WO2025109252A1

WO2025109252A1 - Determining route for unmanned vehicle based on historical quality of service measurements

Info

Publication number: WO2025109252A1
Application number: PCT/FI2024/050625
Authority: WO
Inventors: Andrea Gentili; Tiia Ojanperä
Original assignee: VTT Technical Research Centre of Finland Ltd
Current assignee: VTT Technical Research Centre of Finland Ltd
Priority date: 2023-11-20
Filing date: 2024-11-19
Publication date: 2025-05-30
Anticipated expiration: 2026-05-20
Also published as: FI20236285A1

Abstract

Disclosed is a method comprising determining a route from a departure point to a destination point for an unmanned vehicle, wherein the determination is based at least on a set of historical quality of service measurements and one or more quality of service requirements related to network performance; and transmitting, to the unmanned vehicle or to a user device controlling the unmanned vehicle, information indicating the route.

Description

DETERMINING ROUTE FOR UNMANNED VEHICLE BASED ON HISTORICAL QUALITY

OF SERVICE MEASUREMENTS

FIELD

Various example embodiments relate to unmanned vehicles.

BACKGROUND

Unmanned vehicles, such as drones, are becoming more and more popular for a wide range of applications, from package delivery to agriculture and construction. Their ability to access difficult-to-reach areas may bring enhanced efficiency and safety to various industries. However, the current radio solutions only allow maneuvering unmanned vehicles over a limited range. Thus, it is desirable to provide solutions to extend the operating range of unmanned vehicles.

SUMMARY

The scope of protection sought for various example embodiments is set out by the independent claims. The example embodiments and features, if any, described in this specification that do not fall under the scope of the independent claims are to be interpreted as examples useful for understanding various embodiments.

According to an aspect, there is provided an apparatus comprising at least one processor, and at least one memory storing instructions which, when executed by the at least one processor, cause the apparatus at least to: select, based on a dataset size of a set of historical quality of service measurements, an algorithm to be used for determining a route from a departure point to a destination point for an unmanned vehicle; determine, by using the algorithm selected, the route from the departure point to the destination point for the unmanned vehicle, wherein the determination is based at least on the set of historical quality of service measurements and one or more quality of service requirements related to network performance; and transmit, to the unmanned vehicle or to a user device controlling the unmanned vehicle, information indicating the route. According to another aspect, there is provided an apparatus comprising: means for selecting, based on a dataset size of a set of historical quality of service measurements, an algorithm to be used for determining a route from a departure point to a destination point for an unmanned vehicle; means for determining, by using the algorithm selected, the route from the departure point to the destination point for the unmanned vehicle, wherein the means for determining the route are configured to determine the route based at least on the set of historical quality of service measurements and one or more quality of service requirements related to network performance; and transmitting, to the unmanned vehicle or to a user device controlling the unmanned vehicle, information indicating the route.

According to another aspect, there is provided the apparatus of the previous aspect, further comprising: means for receiving, from the unmanned vehicle, a set of quality of service measurements measured by the unmanned vehicle while traveling on the route; and means for storing the set of quality of service measurements in a database comprising the set of historical quality of service measurements.

According to another aspect, there is provided the apparatus of any of the two previous aspects, wherein the set of historical quality of service measurements are associated with a three-dimensional space, and wherein the information indicating the route comprises a set of three-dimensional geographical coordinates from the departure point to the destination point.

According to another aspect, there is provided the apparatus of the previous aspect, further comprising: means for receiving a user input indicating a desired granularity of the route, wherein the desired granularity of the route indicates a desired distance between the set of three-dimensional geographical coordinates, wherein the means for determining the route are configured to determine the route by determining the set of three-dimensional geographical coordinates according to the desired granularity of the route.

According to another aspect, there is provided the apparatus of any of the four previous aspects, further comprising: means for receiving a user input indicating a time window of the set of historical quality of service measurements to be used for determining the route, wherein the means for determining the route are configured to determine the route based on the set of historical quality of service measurements from the time window indicated by the user input.

According to another aspect, there is provided the apparatus of any of the five previous aspects, wherein the means for determining the route are configured to determine the route based further on at least one of: regulatory requirements indicating one or more restricted areas to be avoided, a load weight of the unmanned vehicle, a battery level of the unmanned vehicle, an urgency level of getting the unmanned vehicle to the destination point, or weather data.

According to another aspect, there is provided a method comprising: selecting, based on a dataset size of a set of historical quality of service measurements, an algorithm to be used for determining a route from a departure point to a destination point for an unmanned vehicle; determining, by using the algorithm selected, the route from the departure point to the destination point for the unmanned vehicle, wherein the determination is based at least on the set of historical quality of service measurements and one or more quality of service requirements related to network performance; and transmitting, to the unmanned vehicle or to a user device controlling the unmanned vehicle, information indicating the route.

According to another aspect, there is provided a computer program comprising instructions which, when executed by an apparatus, cause the apparatus to perform at least the following: selecting, based on a dataset size of a set of historical quality of service measurements, an algorithm to be used for determining a route from a departure point to a destination point for an unmanned vehicle; determining, by using the algorithm selected, the route from the departure point to the destination point for the unmanned vehicle, wherein the determination is based at least on the set of historical quality of service measurements and one or more quality of service requirements related to network performance; and transmitting, to the unmanned vehicle or to a user device controlling the unmanned vehicle, information indicating the route.

According to another aspect, there is provided a computer readable medium comprising program instructions which, when executed by an apparatus, cause the apparatus to perform at least the following: selecting, based on a dataset size of a set of historical quality of service measurements, an algorithm to be used for determining a route from a departure point to a destination point for an unmanned vehicle; determining, by using the algorithm selected, the route from the departure point to the destination point for the unmanned vehicle, wherein the determination is based at least on the set of historical quality of service measurements and one or more quality of service requirements related to network performance; and transmitting, to the unmanned vehicle or to a user device controlling the unmanned vehicle, information indicating the route.

According to another aspect, there is provided a non-transitory computer readable medium comprising program instructions which, when executed by an apparatus, cause the apparatus to perform at least the following: selecting, based on a dataset size of a set of historical quality of service measurements, an algorithm to be used for determining a route from a departure point to a destination point for an unmanned vehicle; determining, by using the algorithm selected, the route from the departure point to the destination point for the unmanned vehicle, wherein the determination is based at least on the set of historical quality of service measurements and one or more quality of service requirements related to network performance; and transmitting, to the unmanned vehicle or to a user device controlling the unmanned vehicle, information indicating the route.

