG. 7 illustrates a schematic diagram of a geometry of a RIS and transmitter phase center (TPC) with respective unit vectors in accordance with one or more non-limiting implementations of the present technology. [0111] FIG. 8 illustrates a schematic diagram of a linear antenna array that could be used for angle of arrival (AoA) estimation in accordance with one or more non-limiting implementations of the present technology. [0112] FIG. 9A illustrates a schematic diagram of a RIS in a separate configuration in accordance with one or more non-limiting implementations of the present technology. [0113] FIG.9B illustrate schematic diagrams of a RIS system in an integrated configuration in accordance with one or more non-limiting implementations of the present technology [0114] FIG.10 illustrates a schematic diagram of a warehouse environment comprising a plurality of objects, a transmitter access point (AP), four receivers (clients) and two RISs with their respective beam coverage in accordance with one or more non- limiting implementations of the present technology. [0115] FIG.11 illustrates a schematic diagram of an artificial intelligence (AI) model trained with the output of search techniques in the context of the post- deployment RIS optimization procedure in accordance with one or more non-limiting implementations of the present technology. [0116] FIG. 12 illustrates a flowchart of a method of determining pre- deployment optimal parameters of at least one RIS in accordance with one or more non- limiting implementations of the present technology. [0117] FIG. 13 illustrates a flowchart of a method of determining post- deployment optimal parameters of at least one RIS in accordance with one or more non- limiting implementations of the present technology. [0118] FIG.14A illustrates a schematic diagram of a plurality of RISs with respective controllers in standalone mode in accordance with one or more non-limiting implementations of the present technology. [0119] FIG. 14B illustrates a schematic diagram of a plurality of RISs with respective controllers and central controller in non-standalone mode in accordance with one or more non-limiting implementations of the present technology. [0120] FIG.15 illustrates a schematic diagram of a RIS controller executing a location detection procedure and an AI model to control a RIS in accordance with one or more non-limiting embodiments of the present technology. DETAILED DESCRIPTION [0121] The examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the present technology and not to limit its scope to such specifically recited examples and conditions. It will be appreciated that those skilled in the art may devise various arrangements which, although not explicitly described or shown herein, nonetheless embody the principles of the present technology and are included within its spirit and scope. [0122] Furthermore, as an aid to understanding, the following description may describe relatively simplified implementations of the present technology. As persons skilled in the art would understand, various implementations of the present technology may be of a greater complexity. [0123] In some cases, what are believed to be helpful examples of modifications to the present technology may also be set forth. This is done merely as an aid to understanding, and, again, not to define the scope or set forth the bounds of the present technology. These modifications are not an exhaustive list, and a person skilled in the art may make other modifications while nonetheless remaining within the scope of the present technology. Further, where no examples of modifications have been set forth, it should not be interpreted that no modifications are possible and/or that what is described is the sole manner of implementing that element of the present technology. [0124] Moreover, all statements herein reciting principles, aspects, and implementations of the present technology, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof, whether they are currently known or developed in the future. Thus, for example, it will be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the present technology. Similarly, it will be appreciated that any flowcharts, flow diagrams, state transition diagrams, pseudo-code, and the like represent various processes which may be substantially represented in computer-readable media and so executed by a computer or processor, whether or not such computer or processor is explicitly shown. [0125] The functions of the various elements shown in the figures, including any functional block labeled as a "processor" or a ^graphics processing unit^, may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. In one or more non-limiting implementations of the present technology, the processor may be a processor, such as a central processing unit (CPU) or a processor dedicated to a specific purpose, such as a graphics processing unit (GPU). Moreover, explicit use of the term "processor" or "controller" should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read-only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage. Other hardware, conventional and/or custom, may also be included. [0126] Software modules, or simply modules which are implied to be software, may be represented herein as any combination of flowchart elements or other elements indicating performance of process steps and/or textual description. Such modules may be executed by hardware that is expressly or implicitly shown. [0127] With these fundamentals in place, we will now consider some non- limiting examples to illustrate various implementations of aspects of the present technology. [0128] Computing device [0129] Referring to FIG.1, there is shown a computing device 100 suitable for use with some implementations of the present technology, the computing device 100 comprising various hardware components including one or more single or multi-core processors collectively represented by processor 110, a graphics processing unit (GPU) 111, a solid-state drive 120, a random-access memory 130, a display interface 140, and an input/output interface 150. It will be appreciated that in alternative implementations of the present technology, the GPU 111 may be optional. [0130] Communication between the various components of the computing device 100 may be enabled by one or more internal and/or external buses 160 (e.g., a PCI bus, universal serial bus, IEEE 1394 ^Firewire^ bus, SCSI bus, Serial-ATA bus, etc.), to which the various hardware components are electronically coupled. [0131] The input/output interface 150 may be coupled to a touchscreen 190 and/or to the one or more internal and/or external buses 160. The touchscreen 190 may be part of the display. In one or more implementations, the touchscreen 190 is the display. The touchscreen 190 may equally be referred to as a screen 190. In the implementations illustrated in FIG.1, the touchscreen 190 comprises touch hardware 194 (e.g., pressure-sensitive cells embedded in a layer of a display allowing detection of a physical interaction between a user and the display) and a touch input/output controller 192 allowing communication with the display interface 140 and/or the one or more internal and/or external buses 160. In one or more implementations, the input/output interface 150 may be connected to a keyboard (not shown), a mouse (not shown) or a trackpad (not shown) allowing the user to interact with the computing device 100 in addition or in replacement of the touchscreen 190. [0132] According to implementations of the present technology, the solid-state drive 120 stores program instructions suitable for being loaded into the random-access memory 130 and executed by the processor 110 and/or the GPU 111 for performing pre-deployment RIS optimization and post-deployment RIS optimization in a wireless environment. For example, the program instructions may be part of a library or an application. [0133] The computing device 100 may be implemented as a server, a desktop computer, a laptop computer, a tablet, a smartphone, a personal digital assistant or any device that may be configured to implement one or more implementations of the present technology, as it may be understood by a person skilled in the art. As a non-limiting example, the computing device 100 may be implemented as a server in a cloud environment. [0134] System [0135] Referring to FIG.2, there is shown a schematic diagram of a computing system and environment 200, the computing system and environment 200 being suitable for implementing one or more non-limiting implementations of the present technology. It is to be expressly understood that the computing system and environment 200 as shown is merely an illustrative implementation of the present technology. Thus, the description thereof that follows is intended to be only a description of illustrative examples of the present technology. This description is not intended to define the scope or set forth the bounds of the present technology. In some cases, what are believed to be helpful examples of modifications to the computing system and environment 200 may also be set forth below. This is done merely as an aid to understanding, and, again, not to define the scope or set forth the bounds of the present technology. These modifications are not an exhaustive list, and, as a person skilled in the art would understand, other modifications are likely possible. Further, where this has not been done (i.e., where no examples of modifications have been set forth), it should not be interpreted that no modifications are possible and/or that what is described is the sole manner of implementing that element of the present technology. As a person skilled in the art would understand, this is likely not the case. In addition, it is to be understood that the computing system and environment 200 may provide in certain instances simple implementations of the present technology, and that where such is the case they have been presented in this manner as an aid to understanding. As persons skilled in the art would understand, various implementations of the present technology may be of a greater complexity. [0136] The computing system and environment 200 comprises inter alia a wireless RIS environment 210, a server 230, and a database 235 communicatively coupled over a second communications network 240. [0137] Wireless RIS Environment [0138] The wireless RIS environment 210 comprises inter alia a transmitter 212, one or more receivers 218, one or more RISs 220 and a plurality of objects 214. [0139] The wireless RIS environment 210 is an external or internal environment comprising a plurality of objects 214 where EM waves are propagated from the transmitter 212 to the one or more receivers 218 via the one or more RISs 220. It will be appreciated that according to some implementations of the present technology, the number, location, and configuration of the one or more RIS 220 may be determined prior to deployment of the one or more RIS 220 during a RIS pre-deployment optimization procedure, and/or may be determined or optimized by executing a RIS post-deployment optimization procedure, as explained hereinafter. [0140] In one or more implementations, the wireless RIS environment 210 may be an interior propagation environment (e.g., office buildings, residential buildings, industrial environments (e.g., warehouses), and underground environments), an exterior propagation environment (e.g., urban, or rural area) or a combination thereof. [0141] As a non-limiting example, the wireless RIS environment 210 may be used for transmission of different types of electromagnetic waves or signals, such as those utilized in Long-Term Evolution (LTE), Wi-Fi, 5G and 6G or any other wireless communication networks. As a non-limiting example, the wireless RIS environment 210 may operate in portions of one or more of the sub-6 GHz band (410 MHz ^ 7,125 GHz), the millimeter (mmWave) band (24250 MHz - 71000 MHz), the terahertz band (THz) (0,1 THz and 10 THz), and the visible light communication (VLC) band (400^ 800 THz). It will be appreciated that other frequencies or bands may be possible. [0142] In one or more implementations, the devices and/or components of wireless RIS environment 210 may form a first communication network (not numbered). It will be appreciated that the first communication network (not numbered) may include a combination of control channels and data exchange mechanisms to enable coordination and optimization of the one or more RISs 220. At least one processing device in the form of a controller (e.g. RIS controller or central controller) may be configured to manage the RIS system(s), while the RIS elements may be configured to actively sense the environment and adjust their reflection properties to enhance wireless communication. [0143] Transmitter [0144] The one or more transmitters 212, also referred to as base stations (BSs) or access points (APs), are configured to emit or propagate one or more EM signals in the environment 210, where the EM signals may be received and propagated by the one or more RISs 220 and/or received by the one or more receivers 218. The one or more transmitters 212 each have a respective transmitter location in the environment 210. [0145] A given transmitter 212 is configured to transmit (i.e., emit or propagate) EM signals throughout the environment 210 to a potential receiver 218 and/or potential RIS 220. It will be appreciated that in the context of the pre-deployment RIS optimization procedure, parameters and position of receivers 217 and/or RIS 222 may not have been yet determined. [0146] In one or more implementations, a given transmitter 212 may be configured to emit (i.e., generate) the EM signals. In one or more other implementations, a given transmitter 212 may be configured to propagate (e.g., emit, reflect, transmit, refract, diffract, etc.) an EM signal in the environment, where the EM signal has been generated by another device (not illustrated). It will be appreciated that the given transmitter 212 may also be a transceiver. [0147] Thus, a given transmitter 212 may be implemented, as a non-limiting example, as an access point, a base station, a broadcast point, another RIS, an antenna., etc. [0148] In one or more implementations, the given transmitter 212 may include components such as oscillators, modulators, amplifiers, filters, and antenna(s) configured to emit the EM signals. [0149] A given transmitter 212 is associated with transmitter properties which influence the properties of the transmitted EM signal, also referred to as wave transmission properties, which will be explained in more detail hereinafter. The wave transmission parameters enable characterizing the EM signal propagated by the given transmitter 212, which will be used in the RIS pre-deployment and post-deployment optimization procedure 300 to determine optimal locations and parameters of RISs to optimize the EM signal received by the receivers. [0150] RIS [0151] The one or more RISs 220 may be located between the transmitter 212 and the one or more receivers 218 in the environment. [0152] The one or more RISs 220 are configured to receive the incident EM waves from the transmitter and propagate the EM waves in the environment to optimize the signal strength and quality of the signals received by the receivers 218. [0153] A given RIS 220 is a programmable structure comprising an array with a plurality of elements, also known as electromagnetic units or cells, that are configured to manipulate the propagation of electromagnetic waves in a controlled manner. A given RIS 220 may be one of an active RIS or a passive RIS. [0154] A given RIS 220 may be mounted on a structure in the environment 210. In one or more alternative implementations, a given RIS 220 could be integrated into a non-stationary or mobile structure (e.g., train or vehicle) or a mobile device in the environment 210. [0155] A given RIS 220 may be configured to perform one or more of: (i) receive control signals; (ii) transmit signals to other devices; (iii) reconfiguring coefficients of the RIS elements; (iv) tuning the coefficients and properties of RIS elements; (v) sensing (e.g. power sensing, location sensing); and (vi) calculate and determine control signals for controlling the RIS elements. It will be appreciated that depending on the implementation of the RIS 220, the RIS 220 may only be configured to perform at least some but not all of the aforementioned actions. [0156] In one or more implementations, the RIS 220 comprises inter alia a RIS panel 222 and comprises, or may be connected to, a RIS interface 226. [0157] A given RIS panel 222 may include reciprocal or non-reciprocal metasurfaces with resonant, non-resonant (sub-wavelength) and/or dielectric-based elements. In one or more implementations, each element (unit cell) phase and/or amplitude may be controlled individually by the RIS interface 226. Additionally, or alternatively, a given RIS panel 222 may include electrically tunable antenna arrays. A given RIS panel 222 generally comprises a micro-controller configured to determine the response of the RIS elements in the electromagnetic domain according to control information from the RIS interface 226. [0158] The RIS interface 226 may be implemented using one or more of: field- programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), system-on-chip (SoC), microcontrollers or embedded systems, and general-purpose processors (GPPs), such as central processing units (CPUs) or digital signal processors (DSPs). [0159] The implementation of the RIS interface 226 depends on the configuration of the RIS, e.g., standalone configuration or non-standalone configuration. As will be explained hereinafter, in non-standalone configuration, a central controller (central interface) is configured to control and coordinate one or more RIS interface 226 of each RIS 220. In standalone configuration, each RIS panel 222 may be controlled by the RIS interface 226 without a central controller. [0160] In one or more implementations, the RIS 220 comprises one or more of amplitude control unit(s) and phase shifter(s), which may be controlled by the RIS interface 226. [0161] It will be appreciated that by controlling phase and/or amplitude, the secondary radiation attributes, such as side lobe level, active/passive gain, beam direction and beamwidth can be controlled electronically. [0162] To control the phase of the elements in the RIS panel 222, analog or digital phase shifters, and/or mechanically-control phase shifting approaches may also be used (not illustrated). [0163] To control the amplitude of the elements in the RIS panel 222, gain control units such as amplifiers may be used. An amplifier may be implemented by using fixed amplifier in series with an attenuator or by using variable-gain amplifier (not illustrated). [0164] In the context of the present technology, the optimal number, locations (positions) and configuration parameters of the one or more RISs 220 may be determined within the environment 210 according to the procedures that will be described hereinafter. [0165] Receiver [0166] The one or more receivers 218, also known as detectors, receiving devices or client devices, are electronic devices configured to receive EM signals from the transmitter 212. In the context of the present technology, the one or more receivers may receive the EM signals via the one or more RISs 220, which propagate the EM signals from the transmitter 212 to the one or more receivers 218. [0167] A given receiver may be an electronic device comprising components configured to capture, demodulate, and decode the transmitted electromagnetic waves, converting them back into their original signal form. A given receiver may include components such as antennas, tuners, demodulator, filters, amplifiers and the like. [0168] Each receiver is associated with respective receiver parameters or properties, which will be described in more detail hereinbelow. [0169] As a non-limiting example, a given receiver 218 may be a cell phone, a computer, a device of a worker or robot in a factory, an antenna, etc. The given receiver 218 may be stationary (fixed position) and/or non-stationary (moving). [0170] Server [0171] The server 230 is configured to execute the RIS pre-deployment and post-deployment optimization procedure 300, as will be explained hereinafter. [0172] It will be appreciated that the server 230 can be implemented as a conventional computer server and may comprise at least some of the features of the computing device 100 shown in FIG.1. In a non-limiting example of one or more implementations of the present technology, the server 230 is implemented as a server running an operating system (OS). Needless to say that the server 230 may be implemented in any suitable hardware and/or software and/or firmware or a combination thereof. In the disclosed non-limiting implementation of present technology, the server 230 is a single server. In one or more alternative non-limiting implementations of the present technology, the functionality of the server 230 may be distributed and may be implemented via multiple servers (not shown). [0173] The implementation of the server 230 is well known to the person skilled in the art. However, the server 230 comprises a communication interface (not shown) configured to communicate with various entities (such as the database 235, for example and other devices potentially coupled to the second communication network 240) via the second communication network 240. The server 230 further comprises at least one computer processor (e.g., the processor 110 and/or GPU 111 of the computing device 100) operationally connected with the communication interface and structured and configured to execute various processes to be described herein. [0174] The server 230 has access to one or more machine learning (ML) models 250. In one or more implementations, the server 230 may execute the ML models 250. In one or more other implementations, the server 230 may provide inputs to the ML models and receive outputs from the ML models 250, which may be executed by other computing devices. [0175] In some implementations of the present technology, the server 230 may perform training of ML models 250 for the RIS pre-deployment and post-deployment optimization procedure 300. [0176] [0177] Database [0178] A database 235 is communicatively coupled to the server 230 via the second communication network 240 but, in one or more alternative implementations, the database 235 may be directly coupled to the server 230 without departing from the teachings of the present technology. Although the database 235 is illustrated schematically herein as a single entity, it will be