According to another aspect, there is provided an apparatus comprising at least one processor, and at least one memory storing instructions which, when executed by the at least one processor, cause the apparatus at least to: receive information indicating a route from a departure point to a destination point for an unmanned vehicle, wherein the route is based at least on a set of historical quality of service measurements and one or more quality of service requirements related to network performance; and navigate the unmanned vehicle on the route.

According to another aspect, there is provided an apparatus comprising: means for receiving information indicating a route from a departure point to a destination point for an unmanned vehicle, wherein the route is based at least on a set of historical quality of service measurements and one or more quality of service requirements related to network performance; and means for navigating the unmanned vehicle on the route.

According to another aspect, there is provided the apparatus of the previous aspect, further comprising: means for obtaining a set of quality of service measurements measured by the unmanned vehicle while traveling on the route; and means for transmitting the set of quality of service measurements to a database comprising the set of historical quality of service measurements, wherein the set of quality of service measurements comprises at least one of: a set of network delay values measured by the unmanned vehicle while traveling on the route, a set of throughput values measured by the unmanned vehicle while traveling on the route, a set of packet loss ratios measured by the unmanned vehicle while traveling on the route, or a set of jitter values measured by the unmanned vehicle while traveling on the route.

According to another aspect, there is provided a method comprising: receiving information indicating a route from a departure point to a destination point for an unmanned vehicle, wherein the route is based at least on a set of historical quality of service measurements and one or more quality of service requirements related to network performance; and navigating the unmanned vehicle on the route.

According to another aspect, there is provided a computer program comprising instructions which, when executed by an apparatus, cause the apparatus to perform at least the following: receiving information indicating a route from a departure point to a destination point for an unmanned vehicle, wherein the route is based at least on a set of historical quality of service measurements and one or more quality of service requirements related to network performance; and navigating the unmanned vehicle on the route.

According to another aspect, there is provided a computer readable medium comprising program instructions which, when executed by an apparatus, cause the apparatus to perform at least the following: receiving information indicating a route from a departure point to a destination point for an unmanned vehicle, wherein the route is based at least on a set of historical quality of service measurements and one or more quality of service requirements related to network performance; and navigating the unmanned vehicle on the route.

According to another aspect, there is provided a non-transitory computer readable medium comprising program instructions which, when executed by an apparatus, cause the apparatus to perform at least the following: receiving information indicating a route from a departure point to a destination point for an unmanned vehicle, wherein the route is based at least on a set of historical quality of service measurements and one or more quality of service requirements related to network performance; and navigating the unmanned vehicle on the route.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments will now be described with reference to the accompanying drawings, in which

FIG. 1 illustrates an example of a system;

FIG. 2 illustrates an example of a route;

FIG. 3 illustrates a signal flow diagram;

FIG. 4 illustrates a signal flow diagram;

FIG. 5 illustrates a flow chart;

FIG. 6 illustrates a flow chart;

FIG. 7 illustrates an example of an apparatus; and

FIG. 8 illustrates an example of an apparatus.

DETAILED DESCRIPTION

The following embodiments are exemplifying. Although the specification may refer to "an", "one", or "some" embodiment(s) in several locations of the text, this does not necessarily mean that each reference is made to the same embodiment^), or that a particular feature only applies to a single embodiment. Single features of different embodiments may also be combined to provide other embodiments. Furthermore, the words "comprising" and "including" should be understood as not limiting the described embodiments to consist of only those features that have been mentioned, and such embodiments may also contain features that have not been specifically mentioned. Reference numbers, in the description and/or in the claims, serve to illustrate the embodiments with reference to the drawings, without limiting the embodiments to these examples only.

The example embodiments described herein may be implemented in a wireless communication network comprising a radio access network (RAN) based on one or more of the following radio access technologies (RATs): global system for mobile communications (GSM) or any other second generation (2G) radio access technology, universal mobile telecommunication system (UMTS, 3G) based on basic wideband-code division multiple access (W-CDMA), high-speed packet access (HSPA), long term evolution (LTE), LTE-Advanced, fourth generation (4G), fifth generation (5G), 5G new radio (NR), 5G-Advanced (i.e., 3GPP NR Rel-18 and beyond), or sixth generation (6G). Some examples of radio access networks include the universal mobile telecommunications system (UMTS) radio access network (UTRAN), the evolved universal terrestrial radio access network (E-UTRA), or the next generation radio access network (NG-RAN).

However, a person skilled in the art may also apply the solution to other wireless communication networks or systems provided with necessary properties. For example, some example embodiments may also be applied to a communication system based on IEEE 802.11 specifications (e.g., Wi-Fi), or a communication system based on IEEE 802.15 specifications (e.g., Bluetooth).

Unmanned vehicles, such as drones, are becoming more and more popular for a wide range of applications, from package delivery to agriculture and construction. Their ability to access difficult-to-reach areas may bring enhanced efficiency and safety to various industries. However, the current radio solutions only allow maneuvering unmanned vehicles over a limited range. The operating range of unmanned vehicles can be extended (e.g., beyond the visual line of sight of the human operator) by using a public radio access network.

For example, some use cases may require unmanned vehicles to have uninterrupted communication to the remote-control center or to a remote operator (user), since continuous and low-latency connectivity may be required for maneuvering or video-monitoring unmanned vehicles in real-time. However, in current wireless networks, the unmanned vehicles may often lose network connectivity when travelling to a specific destination, thus disrupting the mission.

For example, 5G and 6G networks are highly affected by obstacles and non-line-of-sight conditions due to their use of higher frequency bands, such as millimeter waves (mmWave), which have shorter wavelengths (and thus lower coverage). Higher frequencies have poorer diffraction characteristics and are more likely to be absorbed or scattered by obstacles like buildings, foliage, and even rain. Furthermore, communication at these higher frequencies typically requires a clear line-of-sight path between the transmitter and receiver for optimal performance. However, the example embodiments described herein may help to address these challenges by planning the route for the unmanned vehicle such that optimal network connectivity is maintained along the whole route.

To maintain optimal network connectivity, the unmanned vehicles should follow a route that can guarantee the required network performance (e.g., low latency) for the communication with the remote operator. For the route planning, some example embodiments may utilize an intelligent algorithm that takes historical quality of service (QoS) information into account. To pre-evaluate the network performance, and to collect the QoS information, dedicated measurement campaigns in a certain geographical area may be performed by collecting measurements for QoS parameters such as: radio signal strength, network delay, packet loss, jitter, radio interference, and/or connection break duration.

In addition, continuous or short (fixed) throughput measurements (i.e., the maximum amount of data traffic that can be sent over a network link) can be integrated during a measurement campaign, to estimate what is the guaranteed bandwidth available when the unmanned vehicle is moving.

Two-dimensional (2D) or three-dimensional (3D) heat maps plotting the signal strength and other QoS parameters may be generated based on the measurements.

Furthermore, weather conditions such as rain and snow may affect the wireless link performance, so multiple measurement campaigns may be carried out at different times to model different weather conditions. The algorithm may then exploit the collected data and the 2D or 3D heat maps to find the most efficient route for the unmanned vehicle.

To keep the measurement database up to date, unmanned vehicles which regularly operate in the area of interest can collaborate and share, in realtime (or offline, after the mission), the measured network QoS data. In this way, it is possible to passively monitor known areas and reduce the number of dedicated measurement campaigns.

During a mission, if an unmanned vehicle detects that the network performance of an area that was previously advertised as good has become worse, an alert may be sent to the management system or server storing the database of the QoS data. The outdoor wireless environment may change significantly due to, for example, large construction works that may obstruct the signal. In order to avoid the interruption of the mission, the unmanned vehicle (e.g., aerial drone) may vertically scan the location to detect whether the network signal improves in other altitudes, and this information may be conveyed into the heatmaps to assist the route planning of future missions.

The example embodiments described herein may help unmanned vehicles to carry out missions in a more reliable manner. For example, for emergency use cases (e.g., transportation of human organs between hospitals) unmanned aerial vehicles (UAVs) can be utilized to avoid traffic jams in crowded cities or during rush hours. The example embodiments allow the UAVs to follow a path that ensures a more stable UAV remote communication, and thus avoid disruptions of the missions.

The example embodiments described herein may be applied to any kind of outdoor or indoor environments (e.g., industrial facilities, offices, underground mines). For example, automatic UAV facility surveillance monitoring may require low-latency communication to maintain good video streaming quality. The same applies for automatic indoor heavy machinery inspections. It should be noted that the weather condition factor is not applicable to indoor environments. Indoor environments may also facilitate better 3D heatmap generation and precision compared to outdoor environments, since it is faster to scan the physical indoor environment.

FIG. 1 illustrates a simplified example of a system, to which some example embodiments may be applied. The connections shown in FIG. 1 may be physical connections or logical connections. It is apparent to a person skilled in the art that the system may also comprise other physical and logical entities than those shown in FIG. 1.

The system comprises at least a server 110 and one or more unmanned vehicles 100, 101, 130. The system may further comprise a user device 120 of a user (e.g., remote human operator of the unmanned vehicle 130).

Herein an unmanned vehicle may refer to, for example, an unmanned aerial vehicle (UAV), or an unmanned surface vehicle (USV), or an unmanned underwater vehicle (UUV), or a ground-based robot or self-driving car or automated guided vehicle (AGV).

The one or more unmanned vehicles 100, 101, 130 may be equipped with a wireless modem or router, measurement software for measuring quality of service measurements, and a global positioning system (GPS) tracking device.

The unmanned vehicle 130 may be operated remotely by the user via the user device 120. Alternatively, the unmanned vehicle 130 may be operated automatically by a computer system and programmed to move autonomously, without the need for the user or the user device 120.

The user device 120 may be a computing device operating with or without a subscriber identification module (SIM), including, but not limited to, the following types of computing devices: a laptop computer, a desktop computer, a tablet, a mobile phone, a smartphone, a handheld remote controller, an augmented reality (AR) headset, a virtual reality (VR) headset, a heads-up display (HUD), or a wearable device (e.g., a watch, earphones or eyeglasses) with radio parts.

The server 110 may refer to a computer or system configured to provide services to other devices 120, 130 in the network. For example, the server 110 may comprise an edge server running in an edge cloud close to the user device 120. Edge cloud computing is a distributed computing framework that brings enterprise applications closer to data sources such as internet of things (loT) devices or local edge servers, reducing latency and bandwidth use while enabling faster and localized data processing.

Alternatively, the server 110 may be a cloud server, i.e., a virtual server (as opposed to a physical server) that runs in a cloud computing environment. The cloud server may be built, hosted, and delivered through a cloud computing platform via the internet and can be accessed remotely.

The server 110 may comprise a database 111 comprising a set of historical quality of service measurements related to network performance of the wireless communication network (e.g., 5G network). Alternatively, the database 111 may be an external database (e.g., hosted on the cloud computing platform) outside of the server 110.

For example, the set of historical quality of service measurements may refer to real measurements that have been previously measured by one or more unmanned vehicles 100, 101 in the past in certain areas and stored in the database 111. The measurements may be collected from a two-dimensional or three-dimensional space. In case the measurements are collected from a three-dimensional space, then the one or more unmanned vehicles 100, 101 may collect the measurements by flying at different altitudes.

For example, to keep the database 111 updated over time, UAVs 100, 101, which frequently fly in the same area, may update the QoS measurements from time to time (in real-time or in an offline manner), contributing to reporting QoS faults or changes over time. In this way, network faults or issues may also be identified and reported to the network operator. In the future, UAVs may be extensively utilized for package deliveries, so numerous UAVs will have the opportunity to scan multiple geographical areas, keeping the database 111 updated. For example, if dedicated corridors will be used for the UAV traffic, the size or volume of the 2D or 3D area to be scanned and monitored by the UAVs can be kept more feasible.

The server 110 may further comprise a route calculation algorithm 112 configured to determine a route 200 from a departure point 201 to a destination point 202 for the unmanned vehicle 130, wherein the determination is based at least on the set of historical quality of service measurements stored in the database 111, and one or more quality of service requirements related to the network performance of the wireless communication network (e.g., 5G network). The one or more quality of service requirements may be pre-defined or received as a user input from the user device 120.