appreciated that the database 235 may be configured in a distributed manner, for example, the database 235 may have different components, each component being configured for a particular kind of retrieval therefrom or storage therein. [0179] The database 235 may be a structured collection of data, irrespective of its particular structure or the computer hardware on which data is stored, implemented or otherwise rendered available for use. The database 235 may reside on the same hardware as a process that stores or makes use of the information stored in the database 235 or it may reside on separate hardware, such as on the server 230. The database 235 may receive data from the server 230 for storage thereof and may provide stored data to the server 230 for use thereof. [0180] In one or more implementations of the present technology, the database 235 is configured to store inter alia: (i) data relating to the wireless RIS environment 210, objects and geometrical layouts; (ii) parameters such as object properties, transmitter properties and RIS properties; and (iii) any data relating to the RIS pre- deployment and post-deployment optimization procedure 300, including inputs, outputs, intermediate results, models, training datasets, and the like. [0181] Non-limiting examples of data that may be stored in and/or retrieved from the database 235 will be described in more detail hereinafter with respect to the RIS pre-deployment and post-deployment optimization procedure 300. [0182] Second Communication Network [0183] In one or more implementations of the present technology, the second communication network 240 is the Internet. In one or more alternative non-limiting implementations, the second communication network 240 may be implemented as any suitable local area network (LAN), wide area network (WAN), a private communication network or the like. It will be appreciated that implementations for the second communication network 240 are for illustration purposes only. How a second communication link 245 (not separately numbered) between the RIS network environment 210, the server 230, the database 235, and/or another electronic device (not shown) and the second communications network 240 is implemented will depend inter alia on how each electronic device is implemented. [0184] In one or more other implementations of the present technology, the server 230 and the database 235 may be located within the RIS network environment 210. [0185] With reference to FIG.3, the RIS pre-deployment and post-deployment optimization procedure 300 will now be described. [0186] Pre-Deployment & Post-Deployment RIS Optimization Procedure [0187] The RIS pre-deployment and post-deployment optimization procedure 300 comprises inter alia a RIS pre-deployment optimization procedure 320, a RIS post- deployment optimization procedure 340, and a visualization and report generation procedure 380. [0188] The RIS pre-deployment and post-deployment optimization procedure 300 may be executed by one or more computing device. In one or more implementations, portions of the RIS pre-deployment and post-deployment optimization procedure 300 may be executed by a single device (e.g., server 230) or a plurality of devices (e.g., computing device 100, server 230, RIS interface 226). [0189] It should be noted that one or more of the RIS pre-deployment optimization procedure 320, the RIS post-deployment optimization procedure 340, and the visualization and report generation procedure 380 may be executed by the same computing device or by different computing devices in a distributed manner. [0190] The RIS pre-deployment optimization procedure 320 will now be described with reference to FIG. 4A in accordance with one or more non-limiting implementations of the present technology. [0191] Pre-Deployment Optimization Procedure [0192] The RIS pre-deployment optimization procedure 320 comprises inter alia a layout modeling procedure 410, a pre-deployment data acquisition procedure 420, a wave propagation modeling procedure 440, and a RIS placement optimization procedure 480. [0193] In one or more implementations of the present technology, the server 230 executes the RIS pre-deployment optimization procedure 320. In alternative implementations, the server 230 may execute at least a portion of the RIS pre- deployment optimization procedure 320, and one or more other servers (not shown) may execute other portions of the RIS pre-deployment optimization procedure 320. [0194] Layout Modeling Procedure [0195] The layout modeling procedure 410 is configured to import and/or generate a layout of the environment in which the electromagnetic waves will propagate (e.g., the RIS network environment 210). It will be appreciated that different types of software methods and techniques may be used to model the geometrical layout of a given environment, which may include position, shape, size and type of objects (walls, obstacles, etc.). [0196] In one or more implementations, the layout modeling procedure 410 receives the layout of the environment. [0197] In one or more other implementations, the layout modeling procedure 410 may import indoor/outdoor layout from computer-aided design (CAD) tool, PDF or any image file used to model structures (e.g., building) and optimize the features of the structures, which will affect the EM wave propagation. [0198] In one or more implementations, the layout modeling procedure 410 is used to create the layout for indoor/outdoor environments by drawing objects such as walls, floors, horizontal, circular, and inclined surfaces. [0199] In one or more implementations, the environment may be the RIS network environment 210 comprising one or more transmitters 212, one or more receivers 218 and one or more objects 214. Each of the one or more transmitters 212, one or more receivers 218 and one or more objects 214 may be associated with geometrical or location information such as position or location, shape, size and type. [0200] The layout modeling procedure 410 outputs the geometrical layout of the environment 210 comprising the plurality of objects 214. [0201] Pre-Deployment Data Acquisition Procedure [0202] The pre-deployment data acquisition procedure 420 is configured to receive properties influencing propagation of the EM waves within the environment, e.g., the RIS network environment 210 comprising the one or more transmitters 212, the one or more receivers 218 and the one or more objects 214 in the environment. [0203] In one or more implementations, the properties of the transmitter 212, the one or more receivers 218, the one or more RISs 220, and the plurality of objects 214 may be stored in one or more databases such as the database 235 and retrieved by the pre-deployment data acquisition procedure 420. [0204] Alternatively, the pre-deployment data acquisition procedure 420 may receive information from one or more sources (e.g., files, database(s)), connected devices (e.g., transmitters, receivers, computing devices, sensors, etc.) and/or may receive information input by one or more users via an input/output interface (e.g., keyboard, touchscreen, voice, etc.) (not illustrated). [0205] Object properties [0206] The object properties, which may also be referred to as object parameters or object characteristics, determine how objects interact with EM waves and influence propagation of the EM waves in the environment. The object properties may include one or more of: a respective size, a respective permeability, a respective conductivity and a respective permittivity thereof. [0207] In one or more implementations, the respective size may include a thickness of the object, e.g., a thickness of a wall. The thicker the wall, the more attenuated the signal will be as it passes through. It will be appreciated that thicker walls will also have a greater impact on lower frequency signals. [0208] In one or more implementations, the respective permeability for one or more objects is received. Permeability is a measure of the ability of a material to conduct a magnetic field. It is defined as the ratio of the magnetic flux density in the material to the magnetic flux density in a vacuum. Permeability is a physical constant that characterizes how easily magnetic lines of flux can pass through a material. [0209] In one or more implementations, the respective conductivity for one or more objects is received. Materials with high conductivity can cause signals to reflect off the surface of the wall, rather than passing through it. This reflection can result in signal loss. [0210] Transmitter properties [0211] In one or more implementations, the parameters of one or more transmitters 212 (e.g., base station/access point) that can affect signal strength of the EM signal may include one or more of: transmit power, antenna gain, antenna height, frequency band, and channel bandwidth. Parameters of the one or more transmitters 212 may also be referred to as wave transmission parameters or wave transmission properties. In one or more other implementations, the transmission parameters include the frequency and power of the transmitted signal, the type of antenna used, and the modulation scheme. [0212] The wave transmission parameters enable characterizing the origin EM signal propagated by the given transmitter 212, which will be used in the RIS pre- deployment and post-deployment optimization procedure 300 to determine optimal locations and parameters of RISs to optimize the EM signal received by the receivers. [0213] Transmit power is the amount of power that the one or more transmitters 212 (e.g., base station/access point) use to transmit signals to receivers 218 (e.g., mobile devices). It will be appreciated that a higher transmit power generally results in a stronger signal, although it can also cause interference and other issues. [0214] Antenna gain is a measure of how well the antenna can focus and direct signals. A higher antenna gain can improve signal strength but can also increase interference and other issues. [0215] Antenna height can affect signal strength and coverage. It will be appreciated that a higher antenna placement can improve coverage but may also increase interference and other issues. [0216] The frequency band that one or more of the transmitters 212 use may affect signal strength and coverage. Different frequency bands have different propagation characteristics and may be better suited for different environments and use cases. [0217] The channel bandwidth that the one or more transmitters 212 use may affects data rates. It will be appreciated that a wider channel bandwidth can support higher data rates, but it can also increase interference and other issues. [0218] RIS properties [0219] In one or more implementations, the parameters of different types RISs that can affect signal strength may include one or more of: a size of the RIS, spacing between elements of the RIS, beam directionality, an amplitude control unit type, a power consumption, a phase shifter type, and a radiator type. [0220] Panel size and beam directionality: the physical aperture (or panel) size controls the maximum achievable secondary radiation beam directivity. The larger the panel size, the higher directivity will achieve. [0221] Element spacing: the element spacing is significant for the steering range. If this value violates from the half-wavelength in square lattice, the grating lobe will appear. Thus, the larger the element spacing, the narrower scan range will be. [0222] The element spacing affects the mutual coupling between the elements. Mutual coupling introduces dissipation loss, limits the antenna scanning range and typically increases the side lobe level due to amplitude and phase error. Usually, the smaller element spacing, the more sever mutual coupling effect is. [0223] Amplitude control unit: the amplitude control unit might be active (e.g., an amplifier) or passive (e.g., an attenuator). This parameter affects the RIS performance in two ways: (i) control the effective aperture size, which can be done by using specific weighting on the aperture field to control the side lobe level and beamwidth; and (ii) controls the active gain of amplifier which controls the total aperture power. [0224] Power consumption: the panel power consumption generally depends on the number of elements and the type and the gain amount of the amplitude control units such as drive amplifier, LNA, attenuator, etc. [0225] Phase shifters: to control the complex coefficient of each element, both phase and amplitude should be controlled. The phase shifters, in general, are needed to steer the RIS beam direction. [0226] Radiators: the radiator type has an effect on bandwidth and total radiation pattern and efficiency of the RIS. [0227] In accordance with one or more implementations of the present technology, one or more of the object properties, transmitter properties, the receiver properties and RIS properties may be stored in and received from the database 235 and/or from other computing devices. [0228] The pre-deployment data acquisition procedure 420 may associate the object properties, the transmitter properties and RIS properties with the layout of the environment. [0229] The RIS pre-deployment optimization procedure 320 is configured to execute a wave propagation modeling procedure 440 based on the layout, the object properties and the transmitter. [0230] Wave Propagation Modeling Procedure [0231] The wave propagation modeling procedure 440 is configured to model behavior of EM waves as they propagate in the layout of the environment 210 comprising objects 214 between the transmitter 212, the RIS 220 and the receiver 218. [0232] The wave propagation modeling procedure 440 generally uses a mathematical formulation or algorithmic representation designed to predict the behavior and characteristics of EM wave propagation in the given environment or over a defined path. Generally, the wave propagation modeling procedure 440 calculates the loss of power density (attenuation) of an EM signal as it propagates through the medium, taking into account a variety of factors such as distance, frequency, terrain, environment (urban, suburban, rural), atmospheric conditions, and the presence of objects (e.g., walls of buildings or vegetation). [0233] In one or more implementations, the wave propagation modeling procedure 440 uses a ray tracing modeling procedure 460. [0234] In one or more other implementations, the wave propagation modeling procedure 440 uses alternative propagation modeling procedure(s) 470, such as the COST 231 Hata propagation model. [0235] Ray Tracing Computation Procedure [0236] The ray tracing modeling procedure 460 is configured to predict the propagation of electromagnetic waves in a given environment such as the wireless RIS environment 210. [0237] The ray tracing modeling procedure 460 may be executed by one or more processors, such as one or more of the processor 110 or GPU 111 of the server 230. Specifically adapted computing devices may also be used to execute portions of the ray tracing modeling procedure 460. [0238] The ray tracing modeling procedure 460 is configured to simulate the propagation of EM waves by tracing the paths (or rays) that the EM waves follow as they interact with various objects in the given environment, such as walls, buildings, and other obstacles. [0239] It will be appreciated that the layout of the environment and properties of the various objects in the environment may have been previously determined and/or received via the layout modeling procedure 410 and pre-deployment data acquisition procedure 420. [0240] The ray tracing modeling procedure 460 may use one or more ray tracing algorithms adapted for simulating propagation of EM waves. [0241] In ray tracing, as shown in FIG. 5A, rays are emanated from the transmitter 512 (e.g., BS, AP) and/or a RIS and traced through the environment, considering the material properties of objects and the laws of electromagnetic wave propagation. The ray tracing modeling procedure 460 uses ray tracing techniques to find all possible paths between the transmitter 512 and the receiver 518. [0242] Ray tracing algorithms, based on image theory, are used to design complex indoor layouts due to their high level of computational accuracy. Image theory algorithms work by replacing each transmitter with mirrored images relative to the walls of the given layout of the environment. [0243] The transmitter images are further mirrored with respect to the relevant walls based on the number of reflections required as shown in FIG.5A, where the image is marked with the wall number (sequence of walls number) enclosed between curly brackets. [0244] The final set of virtual transmitters, which represent propagation paths experiencing reflections over the corresponding sequence of mirroring walls, are used to get the paths from the transmitter 582 to receiver 588. [0245] In the non-limiting example illustrated in FIG. 5B, a mirror of the transmitter over wall 2 represents the image 590 represented by {2} where the perpendicular line to the wall that links the transmitter 582 and its reflection is at a right angle to the wall, and the distance between the wall and the reflection is the same as the distance between the wall and the transmitter 582. Within the context of image theory, higher order reflections can be achieved by reflecting image transmitters across alternate walls. Employing this technique, mirroring image transmitter 582 across wall 3 results in the image transmitter 585 represented by (4, 3) as can be seen in FIG.5C. Additionally, it can be demonstrated that the distance traveled by a reflected ray is equivalent to the distance between the transmitter's image and receiver 588. [0246] In one or more implementations, the ray tracing modeling procedure 460 uses techniques to reduce the complexity of ray tracing based on the assumption that it necessary for a given image source to be valid towards all receiving points in a given environment such as a building. For valid image source, the line connecting this image source and the receiving point must intersect with the last wall of the image source. For every image source, there is a set of valid reception points which may be referred to as feasible region or shadow region. [0247] The ray tracing modeling procedure 460 may be based on concept of a feasible region for the image. A feasible reflection region is the area for which paths reflecting over the sequence of walls corresponding to a given transmitter (TX) image are geometrically valid. As a result, for each path formed due to the multiple imaging of the TX over the walls, a feasible reflection region is obtained. The feasible region defines all the reception positions at which a certain reflection path is valid. The ray tracing modeling procedure 460 is configured to find the two points that will specify the limits of the feasible region on the last reflection wall before reaching the receiving point (i.e., receiver 218). The lines connecting each of these points to the image transmitter along with the last reflection wall represent the boundaries of the feasible region. [0248] As shown in FIGS.6A to 6D, the feasible reflection region of the image source {3, 4, 1} may be computed by the ray tracing modeling procedure 460 using the following steps. [0249] First, the feasible region 602 of the image source {3} is obtained. Since this region is due to a first order reflection, then the region is assumed to be limited by the two end points of wall 3 as shown in FIG.6A. As such, the feasible region 602 for the image source {3} is simply defined by the area that contains the real transmitter (Tx) and is bounded by wall 3 and the two lines connecting image source {3} and the points defining the beginning and end of wall 3. Similar to finding the reflection paths, the feasible reflection regions are calculated iteratively. For the double reflection path defined by image source {3, 4}, the two end points of wall 4 are checked to lie within the region of image source {3} previously calculated. Since, this is true for the two end points of wall 4, then the feasible reflection region 612 for the source {3, 4} is confined by the lines connecting the beginning and end points of wall 4 to the image source {3, 4} and wall 4 as shown in FIG.6B. For the third reflection path {3, 4, 1}, the two end points of wall 1 are also checked to lie within the feasible region of the source {3, 4} previously calculated. [0250] FIG.6C shows that the feasible region 622 of image {3, 4, 1} containing the lines drawn from the two points of intersection to the image {3, 4, 1} and wall 1. Each image has two lines that put the boundaries for the feasible region in which the valid receiving points for this image are located. To obtain the valid reception point, the ray tracing modeling procedure 460 is configured to draw parallel lines to the last reflection wall of the image across the environment layout, where for each drawn line, there are two intersection points that puts the limits in which the valid reception points are located. [0251] FIG. 6D illustrates how the ray tracing modeling procedure 460 performs the aforementioned steps, assuming an image {3,4,1} with third order of reflection, and the two feasible region lines 652, 654. The ray tracing modeling procedure 460 begins drawing parallel lines to wall 1 which is the last reflection wall for image {3,4,1}, where for each line there are two intersection points that define the boundaries for the valid receiving points on this line. [0252] Calculating reflections and refraction coefficients: [0253] It will be appreciated that rays (e.g., EM waves) may interact with objects in the environment, leading to reflections and refraction. Interactions with objects in the environment may be modeled using reflection and refraction coefficients that depend on the material properties of the objects in the environment, which may be received after the pre-deployment data acquisition procedure 420. The material properties (parameters) of the objects may be stored in the database 235. [0254] In one or more implementations, the ray tracing modeling procedure 460 is configured to determine reflection and refraction coefficients of the objects. [0255] The reflection and refraction coefficients for two polarizations (e.g., vertical and horizontal polarization) can be calculated using the same equations as for a single polarization, with some additional considerations for the polarization- dependent reflection and transmission of electromagnetic waves at the boundary. The reflection coefficient for vertical polarization ( ) and horizontal polarization ( ) can be calculated using the following equation(s): cos sin cos sin , (1) cos sin