Quality of service (QoS) is a network performance management strategy that ensures a high level of performance, reliability, and availability for specific types of data traffic or applications. QoS aims to prioritize and manage network resources efficiently to meet the specific requirements of different data flows, reducing latency, packet loss, and jitter, and improving overall network performance.

For example, the one or more quality of service requirements may comprise at least one of: a maximum network delay, a minimum throughput, a maximum packet loss ratio, or a maximum jitter.

The network delay refers to the time it takes for a data packet to travel from the source to the destination (e.g., from the unmanned vehicle 130 to the measurement server 140, or vice versa), including transmission, processing, and propagation delays. For example, the network delay may be measured in milliseconds (ms). This delay can be affected by various factors including signal interference, network congestion, and the distance between the source and the destination. The network delay may also be referred to as latency.

The throughput refers to the rate at which data is successfully transmitted from the source to the destination (e.g., from the unmanned vehicle 130 to the measurement server 140, or vice versa), over a specific period of time. The throughput is a key performance indicator of network efficiency and it may be measured in bits per second (bps) or its derivatives (kbps, Mbps, etc.). Throughput in a RAN is influenced by factors such as network congestion, signal interference, available bandwidth, and the quality of the wireless connection. High throughput indicates efficient data transmission and better network performance.

The packet loss ratio is a measure that represents the proportion of data packets transmitted from the source to the destination (e.g., from the unmanned vehicle 130 to the measurement server 140, or vice versa) that fail to reach their destination. The packet loss ratio may be calculated by dividing the number of lost packets by the total number of packets sent. The packet loss ratio may be expressed as a percentage, for example. Packet loss can occur due to various reasons, including network congestion, faulty hardware, software errors, signal interference, or issues with network protocols. A high packet loss ratio can lead to degradation in network performance, resulting in issues like delayed or interrupted data transmission, reduced throughput, and poor quality of service.

Jitter refers to the variability in the delay of received data packets. In other words, jitter is the inconsistency or variation in the time it takes for data packets to travel from the source to the destination (e.g., from the unmanned vehicle 130 to the measurement server 140, or vice versa). Jitter can be caused by various factors including network congestion, route changes, or interference.

The server 110 is configured to transmit, to the unmanned vehicle 130 (e.g., in case of an autonomous drone) or to the user device 120 (e.g., in case the unmanned vehicle 130 is controlled remotely by the user), information indicating the route 200.

The unmanned vehicle 130 and/or the user device 120 may be configured to receive the information from the server 110, and to navigate the unmanned vehicle 130 on the route 200.

The unmanned vehicle 130 may be further configured to measure a set of quality of service measurements while traveling on the route 200, and to transmit the set of quality of service measurements to the database 111 comprising the set of historical quality of service measurements. One or more QoS parameters, or a combination of multiple QoS parameters, may be measured at each measurement point on the route 200. For example, at a given point on the route 200, the unmanned vehicle may measure the network delay, throughput, jitter and packet loss ratio.

For example, there may be a continuous flow of data packets (continuous connection) between the unmanned vehicle 130 and a measurement server 140, wherein these data packets may be used for measuring the QoS measurements, while the unmanned vehicle 130 is traveling on the route 200. These QoS measurements that are obtained by transmitting and receiving data packets between the unmanned vehicle 130 and the measurement server 140 may also be referred to as two-point measurements, whereas one-point measurements only involve receiving radio signal strength values. The measurement server 140 is a reference server that may be run in the cloud computing platform or edge cloud. The measurement server 140 may be an independent entity running in a separate server from the server 110. Alternatively, the measurement server 140 may be run in the server 110.

FIG. 2 illustrates an example of a route 200 from a departure point 201 to a destination point 202 for the unmanned vehicle 130, as determined by the route calculation algorithm 112. As shown in FIG. 2, the route 200 is determined such that it only passes through areas with good connection quality to one or more RAN nodes 210, 211, 212, 213, 214, 215, as indicated by the set of historical quality of service measurements, while avoiding areas with bad connection quality.

The one or more RAN nodes 210, 211, 212, 213, 214, 215 provide the radio cells in the cellular communication network. The one or more RAN nodes 210, 211, 212, 213, 214, 215 may be configured to be in a wireless connection with the unmanned vehicle 130. The one or more RAN nodes 210, 211, 212, 213, 214, 215 may include or be coupled to transceivers. From the transceivers, a connection may be provided to an antenna unit that establishes a bi-directional radio link to the unmanned vehicle 130. The antenna unit may comprise an antenna or antenna element, or a plurality of antennas or antenna elements. The wireless connection (e.g., radio link) from the unmanned vehicle 130 to the RAN node may be called uplink (UL) or reverse link, and the wireless connection (e.g., radio link) from the RAN node to the unmanned vehicle may be called downlink (DL) or forward link.

It should be appreciated that the RAN node or its functionalities may be implemented by using any node, host, server, access point or other entity suitable for providing such functionalities. For example, the RAN node may be an evolved NodeB (abbreviated as eNB or eNodeB), or a next generation evolved NodeB (abbreviated as ng-eNB), or a next generation NodeB (abbreviated as gNB or gNodeB), providing the radio cell.

FIG. 3 illustrates a signal flow diagram according to an example embodiment.

Referring to FIG. 3, at 301, the server 110 receives, from the user device 120, one or more user inputs for determining a route 200 from a departure point 201 to a destination point 202 for an unmanned vehicle 130. The user may set the one or more inputs via a graphical user interface of the user device 120. For example, the one or more user inputs may comprise a request for determining a route 200 from the departure point 201 to the destination point 202.

The one or more user inputs may further comprise a user input indicating one or more quality of service requirements related to network performance. Alternatively, the one or more quality of service requirements may be pre-defined at the server 110. For example, the one or more quality of service requirements may comprise at least one of: a maximum network delay, a minimum throughput, a maximum packet loss ratio, or a maximum jitter.

The one or more user inputs may further comprise a user input indicating a desired granularity of the route 200, wherein the desired granularity of the route 200 indicates a desired distance between the set of two-dimensional or three-dimensional geographical coordinates of the route 200. In this way, the user may control how precise or smooth the movements on the route 200 are.

The one or more user inputs may further comprise a user input indicating a time window of a set of historical quality of service measurements to be used for determining the route 200. In this way, the user may set a customized date range for the measurements to be utilized for determining the route 200 (e.g., to only use measurements that were collected recently). For example, the user may wish to utilize only the measurements collected during the past few days or a certain month or time of year.

For example, the set of historical quality of service measurements may comprise at least one of: a set of network delay values measured (e.g., by one or more other unmanned vehicles 100, 101) in an area between the departure point 201 and the destination point 202, a set of throughput values measured (e.g., by one or more other unmanned vehicles 100, 101) in the area between the departure point 201 and the destination point 202, a set of packet loss ratios measured (e.g., by one or more other unmanned vehicles 100, 101) in the area between the departure point 201 and the destination point 202, ora set of jitter values measured (e.g., by one or more other unmanned vehicles 100, 101) in the area between the departure point 201 and the destination point 202.

The set of historical quality of service measurements may be associated with a two-dimensional space (e.g., latitude and longitude coordinates) or a three- dimensional space (e.g., latitude, longitude, and altitude coordinates).

The set of historical quality of service measurements used for the route calculation may comprise only a part or subset of all the measurements included in the database 111, since there may be a very large number of measurements in the database 111. By selecting the measurement points satisfying the user requirements or policies (e.g., the QoS measurements for the parameters indicated in the QoS requirements, the time window, etc.), the dataset size used for the route calculation may be filtered or reduced by including only the relevant data.

At 302, the server 110 may select, based on the filtered dataset size of the set of historical quality of service measurements satisfying the user requirements or policies in the database 111, an algorithm 112 to be used for determining the route 200. The dataset size refers to the number of measurement points in the set of historical quality of service measurements.

For example, the algorithm 112 may comprise the A-star (A*) algorithm or the Dijkstra algorithm, or an artificial intelligence or machine learning algorithm. The A* algorithm may perform well when the dataset size is large, but suffers a bit when the size is small. Therefore, the server 110 may select the A* algorithm when the dataset size is large (e.g., above a threshold), or the server 110 may select the Dijkstra algorithm when the dataset size is small (e.g., below the threshold). In other words, the server 110 may decide, based on the filtered dataset size, which algorithm to use to calculate the route 200 in the shortest amount of time possible. At 303, based on the one or more user inputs, the server 110 determines the route 200 from the departure point 201 to the destination point 202 for the unmanned vehicle 130, wherein the determination is based at least on the set of historical quality of service measurements and the one or more quality of service requirements related to network performance.

The route 200 may be determined by using the selected algorithm. The algorithm may plan the most efficient (e.g., shortest) route from the departure point 201 to the destination point 202 in order to have reliable network connectivity, which meets the one or more quality of service requirements.

The calculation of the route 200 may be further optimized by using, for example, a graphics processing unit (GPU) server or parallel computing.

The route 200 may be determined based on a two-dimensional or three- dimensional heatmap generated based on the set of historical quality of service measurements. The heatmap is a visual representation that indicates the radio signal strength and other QoS parameters over a certain geographical area or location. The heatmap may use colors to represent the intensity of the network performance: warmer colors (like red or orange) may indicate stronger network performance, while cooler colors (like blue or green) may indicate weaker network performance. The heatmap may be generated based on a combination of multiple QoS parameters (e.g., network delay, throughput, and jitter). The heatmap may differ based on the QoS requirements for each mission.

The determination of the route 200 may be further based on a precision of the heatmap (e.g., a circular area covering the surroundings of the unmanned vehicle 130).

In case the server 110 received the user input indicating the desired granularity of the route 200, then the route 200 may be determined by determining the set of two-dimensional or three-dimensional geographical coordinates according to the desired granularity of the route 200. In this way, the user may control how precise or smooth the movements on the route 200 are.

In case the server 110 received the user input indicating the time window of the set of historical quality of service measurements, then the route 200 may be determined based on the set of historical quality of service measurements from the time window indicated by the user input.

The route 200 may be determined based further on at least one of: regulatory requirements (e.g., minimum flight height and/or maximum flight height) indicating one or more restricted areas to be avoided, a load weight of the unmanned vehicle 130, a battery level of the unmanned vehicle 130, an urgency level of getting the unmanned vehicle 130 to the destination point 202, a distance from the departure point 201 to the destination point 202, or weather data (weather conditions) in the area where the departure point 201 and the destination point 202 are located.

For example, based on the regulatory requirements, the server 110 may check whether the original route crosses a sensitive area and then calculates the best route according to minimum height and/or maximum height enforced (e.g., first finds the best route in each area, and then merges the route).

As another example, based on the battery level, the route 200 may be optimized in terms of the power consumption of the unmanned vehicle 130 (e.g., by avoiding longer routes and/or altitude variations that would drain the battery).

At 304, the server 110 transmits, to the user device 120 controlling the unmanned vehicle 130, information indicating the determined route 200. The information may also indicate an estimated or predicted arrival time or duration of when the unmanned vehicle 130 will arrive at the destination point 202, as calculated by the server 110. The user device 120 receives the information.

The information indicating the determined route 200 may comprise a set of two-dimensional geographical coordinates (e.g., latitude and longitude) or a set of three-dimensional geographical coordinates (e.g., latitude, longitude, and altitude) from the departure point 201 to the destination point 202. In case of three- dimensional coordinates, the unmanned vehicle 130 may fly at different altitudes along the route 200 according to the altitude coordinates in order to maintain the connection quality, since the connection quality may vary depending on the altitude. At 305, the user device 120 may visualize the route 200 to the user via a graphical user interface. The route 200 may be visualized in two dimensions or three dimensions. The user device 120 may also visualize or indicate the esti- mated/predicted arrival time and/or duration to the user.

At 306, the user device 120 transmits one or more commands to the unmanned vehicle 130 for navigating the unmanned vehicle 130 on the route 200. The user may provide the one or more commands based on the visualization to manoeuvre the unmanned vehicle 130 along the route 200. The user device 120 may transmit the one or more commands to the unmanned vehicle 130 via a RAN node 210, 211, 212, 213, 214, 215 (e.g., a gNB).