cos sin , [0256] where and are the refractive indices of the media on

of the

angle of incidence, and is the angle of refraction. [0257] The refraction coefficient for vertical polarization ( ) and horizontal polarization ( ) can be calculated using equations (3-4). 2 cos , (3) (4) [0258] where

side of the boundary, is the angle of incidence, and is the angle of refraction. [0259] The ray tracing modeling procedure 460 is configured to compute the received signal. [0260] The ray tracing modeling procedure 460 is configured to compute the received signal based at least on the wave transmission parameters. [0261] Once the rays (EM waves) reach the receiver, the received signal power may be calculated for transmit power and antenna gain by summing the contributions of all the rays that reach the receiver using equation (5): 4 , (5) [0262] where

and d is the reference distance. E are combination electromagnetic field, respectively and is given by equations (6-7): c^{os cos} ₍₆₎ ₍₇₎ ₍₈₎ [0263]

scattering, or other factors. represents the length of the path from the transmitter to receiver while is phase shift between the path u and the reference signal. Also, and represent the angle of arrival (AOA) and path loss exponent, respectively. [0264] In the context of the present technology, the one or more RISs 220 may be viewed as a new type of transmitter, and the RIS transmit power may rely on several factors, including the transmit power of the transmitter 212 (e.g., base station or access point), the distance(s) between the transmitter 212 and the one or more RISs 220, and the gain of the RIS. [0265] The power captured by each element of a given RIS 220 can be determined based on the following equation: (₉₎ [0266] where

point (transmitter), respectively. is the gain of the element and is the distance from the transmitter antenna phase center to the i-th element of the RIS. (702) and (704) are the unit vectors shown in FIG.7, in transmitter coordinate system 700 and RIS element coordinate system 710, respectively. [0267] Equation (9) can also be written for the receiver 218, and combined with equation (9) for the RIS to determine the power received by the receiver 218 via the RIS 220. [0268] It should be noted that the power transmitted by the i-th element of the given RIS 220 can be written as follows: (10)