At 307, the unmanned vehicle 130 may measure a set of (new) quality of service measurements while traveling on the route 200. The unmanned vehicle 130 may perform the measurements continuously, such that the measurements are started at the departure point 201 and stopped at the destination point 202.

The set of (new) quality of service measurements may comprise at least one of: a set of network delay values measured by the unmanned vehicle 130 while traveling on the route 200, a set of throughput values measured by the unmanned vehicle 130 while traveling on the route 200, a set of packet loss ratios measured by the unmanned vehicle 130 while traveling on the route 200, or a set of jitter values measured by the unmanned vehicle 130 while traveling on the route 200.

At 308, the unmanned vehicle 130 may transmit the set of (new) quality of service measurements to the server 110 to be stored in the database 111 comprising the set of historical quality of service measurements. The unmanned vehicle 130 may transmit the set of (new) quality of service measurements directly to the server 110, or the unmanned vehicle 130 may transmit the set of (new) quality of service measurements to the user device 120, and the user device 120 may then forward the set of (new) quality of service measurements to the server 110.

The unmanned vehicle 130 may transmit the set of (new) quality of service measurements continuously in real-time while traveling on the route 200, or after the unmanned vehicle 130 arrives at the destination point 202. At 309, the server 110 may store the received set of (new) quality of service measurements in the database 111 comprising the set of historical quality of service measurements.

FIG. 4 illustrates a signal flow diagram according to an example embodiment.

Referring to FIG. 4, at 401, the server 110 receives, from the user device 120, one or more user inputs for determining a route 200 from a departure point 201 to a destination point 202 for an unmanned vehicle 130. The user may set the one or more inputs via a graphical user interface of the user device 120. For example, the one or more user inputs may comprise a request for determining a route 200 from the departure point 201 to the destination point 202.

The one or more user inputs may further comprise a user input indicating one or more quality of service requirements related to network performance. Alternatively, the one or more quality of service requirements may be pre-defined at the server 110, or automatically set by the unmanned vehicle 130 itself (e.g., depending on the type of the trip or mission). For example, the one or more quality of service requirements may comprise at least one of: a maximum network delay, a minimum throughput, a maximum packet loss ratio, or a maximum jitter.

The one or more user inputs may further comprise a user input indicating a desired granularity of the route 200, wherein the desired granularity of the route 200 indicates a desired distance between the set of two-dimensional or three-dimensional geographical coordinates of the route 200.

The one or more user inputs may further comprise a user input indicating a time window of a set of historical quality of service measurements to be used for determining the route 200. In this way, the user may set a customized date range for the measurements to be utilized for determining the route 200. For example, the user may wish to utilize only the measurements collected during the past few days or a certain month or time of year.

For example, the set of historical quality of service measurements may comprise at least one of: a set of network delay values measured (e.g., by one or more other unmanned vehicles 100, 101) in an area between the departure point 201 and the destination point 202, a set of throughput values measured (e.g., by one or more other unmanned vehicles 100, 101) in the area between the departure point 201 and the destination point 202, a set of packet loss ratios measured (e.g., by one or more other unmanned vehicles 100, 101) in the area between the departure point 201 and the destination point 202, or a set of jitter values measured (e.g., by one or more other unmanned vehicles 100, 101) in the area between the departure point 201 and the destination point 202.

The set of historical quality of service measurements may be associated with a two-dimensional space (e.g., latitude and longitude coordinates) or a three- dimensional space (e.g., latitude, longitude and altitude coordinates).

At 402, the server 110 may select, based on the filtered dataset size of the set of historical quality of service measurements in the database 111, an algorithm 112 to be used for determining the route 200. The dataset size refers to the number of measurement points satisfying the user requirements or policies in the set of historical quality of service measurements.

For example, the algorithm 112 may comprise the A-star (A*) algorithm or the Dijkstra algorithm, or an artificial intelligence or machine learning algorithm. The A* algorithm may perform well when the dataset size is large, but suffers a bit when the size is small. Therefore, the server 110 may select the A* algorithm when the dataset size is large (e.g., above a threshold), or the server 110 may select the Dijkstra algorithm when the dataset size is small (e.g., below the threshold). In other words, the server 110 may decide, based on the filtered dataset size, which algorithm to use to calculate the route 200 in the shortest amount of time possible. At 403, based on the one or more user inputs, the server 110 determines the route 200 from the departure point 201 to the destination point 202 for the unmanned vehicle 130, wherein the determination is based at least on the set of historical quality of service measurements and the one or more quality of service requirements related to network performance.

In case the server 110 received the user input indicating the desired granularity of the route 200, then the route 200 may be determined by determining the set of two-dimensional or three-dimensional geographical coordinates according to the desired granularity of the route 200.

The route 200 may be determined based further on at least one of: regulatory requirements (e.g., minimum flight height and/or maximum flight height) indicating one or more restricted areas to be avoided, a load weight of the unmanned vehicle 130, a battery level of the unmanned vehicle 130, an urgency level of getting the unmanned vehicle 130 to the destination point 202, or weather data in the area where the departure point 201 and the destination point 202 are located.

At 404, the server 110 transmits, to the unmanned vehicle 130, information indicating the determined route 200. The unmanned vehicle 130 receives the information.

The information indicating the determined route 200 may comprise a set of two-dimensional geographical coordinates (e.g., latitude and longitude) or a set of three-dimensional geographical coordinates (e.g., latitude, longitude, and altitude) from the departure point 201 to the destination point 202. In case of three- dimensional coordinates, the unmanned vehicle 130 may fly at different altitudes along the route 200 according to the altitude coordinates in order to maintain the connection quality, since the connection quality may vary depending on the altitude.

At 405, based on the information, the unmanned vehicle 130 navigates autonomously on the route 200.

At 406, the unmanned vehicle 130 may measure a set of (new) quality of service measurements while traveling on the route 200. The unmanned vehicle 130 may perform the measurements continuously, such that the measurements are started at the departure point 201 and stopped at the destination point 202.

At 407, the unmanned vehicle 130 may transmitthe setof (new) quality of service measurements to the server 110 to be stored in the database 111 comprising the set of historical quality of service measurements. The unmanned vehicle 130 may transmit the set of (new) quality of service measurements to the server 110 via a RAN node 210, 211, 212, 213, 214 (e.g., a gNB).

The unmanned vehicle 130 may transmit the set of (new) quality of service measurements continuously in real-time while traveling on the route 200, or after the unmanned vehicle 130 arrives at the destination point 202.

At 408, the server 110 may store the received set of (new) quality of service measurements in the database 111 comprising the set of historical quality of service measurements.

FIG. 5 illustrates a flow chart according to an example embodiment of a method performed by an apparatus 800 depicted in FIG. 8. For example, the apparatus 800 may comprise a server 110 or any other computing device.

Referring to FIG. 5, in block 501, the apparatus 800 determines a route 200 from a departure point 201 to a destination point 202 for an unmanned vehicle 130, wherein the determination is based at least on a set of historical quality of service measurements and one or more quality of service requirements related to network performance.

For example, the one or more quality of service requirements may comprise at least one of: a maximum network delay, a minimum throughput, a maximum packet loss ratio, or a maximum jitter. For example, the set of historical quality of service measurements comprises at least one of: a set of network delay values measured in an area between the departure point and the destination point, a set of throughput values measured in the area between the departure point and the destination point, a set of packet loss ratios measured in the area between the departure point and the destination point, or a set of jitter values measured in the area between the departure point and the destination point.

The set of historical quality of service measurements may be obtained from a database 111.

In block 502, the apparatus 800 transmits, to the unmanned vehicle 130 or to a user device 120 controlling the unmanned vehicle 130, information indicating the route 200.

The apparatus 800 may receive, from the unmanned vehicle 130, a set of quality of service measurements measured by the unmanned vehicle 130 while traveling on the route 200. The apparatus 800 may store the received set of quality of service measurements in the database 111 comprising the set of historical quality of service measurements.

The set of historical quality of service measurements may be associated with a two-dimensional space, in which case the information indicating the route 200 may comprise a set of two-dimensional geographical coordinates (e.g., latitude and longitude) from the departure point 201 to the destination point 202.

Alternatively, the set of historical quality of service measurements may be associated with a three-dimensional space, in which case the information indicating the route 200 may comprise a set of three-dimensional geographical coordinates (e.g. latitude, longitude, and altitude) from the departure point 201 to the destination point 202.

The apparatus 800 may receive a user input indicating a desired granularity of the route 200, wherein the desired granularity of the route indicates a desired distance between the set of two-dimensional or three-dimensional geographical coordinates, wherein the route may be determined by determining the set of two-dimensional or three-dimensional geographical coordinates according to the desired granularity of the route 200.

The apparatus 800 may select, based on a dataset size of the set of historical quality of service measurements, an algorithm to be used for determining the route 200, wherein the route 200 may be determined by using the selected algorithm.

The apparatus 800 may receive a user input indicating a time window of the set of historical quality of service measurements to be used for determining the route 200, wherein the route 200 may be determined based on the set of historical quality of service measurements from the time window indicated by the user input.

The route 200 may be determined based further on at least one of: regulatory requirements indicating one or more restricted areas to be avoided, a load weight of the unmanned vehicle 130, a battery level of the unmanned vehicle 130, an urgency level of getting the unmanned vehicle 130 to the destination point 202, or weather data.

FIG. 6 illustrates a flow chart according to an example embodiment of a method performed by an apparatus 700 depicted in FIG. 7. For example, the apparatus 700 may comprise, or be comprised in, a user device 120, or a control unit of the unmanned vehicle 130.

Referring to FIG. 6, in block 601, the apparatus 700 receives information indicating a route 200 from a departure point 201 to a destination point 202 for an unmanned vehicle 130, wherein the route 200 is based at least on a set of historical quality of service measurements and one or more quality of service requirements related to network performance.

In block 602, the apparatus 700 navigates the unmanned vehicle 130 on the route 200, i.e., sends commands to the unmanned vehicle 130 to guide the movement of the unmanned vehicle 130 along the route 200.

The apparatus 700 may obtain a set of quality of service measurements measured by the unmanned vehicle 130 while traveling on the route 200. The apparatus 700 may transmit the set of quality of service measurements to a database 111 comprising the set of historical quality of service measurements. The set of quality of service measurements may be transmitted in real-time while the unmanned vehicle 130 is traveling on the route 200, or the set of quality of service measurements may be transmitted after the unmanned vehicle 130 arrives at the destination point 202.

The set of quality of service measurements may comprise at least one of: a set of network delay values measured by the unmanned vehicle 130 while traveling on the route 200, a set of throughput values measured by the unmanned vehicle 130 while traveling on the route 200, a set of packet loss ratios measured by the unmanned vehicle 130 while traveling on the route 200, or a set of jitter values measured by the unmanned vehicle 130 while traveling on the route 200.

The functions described above by means of FIG. 3 to FIG. 6 are in no absolute chronological order, and some of them may be performed simultaneously or in an order differing from the described one. Other functions can also be executed between them or within them, and other information may be sent, and/or other rules applied. Some of the functions or one or more pieces of information can also be left out or replaced by a corresponding function or one or more pieces of information.

As used herein, "at least one of the following: <a list of two or more elements^’ and "at least one of <a list of two or more elements>" and similar wording, where the list of two or more elements are joined by "and" or "or", mean at least any one of the elements, or at least any two or more of the elements, or at least all the elements.

FIG. 7 illustrates an example of an apparatus 700 comprising means for performing one or more of the example embodiments described above. For example, the apparatus 700 may comprise, or be comprised in, a user device 120, or a control unit of the unmanned vehicle 130.

The control unit may refer to an integrated system of the unmanned vehicle 130 responsible for processing inputs, executing algorithms, and generating commands to manage and coordinate the unmanned vehicle's operations, navigation, and responses to environmental conditions. In other words, the control unit ensures the autonomous functionality, stability, and safety of the unmanned vehicle. The control unit may comprise various sensors, communication modules, and actuators.

The apparatus 700 may comprise a circuitry or a chipset applicable for realizing one or more of the example embodiments described above. For example, the apparatus 700 may comprise at least one processor 710. The at least one processor 710 interprets instructions (e.g., computer program instructions) and processes data. The at least one processor 710 may comprise one or more programmable processors. The at least one processor 710 may comprise programmable hardware with embedded firmware and may, alternatively or additionally, comprise one or more application-specific integrated circuits (ASICs).