[0269] where is the gain of amplifier (for passive device 1) and is the efficiency due to any loss including the phase shifter insertion loss, mismatch, and radiation efficiency of antenna. Accordingly, the total power transmitted by the RIS can be written as:

[0270] Where is a coefficient imposed by the amplitude control unit. [0271] Alternative Wave Propagation Modeling Procedure(s) [0272] As stated previously, in one or more other implementations, the wave propagation modeling procedure 440 uses alternative wave propagation modeling procedure(s) 470. The alternative wave propagation modeling procedures 470 may include statistical channel models and path loss models. Statistical channel models use statistical properties to describe the wireless channel, considering factors such as multipath fading, shadowing, and interference. These models are based on measurements and statistical analysis of real-world wireless channels. Statistical models provide statistical distributions for signal parameters, such as signal strength, delay spread, and Doppler spread, which can be useful for system design, performance analysis, and simulation. Path loss models are empirical models that describe the attenuation of radio signals as they propagate through a wireless environment. These models are based on measurements and statistical analysis of real-world propagation characteristics. Path loss models consider factors such as distance, frequency, and environmental conditions to estimate signal strength at a given location. Non-limiting examples of path loss models include the Okumura-Hata model, COST 231-Hata model, and the Free Space Path Loss model. [0273] The alternative propagation modeling procedure 470 may execute the COST 231 Hata propagation model as follows: [0274] Cost 231 multiwall propagation model [0275] The Cost 231 multiwall propagation model is a radio propagation model used to estimate the path loss of wireless signals in urban environments. This model takes into account the effects of multiple walls and other obstructions that are typical in urban areas. It is commonly used for designing and planning wireless networks and predicting the coverage area of wireless transmitters. [0276] The equation for the path loss with walls penetration effect can be expressed as: , 10

⁽¹¹⁾ [0277] where: [0278] L is the path loss in decibels (dB) [0279] is the free space loss in dB [0280] n is the path loss exponent, typically between 2 and 6 [0281] d is the distance between the transmitter and receiver in meters [0282] are the antenna gains in the direction of the receiver and transmitter, respectively. [0283] PLF is the penetration loss factor, which is given by the following equation (12): 20 log 1 (12) 2

[0284] where: [0285] is the carrier frequency in megahertz (MHz) [0286] is the wall thickness in meters [0287] is the wave velocity in the wall, typically assumed to be 3 x 10^8 m/s [0288] S is the ^shadowing^ term, which represents the variation in path loss due to local obstructions, and is typically modeled as a log-normal distribution. [0289] It should be noted that the PLF term accounts for the attenuation of the signal as it passes through a single wall. In practice, multiple walls may be encountered, and the path loss due to wall penetration can be estimated by summing the PLF terms for each wall. [0290] In one or more implementations, the wave propagation modeling procedure 440 outputs parameters of the received EM signals. [0291] In one or more implementations, the wave propagation modeling procedure 440 outputs possible propagation paths of the EM signals from the transmitter in the environment, each propagation path being associated with a respective received signal power. [0292] In one or more implementations, the wave propagation modeling procedure 440 outputs a feasible region for the propagation paths and the valid reception points for the EM signals and associated signal power. [0293] RIS Placement Optimization Procedure [0294] The purpose of the RIS placement optimization procedure 480 is to determine the optimal locations for RISs in a given area based on the output of the wave propagation modeling procedure 440. [0295] It will be appreciated that RIS placement requires careful consideration of several factors especially when dealing with large-scale environments or multiple objectives. [0296] The RIS placement optimization procedure 480 is configured to determine possible locations for a RIS within an environment based on inter alia the transmitter location, the wave transmission parameters and the propagation path associated with a respective received signal power. In one or more implementations, the RIS placement optimization procedure 480 may determine the possible locations for the at least one RIS further based on the respective receiver locations (e.g., when receivers have predetermined locations). In one or more other implementations, the RIS placement optimization procedure 480 may determine the possible locations for the one or more RISs 220 and the one or more receivers 218. [0297] In one or more alternative implementations, the RIS placement optimization procedure 480 may determine the optimal location for RISs within an area based on the location of transmitters (e.g., AP/BS), and the RIS placement optimization procedure 480 may also optimize the locations and configurations of both transmitters and RISs to meet the performance requirements of an operator or of receivers. [0298] In one or more implementations, the RIS placement optimization procedure 480 uses metaheuristic algorithms to determine optimal location of the RIS 220. [0299] Metaheuristic algorithms are a class of optimization algorithms that can efficiently search for optimal solutions in complex, high-dimensional search spaces. These algorithms do not guarantee optimal solutions, but instead aim to find good solutions in a reasonable amount of time by intelligently exploring the search space. It may be possible to exhaustively search the entire search space to find the optimal solution. However, this approach can be computationally expensive and may not be practical for large-scale environments. [0300] In one or more implementations, the RIS placement optimization procedure 480 uses metaheuristic algorithms such as one of genetic algorithms, Particle Swarm optimization, Brainstorming optimization, Tabu Search, Simulated Annealing, Hill Climbing, and the like. It will be appreciated that other metaheuristic algorithms may also be used by the RIS placement optimization procedure 480. [0301] Genetic Algorithm [0302] In some implementations, the RIS placement optimization procedure 480 uses genetic algorithms. [0303] It will be appreciated that a genetic algorithm is highly customizable and can be adapted to different types of optimization problems by changing the representation of the solution, the fitness function, and the genetic operators. Also, genetic algorithms can be easily parallelized to run on multiple processors or computers, which makes them suitable for large-scale optimization problems. The genetic algorithm starts with a population of potential solutions to the problem, and then uses genetic operators such as selection, crossover, and mutation to evolve the population towards better solutions. The process of evolution continues over a number of generations until a satisfactory solution is found or a termination condition is met. [0304] In one or more implementations, one or more of the parameters provided in TABLE 1 may be used: TABLE 1 : List of parameters Parameter Descriptions Set of possible locations of RIS panels Vector of x-coordination of RIS panels Vector of y-coordination of RIS panels Number of RIS panels Number of transmitters (access points (Ps)/ base stations (BSs)) n Number of RIS element in the panel Orientation of the RIS panel Gain of RIS panel 1 if RIS is deployed in grid ( , ) and = 0; otherwise 1 if AP is deployed in grid ( , ) and = 0; otherwise B^{eamwidth o} d^f i ^t r^h e^e c_t ^m i_o ^a nⁱⁿ o_f ^lo m^b a^e x ^o i^f m ^t u^h m^{e r} r^a a^d d^ia i_a ^ti t^o i_o ⁿ n ^{pattern in the} Vector of transmitter (base station/access point) x- coordinates V^{ector of transmitte} c^r o ⁽ o^b r^a d^s i^e n_a ^s t^t e^a s^{tion/access point) y-} Vector of transmitter (base station/access point) transmit power Vector of Base station/access point transmit gain W Matrix wall attributes as starting and end points; for wall i: ( , ), ( , ) M Vector of material type of the walls number of receivers (e.g., users) Vector of x-coordinates of receiver Vector of y-coordinates of receiver [0305] In one or more implementations, the minimal input parameters for RIS placement optimization procedure 480 comprise at least one of: the gain of a RIS panel, beamwidth of the main lobe of the radiation pattern in the direction of maximum radiation, transmitter transmit power, transmitter transmit gain, wall properties, the 5 number of receivers, the vector of x coordinates of a receiver, and the vector of y coordinates of the receiver. [0306] The environment layout is divided into square grids, where all grids are considered as possible locations of the RIS. The grids form a matrix ( ), and the value of each grid is represented by . 10 [0307] It will be appreciated that while the parameters are provided in 2D in (x, y) coordinates, 3D coordinates (x, y, z) may also be considered. [0308] In one or more implementations, the optimization problem is defined to select which RIS should be installed and its parameters based on an optimization of the Signal to Interference and Noise Ratio (SINR). [0309] In one or more other implementations, the optimization aims to select which RIS should be installed and its parameters based on: an optimization of the SINR and based on at least one of the optimization of power consumption of the RIS, an optimization of the deployment cost, an optimization of the power leakage, and an optimization of the provided coverage. [0310] Signal to Interference and Noise Ratio (SINR): The first objective is introduced to maximize the average received power in all receiver clusters (i.e., maximize the provided SINR) provided by the deployed RIS. The first objective may be formulated using equations (13-15): , [0311] where

point to user and from base station/access point to RIS, respectively as calculated using ray tracing engine in equation (5). Additionally, are thermal noise power and interference at the receiver, respectively, and is the received power from RIS i to user. The above equations show that the SINR can be maximized by increasing the number of antennas. It will be appreciated that adding a new RIS antenna will increase the overall deployment cost ( ). [0312] Deployment Cost: The deployment cost objective determines the locations (positions) and number of RIS to be deployed in the layout such that the deployment financial cost is minimized. Additionally, in other scenarios, the deployment cost may minimize the cost based on the number of RISs and transmitters (APs/BSs) to be deployed in the layout. The deployment cost as a function of RIS deployment cost and the deployment cost of transmitters (APs/BPs) is given by equation (16): ,^, (16) [0313] Power

appreciated that another objective may be formulated as minimizing power consumption required for controlling the surface elements of the RIS. Since each element may need to be individually controlled to achieve a desired electromagnetic effect, the power consumption of the RIS can become an issue. Minimizing power consumption may be required for reducing the operating costs of the RIS but also for improving its sustainability and reducing its environmental impact. The power consumption objective is to minimize the power consumption of RIS panels and transmitters (e.g., APs/BSs) by optimizing the placement and configuration of the RIS elements to minimize the total number of elements required for achieving a desired performance level as well as the number of transmitters (e.g., APs/BSs), is given by equation (17): (17) [0314] Where is the direct current power required for each element, is the power consumption for each transmitter (e.g., APs/BSs), and is the coefficient depending on the design of the RIS. [0315] Power Leakage (out of the building): The fourth objective is to minimize the average power leakage outside the building which is described by decreasing the power received on the external (outer) walls, given by equation (18):

[0316] where _, is the received power at the grid. [0317] Provided coverage: The fifth objective is to maximize the received power for low coverage receivers (e.g., users), which is given by equation (19): _, (19) [0318] In one

problem with conflicting terms can be obtained by multiplying each individual objective function ( ) by a weighting factor ( ) that represents the importance of the respective individual objective relative to the other objectives. The values of weights are to be determined by operator(s) of the present technology. [0319] The weighting method converts the multi-objective function to a single objective function that is represented by the value of the weighted sum of the multi objectives given by equation (20): Max _{, , ,} (20) [0320]

, received power ( ) that must be satisfied at all the user cluster locations in order to provide the one or more receivers 218 with the minimum required bit rate. This minimum power value is calculated based on the type of EM signal propagation (e.g., LTE, Wi-Fi, and others). [0321] The constraint provides that the total power at a certain receiving location is the summation of all the powers received from all transmitter antennas, which is expressed by equation (21):

[0322] It will be appreciated that the equation (20) is a non-convex function that cannot be effectively solved through gradient-based or direct search methods to obtain optimal solutions. In such a context, meta-heuristic algorithms offer viable alternatives to acquire optimal or nearly optimal solution. [0323] In one or more implementations, the RIS placement optimization procedure 480 uses a metaheuristic genetic algorithm to perform crossovers between each two solutions with probability and mutation with the probability 1 on all solutions in the population. The new generation population consists of the best solutions with highest value of objective function (20), and these procedures are repeated times then the algorithm terminates. In one or more implementations, the RIS placement optimization procedure 480 uses the genetic algorithm as follows: [0324] Initialization: The algorithm starts by creating an initial population of candidate solutions to the problem, and each candidate solution is represented as a chromosome. The initial solution should satisfy the constraint in equation (21) with acceptable accuracy, and may be modified based on the protocol of the wireless communication system, which may be, as a non-limiting example, LTE, Wi-Fi, etc. [0325] Evaluation: The fitness of each chromosome in the population is evaluated using a fitness function of equation (20) that measures how well the chromosome solves the problem. The fitness function can be any objective function that reflects the quality of the solution. [0326] Selection: The fittest chromosomes are selected for reproduction. The selection process is based on the principle of survival of the fittest, where the most fit individuals are more likely to survive and reproduce. Based on the evaluation process, the L chromosomes with highest scores are selected to survive and reproduce. [0327] Crossover: The selected chromosomes are then recombined using a crossover operator to produce new offspring. Crossover involves exchanging genetic material between two parent chromosomes to create a new child chromosome. [0328] Mutation: To introduce diversity into the population, some offspring chromosomes are mutated by randomly changing one or more genes. [0329] Replacement: The new offspring and some of the original parent chromosomes are combined to form the next generation of the population. The process of selection, crossover, mutation, and replacement is repeated for several generations until a satisfactory solution is found or a termination condition is met. [0330] In one or more implementation, the problem may be solved by using meta-heuristic genetic algorithm. The genetic algorithm performs cross overs between each two solutions with probability and mutation with the probability 1 on all solution in the population. The new generation population consists of the best solutions with highest value of objective function (20), and these procedures are repeated times, then the algorithm terminates. [0331] The RIS placement optimization procedure 480 outputs the number of RISs, an optimal location of the one or more RIS and respective parameters for the one or more RIS 220. [0332] In one or more implementations, the RIS placement optimization procedure 480 may output one RIS with optimal location(s), and its RIS parameters including a size of the RIS and spacing between elements of the RIS 220. [0333] In one or more implementations, the RIS pre-deployment optimization procedure 320 may transmit the output information to the visualization and report generation procedure 380. [0334] The one or more RISs 220 may be deployed in the environment 210 according to the output of the RIS pre-deployment optimization procedure 320. [0335] With reference to FIG.4B, there is shown a schematic diagram of the RIS post-deployment optimization procedure 340 in accordance with one or more non- limiting implementations of the present technology. [0336] Post-deployment Optimization Procedure [0337] In one or more implementations, the RIS post-deployment optimization procedure 340 is executed after the RIS pre-deployment optimization procedure 320. [0338] In one or more other implementations, the RIS post-deployment optimization procedure 340 may be executed without the RIS pre-deployment optimization procedure 320, i.e., in an already deployed environment that has been deployed without using the RIS pre-deployment optimization procedure 320. In such implementations, the RIS post-deployment optimization procedure 340 may receive information (i.e., properties or parameters) relating to the environment 210, the transmitter 212, the one or more receivers 218, and the one or more RISs 220. [0339] While the number, positions, and optimal complex coefficients of RISs may be determined during the RIS pre-deployment optimization procedure 320, developers of the present technology have appreciated that in practice, there might be a few changes or variations in the environment layout including the location of receivers (e.g., a new line of robots in a factory), presence of new objects (e.g., a new shelf deployed in a warehouse), etc. Developers have appreciated that the phase and/or amplitude of the elements of each RIS 220 could be optimized accordingly, to maximize the RIS system performance. In one or more implementations, the complex coefficients (i.e., combination of phase and amplitude) may be determined and optimized. [0340] The purpose of the RIS post-deployment optimization procedure 340 is to establish an adaptive RIS system, using AoA and receiver position(s) to determine optimal phase and/or amplitude coefficients of each RIS in a given environment to maximize the EM signal received by the receiver(s) 218. [0341] The RISs may be deployed in one of standalone mode and non- standalone mode or configuration. [0342] FIG.14A illustrates a standalone mode or configuration of a plurality of RISs 1400. In standalone mode, each RIS panel 1404, 1408, 1410 is operatively connected to a respective controller 1402, 1406, 1412 and operates independently, utilizing local information for coefficient adjustment. Each RIS dynamically configures its surface to enhance signal strength or directivity based on real-time channel conditions as shown in FIG.14A. [0343] FIG.14B shows a non-standalone mode or configuration of a plurality of RISs 1450. Each RIS panel 1454, 1458, 1462 is operatively connected to a respective controller 1452, 1456, 1460. Each respective controller 1452, 1456, 1460 is operatively connected to a central controller 1470 which collects and processes data from multiple sources to determine the optimal coefficients for all of the plurality of RISs 1450 units in the network. This coordination enables efficient utilization of RISs across the network, improving overall performance and adaptability to changing environmental conditions. [0344] In one or more implementations, the RIS post-deployment optimization procedure 340 may determine one of optimal phase coefficients or amplitude coefficients for each RIS. [0345] In one or more alternative implementations, the RIS post-deployment optimization procedure 340 may determine the optimal complex coefficients (phase and amplitude) for each RIS. [0346] The RIS post-deployment optimization procedure 340 comprises inter alia a post-deployment data acquisition procedure 520, an AoA estimation procedure 560, and a post-deployment RIS parameter optimization procedure 580. [0347] In one or more implementations, the RIS post-deployment optimization procedure 340 is configured to inter alia: (i) receive, for each RIS, an estimation of the AOA; (ii) optimize the phases and/or amplitudes of the RISs based on the AoA of the RIS; and (iii) transmit command signals to the one or more RISs for maximizing the signals received by the receivers. [0348] In one or more implementations, the RIS post-deployment optimization procedure 340 may receive the estimated AoA. In one or more other implementations, the RIS post-deployment optimization procedure 340 may perform estimation of the AoA. [0349] In one or more implementations, the RIS post-deployment optimization procedure 340 determines a beam direction. [0350] In one or more implementations, the RIS post-deployment optimization procedure 340 determines the locations of the receivers 218 based on the estimated AoA of the RIS. [0351] In one or more implementations, the RIS post-deployment optimization procedure 340 may be performed in real-time, such that the RIS interface 226 controls the RIS 220 based on the output of the RIS post-deployment optimization procedure 340. [0352] In one or more implementations, the RIS post-deployment optimization procedure 340 may perform training of ML models such that the models learn optimization of the complex coefficients by receiving data from a search technique to determine the optimal placement for the antenna using an iterative process. [0353] System Configuration for Post-Deployment [0354] In one or more implementations, to perform the RIS post-deployment optimization procedure 340, each receiver 218 may be equipped with a radiating tag to transmit signals indicative of its respective position(s), and each RIS 220 may be equipped with components for estimating an angle of arrival (AoA) (comprising antenna array and interface) for each RIS element. [0355] Radiating Tag [0356] In one or more implementations, each receiver 218 is assigned with a radiating tag. The radiating tag may be mounted or attached to each receiver 218 using various techniques known in the art. [0357] The radiating tag is configured to emit a signal, which may be used to determine its relative position and is indicative of the location of the receiver 218. The signal indicative of the location of the receiver 218 may be received by an antenna configured to receive signals from the radiating tag. [0358] It will be appreciated that the signal received by each tag can be discriminated from others by using any available temporal or/and spatial information, such as frequency deviation, polarization, etc. [0359] As a non-limiting example, the radiating tag may be implemented using a Bluetooth® 5.1 tag. [0360] In one or more alternative implementations where the locations of the RIS and the receivers are known or may be received by the RIS post-deployment optimization procedure 340, radiating tags may be optional. [0361] RIS Antenna Array [0362] Each RIS may be associated with an antenna array operatively connected to an interface to estimate the AoA. The antenna array is configured to detect EM signals for each of its elements and the interface is configured to estimate the AoA based on the detected EM signals. In one or more implementations, the AoA may be estimated by a time difference of arrival (TDOA) between individual elements of the RIS array. It will be appreciated that in one or more alternative implementations, other techniques may be used to estimate the AoA. [0363] The antenna array may be integrated into the RIS panel (shown in FIG. 9A) or may be a physically separate device (shown in FIG.9B). [0364] In one or more implementations, the antenna array may be a linear array for azimuth or altitude angle estimation. In one or more other implementations, the antenna array may be planar array which can be used to determine the azimuth and elevation AoA. [0365] A non-limiting example of a linear antenna array 800 used for an AoA estimation procedure 850 is shown in FIG.8. The linear antenna array 800 comprises a plurality of antenna elements 802 (only one numbered) with inter-element distance 806. An incoming signal 812 arrives at angle of arrival 810 on antenna element 802. The AoA estimation procedure 850 estimates the AoA using the angle of arrival 810 and the inter-element distance 806. [0366] As shown in FIG. 9A, the RIS 900, the antenna array 904 and the interface 902 are in separate configuration, where the antenna array 904, the interface 902 and the RIS panel 900 are physically separate devices and are operatively connected to each other. [0367] As shown in FIG. 9B, the antenna array radiators 906 and the RIS elements 908 are combined in a single panel 910 for a more compact product. In FIG. 9B, the first row and column of the panel 910 is devoted to the array and the rest of the elements are for RIS. It should be noted that the RIS and antenna array may work in different frequency or polarization; thus, the constituting radiators may not be the same in terms of physical size, shape and architecture. [0368] RIS Interface [0369] The RIS interface 226 is configured to inter alia: (i) receive optimized configuration parameters for each element of the RIS; and (ii) determine and transmit command signals to control the elements of the RIS based on the optimized configurations parameters. [0370] In one or more implementations, the RIS interface 226 may be a computing device configured to estimate the AoA. [0371] Post-deployment data acquisition procedure [0372] The post-deployment data acquisition procedure 520 is configured to inter alia: (i) receive, for each receiver, one or more respective position(s) [0373] In one or more implementations, the post-deployment data acquisition procedure 520 may be executed by the RIS interface 226. Additionally, or alternatively, the post-deployment data acquisition procedure 520 may be executed by one or more other computing devices such as the server 230. [0374] The post-deployment data acquisition procedure 520 receives, for each receiver in the environment, one or more respective positions. In some implementations, a given receiver may move between respective positions, and in such implementations, its respective positions and/or velocity may also be received and/or determined. [0375] In one or more implementations, the post-deployment data acquisition procedure 520 may receive and/or determine the position of the respective receiver 218 based on signal transmitted by the radiating tag of the respective receiver 218. [0376] In one or more implementations, the radiating tags emit signals that may be received by transmitter (e.g., access points or base stations). By using temporal or spatial information such as frequency deviation or polarization, the signals received by each radiating tag may be discriminated from the signals received from other radiating tags. It will be appreciated that this enables localization of receivers (e.g., users) based on the signals received from their tags, enabling accurate tracking and monitoring within the network. [0377] In one or more other implementations, radiating tags may not be required, and the positions of receivers may be determined based on the protocol used in the communication system (e.g., WiFi, LTE, and 5G). In such implementations, the signals transmitted by receivers to the transmitters (e.g., APs/BSs) are analyzed to determine their positions. By analyzing the signal characteristics and the timing of signal arrival at different receivers, the positions of receivers may be estimated. [0378] In one or more implementations, the post-deployment data acquisition procedure 520 receives the number of RIS and the current RIS configuration parameters (e.g., position, phase, amplitude coefficients) determined during the pre-deployment optimization procedure 320. It will be appreciated that the information may be stored in the database 235. [0379] In one or more other implementations, where one or more RISs 220 have been deployed without executing the pre-deployment optimization procedure 320, the post-deployment data acquisition procedure 520 may receive information relating to the environment 210, the transmitter 212, the receiver 218, and the given RIS 220. [0380] Angle of Arrival (AoA) estimation procedure [0381] AoA estimation is the process of determining the direction of arrival of the incoming signal by processing the signal received by each constituting element of the RIS antenna array. The received signal spatial-domain attributes, including amplitude and/or phase, at different antenna array elements can be processed to extract the information about the receiver position. [0382] The AoA estimation procedure 560 is configured to inter alia estimate the AoA for each element of a given RIS 220. [0383] In one or more implementations, the AoA estimation procedure 560 may be executed by a computing device connected to a given RIS, such as the RIS interface 226. Additionally or alternatively, the AoA estimation procedure 560 may be executed by one or more computing devices such as the server 230. [0384] It will be appreciated that the AoA estimation procedure 560 may be configured to use different approaches for AoA estimation. [0385] In one implementation, the AoA estimation procedure 560 may estimate the AoA based on the beam direction providing the highest signal strength. [0386] In one or more implementations, other approaches may be performed based on noise subspace¹, such as multiple signal classification (MUSIC). It will be appreciated that such approaches may require specialized hardware and higher computational complexity. [0387] The AoA estimation procedure 560 outputs the AoA for elements of the given RIS 220. [0388] Optimal Parameter Determination Procedure [0389] The post-deployment RIS parameter optimization procedure 580 is configured to inter alia: (i) receive, for each RIS, an estimation of the AoA; (ii) determine, for each RIS, based the AoA, optimal amplitude and/or phase parameters for each RIS; and (iii) transmit the determined optimal amplitude and/or phase parameters for each RIS. [0390] The optimal parameters refer to a ^balanced state^ of signaling between all receivers, where all receivers receive acceptable level of SNR (i.e., above a threshold). In the balanced state, all receivers receive a SNR above a threshold, and the SNR received by one receiver cannot be promoted without degrading other SNR(s) of other receiver below the threshold. [0391] In one or more implementations, the post-deployment RIS parameter optimization procedure 580 uses one or more AI models 250 to determine, for each RIS, based on the receiver location(s) and the AoA, optimal amplitude and/or phase parameters. [0392] Non-limiting examples of AI models that could be used to determine optimal amplitude and/or phase parameters may include, but are not limited to: Reinforcement Learning (RL), Deep Learning (DL), Convolutional Neural Networks (CNN) and Support Vector Machine (SVM). [0393] In some implementations as shown in FIG. 11, the one or more AI models 1150 (e.g., ML models 250) may be trained during a model training procedure 1140 based on the output from a guided random search technique 1120. As a non- ¹ The eigenvectors associated to all small eigenvalues of the array covariance matrix. limiting example, if the RIS placement optimization procedure 480 has been previously executed, the output from guided random search technique 1120 (e.g., one of Genetic Algorithms, Particle Swarm optimization, Brainstorming optimization, Tabu Search, Simulated Annealing, Hill Climbing, etc.) may be stored in the database 235, and used to train ML models during the model training procedure 1140 to determine the optimal RIS parameters. [0394] During training iterations of the model training procedure 1140, a ML algorithm adjusts the ML model^s parameters to minimize the difference between the predicted output and the actual output. Once the learning stage is complete, the training procedure 1140 outputs a trained AI (ML) model 1150. The trained ML model 1150 can automatically configure the RIS panels without the need for manual adjustment or search technique, resulting in maximum performance for the network. In one or more implementations, the trained ML model 1150 may be executed by a RIS interface and/or a central controller (e.g., in non-standalone mode). [0395] In one or more other implementations, the post-deployment RIS parameter optimization procedure 580 may execute a guided random search technique to determine for each RIS, based on the receiver location(s) and the AoA, optimal amplitude and/or phase parameters. In such implementations, the output from the guided random search techniques may be stored in the database 235 until there is sufficient data to train the one or more ML models to determine the optimal amplitude and/or phase parameters. [0396] The post-deployment RIS parameter optimization procedure 580 outputs, for each given RIS 220, the optimal amplitude and/or phase parameters. [0397] In one or more implementations, the post-deployment RIS parameter optimization procedure 580 transmits the optimal amplitude and/or phase parameters to the respective RIS interface 226. The respective RIS interface 226 controls the RIS elements based on the optimal amplitude and/or phase parameters. [0398] In one or more alternative implementations, the post-deployment RIS parameter optimization procedure 580 determines command signal(s) for controlling the RIS based on the optimal amplitude and/or phase parameters, and transmits the command signals, which cause each RIS to adjust its elements accordingly. [0399] In some implementations, a trained ML model 250 may be executed by the RIS interface 226 or another computing device connected to the RIS to control the amplitude and/or phase of the RIS elements in real-time. [0400] FIG.15 illustrates a schematic diagram of a RIS controller 1500 or RIS interface 1500 in accordance with one or more non-limiting embodiments of the present technology. [0401] The RIS controller 1500 executes a location detection procedure 1510, and an AI model 1520. The location detection procedure 1510 uses AoA to determine a respective location of a user, which is received by the AI model 1520. [0402] The AI model 1520 is configured to determine phase shifts and/or amplitude shifts and to transmit signals including indications of the phase shifts/or amplitude shifts to each RIS element in a RIS panel. It will be appreciated that using the AI algorithm 1520 enables continuously learning and adapting to the changing environment. [0403] In implementations where the RIS is non-standalone mode and uses a controller (shown in FIG.14B), the AI model 1520 is configured to receive a signal from a central controller (not shown) and to transmit signal to the central controller, such that the central controller manage and coordinates the operation of all RIS panels within a system. The connection between the central controller and the RIS panels enables the central controller to adjust the phase shifts and/or amplitude of the RIS elements based on the system's requirements, such as optimizing the wireless signal's reflection and/or transmission pattern to improve signal strength or reduce interference for all users in the network. Additionally, the location detection procedure 1510 enables the system to determine the optimal configuration for the RIS elements to enhance the wireless signal's propagation towards the receiver's location, leading to improved signal quality and coverage. [0404] The AI model 1520 enables RIS to adapt to dynamic environments by predicting receiver movements and channel variations. By analyzing historical data and current environmental conditions, AI models may anticipate user behavior and adjust the RIS coefficients accordingly. This proactive adaptation can enhance the performance of RIS in dynamic environments, ensuring reliable and efficient communication for moving users. By leveraging machine learning and predictive algorithms, RISs can adapt to changing conditions and provide seamless wireless communication services. [0405] In other implementations where the RIS is standalone mode, the AI model 1520 is configured to transmit information to each respective RIS, which determines the phases and amplitude mainly based on the local information of the RIS without connection to a central controller. [0406] It will be appreciated that the one or more RIS 220 for the post- deployment RIS parameter optimization procedure 580 may be active/passive, reciprocal or non-reciprocal metasurface with resonant, non-resonant (sub-wavelength) or dielectric-based unit cells. Each unit cell phase and/or amplitude may be controlled individually; thus, the secondary radiation attributes, such as side lobe level, active/passive gain and beamwidth can be controlled electronically. [0407] In some implementations, one of phase and amplitude of each element of the RIS 220 may be controlled individually. [0408] In other implementations, both phase and amplitude of each constituting element of the RIS 220 may be controlled individually. Thus, in such implementations, in addition to the beam direction, the beamwidth and gain of the secondary radiation pattern would be controllable. [0409] To control the phase, phase control units such as analog or digital phase shifters, mechanically-control phase shifting approaches, and the like may be used with the RIS. [0410] To control the amplitude, the gain control units such as amplifier may be used by the RIS. A gain control unit may be implemented using fixed amplifier in series with an attenuator or by using variable-gain amplifier. [0411] It should be noted that to comply with the link budget for most of the practical cases, amplifiers may be used to increase output signal level. [0412] In the example shown in FIG.10, four receivers (i.e., first receiver 1002, second receiver 1004, third receiver 1006 and fourth receiver 1008) are covered by two RISs (i.e., first RIS 1010 and second RIS 1012) deployed in two positions. The number of RIS and the RIS parameters (i.e., position) may have been determined by the RIS pre-deployment optimization procedure 320. Alternatively, the post-deployment RIS parameter optimization procedure 580 may receive the RIS information configuration during the post-deployment data acquisition procedure 520 (which may be provided for example by human operators or another computing device). [0413] As can be seen in FIG. 10, the third receiver 1006 and the fourth receiver 1008 are covered by the first beam 1017 emanating from the first RIS 1010. The first receiver 1002 and the moving second receiver 1004 are covered by the beam 1019 emanating from the second RIS 1012. Note that such scenario is not easy to be addressed by the RIS pre-deployment optimization procedure 320 as the second receiver 1004 does not have a fixed position. Besides, there might be changes in the environment such as installing a new object (e.g., shelf) in the warehouse which may degrade a signal path that was detected an optimal one by the RIS pre-deployment optimization procedure 320. Thus, by executing the post-deployment optimization procedure 340, propagation of the EM waves from each RIS 220 to the receivers may be controlled in real-time to optimize the EM signal received by the receivers. [0414] Visualization and Report Generation Procedure [0415] In one or more implementations, the visualization and report generation procedure 380 is configured to provide various ways for analyzing signal strength, coverage, and energy consumption in the indoor/outdoor layout for different wireless communication systems (e.g., Wi-Fi and cellular networks) prior to, during and after the RIS pre-deployment optimization procedure 320 and/or the RIS post-deployment optimization procedure 340 [0416] In one or more implementations, the visualization and report generation procedure 380 provides graphs, charts, analysis based on stored or received information relating to the RIS pre-deployment optimization procedure 320 and/or the RIS post- deployment optimization procedure 340. [0417] In some implementations, the information may be stored in the database 235 before, during and after execution of the RIS pre-deployment optimization procedure 320 and/or the RIS post-deployment optimization procedure 340. [0418] In one or more implementations, the visualization and report generation procedure 380 is configured to output one or more of the following: [0419] Heatmaps use color-coding to show the strength and coverage of wireless signals across a map of the layout. Typically, cooler colors (such as green) indicate areas with stronger signals, while warmer colors (such as red) indicate areas with weaker signals. [0420] Contour Plots: Another way to visualize signal strength and coverage and other metrics is through contour plots. For example, contour plots use lines to represent different levels of signal strength, with thicker lines indicating stronger signals. Contour plots can provide a more detailed view of signal strength and coverage in specific areas of the building. [0421] Reports: reports in computer-readable and human-readable format (e.g., excel or PDF) may be generated. The report may for example include one or more of display equipment lists, output maps, energy consumption report, and RF survey reports. The tool enables comparison of predicted results from the tool with actual measurements in the form of a report. [0422] Certain metric vs. Time: For example, the visualization and report generation procedure 380 can enable user(s) to analyze signal strength over time. This can be useful for understanding how signal strength and coverage change throughout the day or in response to different user patterns, and how the RIS panels adapt to these changes. [0423] In one or more implementations, the visualization and report generation procedure 380 is configured to output one or more of the following parameters: [0424] Signal strength: the signal strength parameter measures the strength of the wireless signal with RIS panels at various points in the building, expressed in decibels (dB) for different technologies including Received Signal Strength Indicator (RSSI), Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and others. [0425] Signal-to-Noise Ratio (SNR): the SNR parameter is a measure of the quality of the wireless signal aided with RIS panels and is calculated by comparing the signal strength to the amount of background noise in the environment. [0426] Interference: the interference parameter measures the level of interference from other wireless signals or sources of electromagnetic interference (EMI) that could affect the performance of the RIS system. [0427] Coverage area: the coverage area parameter shows the area covered by base station/access point, which can be useful for determining the number and placement of RIS panels and configurations needed to provide sufficient coverage. [0428] Having described different implementations of the RIS pre-deployment optimization procedure 320 and/or the RIS post-deployment optimization procedure 340, reference is now made to FIGS. 12 and 13 which provide flowcharts of implementation of methods for performing the RIS pre-deployment optimization procedure 320 and/or the RIS post-deployment optimization procedure 340. [0429] Method Description [0430] FIG.12 illustrates a flowchart of a method 1200 for determining pre- deployment optimal parameters of at least one RIS in accordance with one or more non- limiting implementations of the present technology. [0431] In one or more implementations, the method 1200 is executed by a computing device such as the server 230. [0432] In one or more implementations, the server 230 comprises at least one processing device such as the processor 110 and/or the GPU 111 operatively connected to a non-transitory computer readable storage medium such as the solid-state drive 120 and/or the random-access memory 130 storing computer-readable instructions. The at least one processing device upon executing the computer-readable instructions, is configured to or operable to execute the method 1200. [0433] The method 1200 begins at processing step 1204. [0434] According to processing step 1204, the at least one processor receives a layout of the environment, the environment comprising a set of objects associated with respective object locations and a transmitter location associated with the transmitter. [0435] In one or more implementations, the set of objects comprises a plurality of objects. The set of objects affect or influence the transmission, reflection, absorption, or scattering of electromagnetic waves. The plurality of objects may include, as a non- limiting example, depending on the context, buildings (e.g., walls, floors, ceilings, windows, and doors), structures (e.g., bridges, tunnels, and fences), furniture (e.g., desks, chairs, tables, cabinets, and shelves), electronic devices, appliances, vehicles, natural features, human made structures, and the like. [0436] In one or more implementations, the at least one RIS comprises at least one of: a reflective RIS and a transmissive RIS. [0437] According to processing step 1208, the at least one processor receives, for each object of the set of objects in the layout of the environment, respective material characteristics influencing propagation of the EM signals in the environment. [0438] In one or more implementations, the respective material characteristics of each object comprise: a respective size, a respective permeability, a respective conductivity, and a respective permittivity thereof. [0439] According to processing step 1212, the at least one processor receives wave transmission parameters indicative of a signal strength of the EM signals transmitted by the transmitter. [0440] In one or more implementations, the wave transmission parameters comprise at least one of: transmission power, antenna gain, antenna height, frequency band and channel bandwidth. [0441] According to processing step 1216, the at least one processor simulates, using a wave propagation model, based on the layout of the environment, the wave transmission parameters and the respective material characteristics of the set of objects, possible propagation paths of the EM signals, each propagation path being associated with a respective received signal power. [0442] In one or more implementations, the wave propagation model comprises a ray tracing propagation model. [0443] In one or more implementations, said simulating, using the wave propagation model comprising the ray tracing propagation model, based on the geometrical layout of the wireless environment, the wave transmission parameters and the respective material characteristics of the set of objects, possible propagation paths of the EM signals in the wireless environment: calculating at least one feasible region representative of reception positions at which a given propagation path of the EM signals is valid. [0444] In one or more implementations, calculating the at least one feasible region comprises determining a plurality of order of reflections of the EM signals. [0445] In one or more other implementations, the wave propagation model comprises a COST Hata wave propagation model. [0446] According to processing step 1220, the at least one processor determines, using an optimization algorithm, a set of locations for the at least one RIS based on: the transmitter location, the wave transmission parameters and the respective received signal power. [0447] In one or more implementations, the optimization algorithm comprises a metaheuristic algorithm. [0448] In one or more implementations, the metaheuristic algorithm comprises one of: a genetic algorithm, Particle Swarm optimization, Brainstorming optimization, Tabu Search, Simulated Annealing, and Hill Climbing. [0449] In one or more implementations, said determining, using the optimization algorithm, based on: the transmitter location, the wave transmission parameters and the respective received EM signal power, the set of locations for the at least one RIS further comprises determining a new transmitter location and new transmitter parameters for the transmitter. [0450] In one or more implementations, said determining, using the optimization algorithm, the set of locations of the RIS comprises: solving an objective problem, the objective problem comprising at least one of: maximizing a signal to interference and noise ratio (SINR) of the received signal power, minimizing a power consumption of the transmitter, minimizing a deployment cost of the at least one RIS, maximizing a received power by a given receiver in the wireless environment, and minimizing an average power leakage from at least an area of the wireless environment. [0451] In one or more implementations, the wireless environment comprises at least one receiver associated with a respective receiver location, and said determining, using the optimization algorithm, based on: the transmitter location, the wave transmission parameters and the respective received EM signal power, the set of locations of the at least one RIS is further based on the respective receiver location. [0452] According to processing step 1224, the at least one processor determines parameters of the at least one RIS, the at least one RIS comprising a plurality of elements, the parameters comprising: a size of the RIS and spacing between the plurality of elements of the at least one RIS. [0453] In one or more implementations, the parameters of the at least one RIS comprise: a beam directionality, an amplitude control unit type, a power consumption, a phase shifter type, and a radiator type. [0454] In one or more implementations, the at least one RIS comprises a plurality of RISs, and wherein said determining parameters of the at least one RIS comprises determining a number of the plurality of RISs. [0455] In one or more implementations, the method 1200 may further comprise training a machine learning model to determine the set of locations of the at least one RIS based on: the transmitter location, the wave transmission parameters and the possible propagation paths of the EM signals, each propagation path being associated with the respective received EM signal power. [0456] The method 1200 then ends. [0457] FIG.13 illustrates a flowchart of a method 1300 of determining RIS post-deployment optimal parameters of at least one RIS in accordance with one or more non-limiting implementations of the present technology. [0458] In one or more implementations, the method 1300 may be executed in real-time. [0459] In one or more implementations, the server 230 comprises a processing device such as the processor 110 and/or the GPU 111 operatively connected to a non- transitory computer readable storage medium such as the solid-state drive 120 and/or the random-access memory 130 storing computer-readable instructions. The processing device, upon executing the computer-readable instructions, is configured to or operable to execute the method 1300. In one or more other implementations, the method 1300 may be executed by the RIS interface 226. [0460] In one or more implementations, the method 1300 may be executed by a RIS interface (i.e., RIS controller). [0461] The method 1300 begins at processing step 1304. [0462] According to processing step 1304, the at least one processor receives respective RIS parameters of the at least one RIS, the respective RIS parameters comprising a phase of each of the plurality of elements. [0463] In one or more implementations, the RIS parameters include the amplitude of each of the plurality of RIS elements. [0464] According to processing step 1308, the at least one processor receives an estimated AoA of the incident EM signals received by each of the plurality of elements of the RIS. [0465] In one or more implementations, the at least one processor estimates, based on the incident EM signal parameters, at least one respective position of the RIS, and the RIS parameters of the at least one RIS, an angle of arrival (AoA) received by each of the plurality of elements. [0466] In one or more implementations, the at least one processor determines the respective locations of each receiver based on the AoA received by each of the plurality of elements. [0467] According to processing step 1312, the at least one processor determines, based on the AoA, updated RIS parameters to maximize EM signals received by the at least one receiver, the updated RIS parameters comprising an updated phase of each of the plurality of elements. [0468] In one or more implementations, the updated RIS parameters include the updated amplitude of each of the plurality of RIS elements. [0469] In one or more implementations, the at least one processor uses search techniques for searching the subspace of solutions to updated RIS parameters. In one or more other implementations, the at least one processor uses one or more ML models to obtain the updated RIS parameters. [0470] In one or more implementations, the one or more ML models may include one or more of: reinforcement learning (RL), deep learning, convolutional neural network (CNN), and support vector machines (SVMs). [0471] In one or more implementations, the method 1300 may be executed after training one or more ML models using the inputs and outputs of the method 1200. In one or more other implementations, the method 1300 may be executed after training one or more ML models based on historical data including one or more of AoAs, transmitter location(s) and transmitter parameters, receiver location(s) and receiver parameters, EM signal parameters, RIS location(s) and parameters (including phase and/or amplitude), without executing the method 1200 beforehand. [0472] The method 1300 then ends. [0473] It should be expressly understood that not all technical effects mentioned herein need to be enjoyed in each and every implementation of the present technology. For example, implementations of the present technology may be implemented without the user enjoying some of these technical effects, while other non-limiting implementations may be implemented with the user enjoying other technical effects or none at all. [0474] Some of these steps and signal sending-receiving are well known in the art and, as such, have been omitted in certain portions of this description for the sake of 5 simplicity. The signals can be sent-received using optical means (such as a fiber-optic connection), electronic means (such as using wired or wireless connection), and mechanical means (such as pressure-based, temperature based or any other suitable physical parameter based). [0475] Modifications and improvements to the above-described 10 implementations of the present technology may become apparent to those skilled in the art. The foregoing description is intended to be exemplary rather than limiting.

Claims

CLAIMS What is claimed is: 1. A method for determining parameters of at least one reconfigurable intelligent surface (RIS) for propagating electromagnetic (EM) signals received from a transmitter in a wireless environment, the method being executed by at least one processor, the method comprising: receiving a geometrical layout of the wireless environment, the geometrical layout of the wireless environment comprising: a set of objects associated with respective object locations, and the transmitter associated with a transmitter location; receiving, for each object of the set of objects in the geometrical layout of the wireless environment, respective material characteristics influencing propagation of the EM signals in the wireless environment; receiving wave transmission parameters indicative of a signal strength of the EM signals transmitted by the transmitter; simulating, using a wave propagation model, based on the geometrical layout of the wireless environment, the wave transmission parameters, and the respective material characteristics of the set of objects, possible propagation paths of the EM signals, each propagation path being associated with a respective received EM signal power; determining, using an optimization algorithm, based on: the transmitter location, the wave transmission parameters and the respective received EM signal power of each propagation path, a set of locations for the at least one RIS; and determining parameters of the at least one RIS, the at least one RIS comprising a plurality of elements, the parameters comprising: a size of the at least one RIS and spacing between the plurality of elements of the at least one RIS.

2. The method of claim 1, wherein the respective material characteristics of each object comprise: a respective size, a respective permeability, a respective conductivity, and a respective permittivity thereof.

3. The method of claim 1 or 2, wherein the wave transmission parameters comprise at least one of: transmission power, antenna gain, antenna height, frequency band and channel bandwidth.

4. The method of any one of claims 1 to 3, wherein the at least one RIS comprises a plurality of RISs, and wherein said determining parameters of the at least one RIS comprises determining a number of the plurality of RISs.

5. The method of any one of claims 1 to 4, wherein the parameters of the at least one RIS comprise: a beam directionality, an amplitude control unit type, a power consumption, a phase shifter type, and a radiator type.

6. The method of any one of claims 1 to 5, wherein said determining, using the optimization algorithm, the set of locations of the RIS comprises: solving an objective problem, the objective problem comprising maximizing a signal to interference and noise ratio (SINR) of the received signal power.

7. The method of any one of claims 1 to 6, wherein said determining, using the optimization algorithm, based on: the transmitter location, the wave transmission parameters and the respective received EM signal power, the set of locations for the at least one RIS further comprises determining a new transmitter location and new transmitter parameters for the transmitter.

8. The method of claim 6 or 7, wherein the objective problem further comprises: minimizing a power consumption of the at least one RIS.

9. The method of claim 8, wherein the objective problem further comprises at least one of: minimizing a power consumption of the transmitter, minimizing a deployment cost of the at least one RIS, maximizing a received power by a given receiver in the wireless environment, and minimizing an average power leakage from at least an area of the wireless environment.

10. The method of any one of claims 1 to 9, wherein the at least one RIS comprises at least one of: a reflective RIS and a transmissive RIS.

11. The method of any one of claims 1 to 10, wherein the wireless environment comprises at least one receiver associated with a respective receiver location; and wherein said determining, using the optimization algorithm, based on: the transmitter location, the wave transmission parameters and the respective received EM signal power, the set of locations of the at least one RIS is further based on the respective receiver location.

12. The method of any one of claims 1 to 11, wherein the wave propagation model comprises a ray tracing propagation model.

13. The method of claim 12, wherein said simulating, using the wave propagation model comprising the ray tracing propagation model, based on the geometrical layout of the wireless environment, the wave transmission parameters and the respective material characteristics of the set of objects, possible propagation paths of the EM signals in the wireless environment: calculating at least one feasible region representative of reception positions at which a given propagation path of the EM signals is valid.

14. The method of claim 13, wherein said calculating the at least one feasible region comprises determining a plurality of order of reflections of the EM signals.

15. The method of any one of claims 1 to 11, wherein the wave propagation model comprises a COST Hata wave propagation model.

16. The method of any one of claims 1 to 15, wherein the optimization algorithm comprises a metaheuristic algorithm.

17. The method of claim 16, wherein the metaheuristic algorithm comprises one of: a genetic algorithm, Particle Swarm optimization, Brainstorming optimization, Tabu Search, Simulated Annealing, and Hill Climbing.

18. The method of any one of claims 1 to 17, further comprising: training a machine learning model to determine the set of locations of the at least one RIS based on: the transmitter location, the wave transmission parameters and the possible propagation paths of the EM signals, each propagation path being associated with the respective received EM signal power.

19. The method of any one of claims 1 to 18, wherein the set of objects comprises a plurality of objects.

20. A method for determining optimal parameters of at least one reconfigurable intelligence surface (RIS) propagating electromagnetic (EM) signals between a transmitter and at least one receiver in a wireless environment, the at least one RIS comprising an array of a plurality of elements, the method being executed by at least one processor connected to the at least one RIS, the method comprising: receiving respective RIS parameters of the at least one RIS, the respective RIS parameters comprising a phase of each of the plurality of elements; receiving an angle of arrival (AoA) of incident EM signals received by each of the plurality of elements of the RIS; and determining, based on the AoA, updated RIS parameters to maximize EM signals received by the at least one receiver, the updated RIS parameters comprising at least an updated phase of each of the plurality of elements.

21. The method of claim 20, further comprising, prior to receiving the AOA received by each of the plurality of elements of the RIS: receiving incident EM signal parameters; and estimating, based on the incident EM signal parameters, the at least one respective position and the RIS parameters of the at least one RIS, an angle of arrival (AoA) received by each of the plurality of elements.

22. The method of claim 20 or 21, further comprising, prior to said determining the updated RIS parameters to maximize EM signals received by the at least one receiver: determining the respective positions of the at least one receiver.

23. The method of any one of claims 20 to 22, further comprising: transmitting the updated RIS parameters to cause the at least one RIS to adjust the phase of the plurality of elements.

24. The method of any one of claims 20 to 23, wherein the at least one processor executes at least one trained machine learning (ML) model, the at least one trained ML model being configured for determining, based on the AoA, the updated RIS parameters to maximize the EM signals received by the at least one receiver, the updated RIS parameters comprising the updated phase of each of the plurality of elements.

25. The method of claim 24, wherein the at least one trained ML model comprises at least one of: reinforcement learning (RL), deep learning, convolutional neural network (CNN), and support vector machines (SVMs).

26. A system for determining parameters of at least one reconfigurable intelligent surface (RIS) for propagating electromagnetic (EM) signals received from a transmitter in a wireless environment, the system comprising: a non-transitory storage medium storing computer-readable instructions thereon; and at least one processor operatively connected to the non-transitory storage medium, the at least one processor, upon executing the computer-readable instructions, being configured for: receiving a geometrical layout of the wireless environment, the geometrical layout of the wireless environment comprising: a set of objects associated with respective object locations, and the transmitter associated with a transmitter location; receiving, for each object of the set of objects in the geometrical layout of the wireless environment, respective material characteristics influencing propagation of the EM signals in the wireless environment; receiving wave transmission parameters indicative of a signal strength of the EM signals transmitted by the transmitter; simulating, using a wave propagation model, based on the geometrical layout of the wireless environment, the wave transmission parameters, and the respective material characteristics of the set of objects, possible propagation paths of the EM signals, each propagation path being associated with a respective received EM signal power; determining, using an optimization algorithm, based on: the transmitter location, the wave transmission parameters and the respective received EM signal power of each propagation path, a set of locations for the at least one RIS; and determining parameters of the at least one RIS, the at least one RIS comprising a plurality of elements, the parameters comprising: a size of the at least one RIS and spacing between the plurality of elements of the at least one RIS.

27. The system of claim 26, wherein the respective material characteristics of each object comprise: a respective size, a respective permeability, a respective conductivity, and a respective permittivity thereof.

28. The system of claim 26 or 27, wherein the wave transmission parameters comprise at least one of: transmission power, antenna gain, antenna height, frequency band and channel bandwidth.

29. The system of any one of claims 26 to 28, wherein the at least one RIS comprises a plurality of RISs, and wherein said determining parameters of the at least one RIS comprises determining a number of the plurality of RISs.

30. The system of any one of claims 26 to 29, wherein the parameters of the at least one RIS comprise: a beam directionality, an amplitude control unit type, a power consumption, a phase shifter type, and a radiator type.

31. The system of any one of claims 26 to 30, wherein said determining, using the optimization algorithm, the set of locations of the RIS comprises: solving an objective problem, the objective problem comprising maximizing a signal to interference and noise ratio (SINR) of the received signal power.

32. The system of any one of claims 26 to 31, wherein said determining, using the optimization algorithm, based on: the transmitter location, the wave transmission parameters and the respective received EM signal power, the set of locations for the at least one RIS further comprises determining a new transmitter location and new transmitter parameters for the transmitter.

33. The system of claim 31 or 32, wherein the objective problem further comprises: minimizing a power consumption of the at least one RIS.

34. The system of claim 33, wherein the objective problem further comprises at least one of: minimizing a power consumption of the transmitter, minimizing a deployment cost of the at least one RIS, maximizing a received power by a given receiver in the wireless environment, and minimizing an average power leakage from at least an area of the wireless environment.

35. The system of any one of claims 26 to 34, wherein the at least one RIS comprises at least one of: a reflective RIS and a transmissive RIS.

36. The system of any one of claims 26 to 35, wherein the wireless environment comprises at least one receiver associated with a respective receiver location; and wherein said determining, using the optimization algorithm, based on: the transmitter location, the wave transmission parameters and the respective received EM signal power, the set of locations of the at least one RIS is further based on the respective receiver location.

37. The system of any one of claims 26 to 36, wherein the wave propagation model comprises a ray tracing propagation model.

38. The system of claim 37, wherein said simulating, using the wave propagation model comprising the ray tracing propagation model, based on the geometrical layout of the wireless environment, the wave transmission parameters and the respective material characteristics of the set of objects, possible propagation paths of the EM signals in the wireless environment: calculating at least one feasible region representative of reception positions at which a given propagation path of the EM signals is valid.

39. The system of claim 38, wherein said calculating the at least one feasible region comprises determining a plurality of order of reflections of the EM signals.

40. The system of any one of claims 26 to 36, wherein the wave propagation model comprises a COST Hata wave propagation model.

41. The system of any one of claims 26 to 40, wherein the optimization algorithm comprises a metaheuristic algorithm.

42. The system of claim 41, wherein the metaheuristic algorithm comprises one of: a genetic algorithm, Particle Swarm optimization, Brainstorming optimization, Tabu Search, Simulated Annealing, and Hill Climbing.

43. The system of any one of claims 26 to 42, wherein the at least one processor is further configured for: training a machine learning model to determine the set of locations of the at least one RIS based on: the transmitter location, the wave transmission parameters and the possible propagation paths of the EM signals, each propagation path being associated with the respective received EM signal power.

44. The system of any one of claims 26 to 43, wherein the set of objects comprises a plurality of objects.

45. A system for determining optimal parameters of at least one reconfigurable intelligence surface (RIS) propagating electromagnetic (EM) signals between a transmitter and at least one receiver in a wireless environment, the at least one RIS comprising an array of a plurality of elements, the system comprising: a non-transitory storage medium storing computer-readable instructions thereon; and at least one processor operatively connected to the non-transitory storage medium, the at least one processor, upon executing the computer-readable instructions, being configured for: receiving respective RIS parameters of the at least one RIS, the respective RIS parameters comprising a phase of each of the plurality of elements; receiving an angle of arrival (AoA) of incident EM signals received by each of the plurality of elements of the RIS; and determining, based on the AoA, updated RIS parameters to maximize EM signals received by the at least one receiver, the updated RIS parameters comprising at least an updated phase of each of the plurality of elements.

46. The system of claim 45, wherein the at least one processor is further configured for, prior to receiving the AOA received by each of the plurality of elements of the RIS: receiving incident EM signal parameters; and estimating, based on the incident EM signal parameters, the at least one respective position and the RIS parameters of the at least one RIS, an angle of arrival (AoA) received by each of the plurality of elements.

47. The system of claim 45 or 46, wherein the at least one processor is further configured for, prior to said determining the updated RIS parameters to maximize EM signals received by the at least one receiver: determining the respective positions of the at least one receiver.

48. The system of any one of claims 45 to 47, wherein the at least one processor is further configured for: transmitting the updated RIS parameters to cause the at least one RIS to adjust the phase of the plurality of elements. 5 49. The system of any one of claims 45 to 48, wherein the at least one processor executes at least one trained machine learning (ML) model, the at least one trained ML model being configured for determining, based on the AoA, the updated RIS parameters to maximize the EM signals received by the at least one receiver, the updated RIS parameters comprising the updated phase of each of the plurality of elements. 10 50. The system of claim 49, wherein the at least one trained ML model comprises at least one of: reinforcement learning (RL), deep learning, convolutional neural network (CNN), and support vector machines (SVMs).