The at least one processor 710 is coupled to at least one memory 720. The at least one processor is configured to read and write data to and from the at least one memory 720. The at least one memory 720 may comprise one or more memory units. The memory units may be volatile or non-volatile. It is to be noted that there may be one or more units of non-volatile memory and one or more units of volatile memory or, alternatively, one or more units of non-volatile memory, or, alternatively, one or more units of volatile memory. Volatile memory may be for example random-access memory (RAM), dynamic random-access memory (DRAM) or synchronous dynamic random-access memory (SDRAM). Non-volatile memory may be for example read-only memory (ROM), programmable read-only memory (PROM), electronically erasable programmable read-only memory (EEPROM), flash memory, optical storage or magnetic storage. In general, memories may be referred to as non-transitory computer readable media. The term "non- transitory," as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM). The at least one memory 720 stores computer readable instructions that are executed by the at least one processor 710 to perform one or more of the example embodiments described above. For example, non-volatile memory stores the computer readable instructions, and the at least one processor 710 executes the instructions using volatile memory for temporary storage of data and/or instructions. The computer readable instructions may refer to computer program code.

The computer readable instructions may have been pre-stored to the at least one memory 720 or, alternatively or additionally, they may be received, by the apparatus, via an electromagnetic carrier signal and/or may be copied from a physical entity such as a computer program product. Execution of the computer readable instructions by the at least one processor 710 causes the apparatus 700 to perform one or more of the example embodiments described above. That is, the at least one processor and the at least one memory storing the instructions may provide the means for providing or causing the performance of any of the methods and/or blocks described above.

In the context of this document, a "memory" or "computer-readable media" or "computer-readable medium" may be any non-transitory media or medium or means that can contain, store, communicate, propagate or transport the instructions for use by or in connection with an instruction execution system, apparatus, or device, such as a computer. The term "non-transitory," as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM).

The apparatus 700 may further comprise, or be connected to, an input unit 730. The input unit 730 may comprise one or more interfaces for receiving input. The one or more interfaces may comprise for example one or more temperature, motion and/or orientation sensors, one or more cameras, one or more accelerometers, one or more microphones, one or more buttons and/or one or more touch detection units. Further, the input unit 730 may comprise an interface to which external devices may connect to.

The apparatus 700 may also comprise an output unit 740. The output unit may comprise or be connected to one or more displays capable of rendering visual content, such as a light emitting diode (LED) display, a liquid crystal display (LCD) and/or a liquid crystal on silicon (LCoS) display. The output unit 740 may further comprise one or more audio outputs. The one or more audio outputs may be for example loudspeakers. The apparatus 700 further comprises a connectivity unit 750. The connectivity unit 750 enables wireless or wired connectivity to one or more external devices, such as the server 110, for example via one or more RAN nodes 210, 211, 212, 213, 214, 215 of the wireless communication network. The connectivity unit 750 may comprise at least one transmitter and at least one receiver that may be integrated to the apparatus 700 or that the apparatus 700 may be connected to. The at least one transmitter comprises at least one transmission antenna, and the at least one receiver comprises at least one receiving antenna. The connectivity unit 750 may comprise an integrated circuit or a set of integrated circuits that provide the wireless communication capability for the apparatus 700. Alternatively, the wireless connectivity may be a hardwired application-specific integrated circuit (ASIC). The connectivity unit 750 may also provide means for performing at least some of the blocks or functions (e.g., transmitting and receiving) of one or more example embodiments described above. The connectivity unit 750 may comprise one or more components, such as: power amplifier, digital front end (DFE), analog-to-digital converter (ADC), digital-to-analog converter (DAC), frequency converter, (de) modulator, and/or encoder/decoder circuitries, controlled by the corresponding controlling units.

It is to be noted that the apparatus 700 may further comprise various components not illustrated in FIG. 7. The various components may be hardware components and/or software components.

FIG. 8 illustrates an example of an apparatus 800 comprising means for performing one or more of the example embodiments described above. For example, the apparatus 800 may comprise, or be comprised in, a server 110 or any other computing device.

The apparatus 800 may comprise, for example, a circuitry or a chipset applicable for realizing one or more of the example embodiments described above. The apparatus 800 may be an electronic device comprising one or more electronic circuitries. The apparatus 800 may comprise a control circuitry 810 such as at least one processor, and at least one memory 820 storing instructions 822 which, when executed by the at least one processor, cause the apparatus 800 to carry out one or more of the example embodiments described above. Such instructions 822 may, for example, include computer program code (software). The at least one processor and the at least one memory storing the instructions may provide the means for providing or causing the performance of any of the methods and/or blocks described above.

The processor is coupled to the memory 820. The processor is configured to read and write data to and from the memory 820. The memory 820 may comprise one or more memory units. The memory units may be volatile or nonvolatile. It is to be noted that there may be one or more units of non-volatile memory and one or more units of volatile memory or, alternatively, one or more units of non-volatile memory, or, alternatively, one or more units of volatile memory. Volatile memory may be for example random-access memory (RAM), dynamic random-access memory (DRAM) or synchronous dynamic random-access memory (SDRAM). Non-volatile memory may be for example read-only memory (ROM), programmable read-only memory (PROM), electronically erasable programmable read-only memory (EEPROM), flash memory, optical storage or magnetic storage. In general, memories may be referred to as non-transitory computer readable media. The term "non-transitory," as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM). The memory 820 stores computer readable instructions that are executed by the processor. For example, non-volatile memory stores the computer readable instructions, and the processor executes the instructions using volatile memory for temporary storage of data and/or instructions.

The computer readable instructions may have been pre-stored to the memory 820 or, alternatively or additionally, they may be received, by the apparatus, via an electromagnetic carrier signal and/or may be copied from a physical entity such as a computer program product. Execution of the computer readable instructions causes the apparatus 800 to perform one or more of the functionalities described above.

The memory 820 may be implemented using any suitable data storage technology, such as semiconductor-based memory devices, flash memory, magnetic memory devices and systems, optical memory devices and systems, fixed memory and/or removable memory.

The apparatus 800 may further comprise or be connected to a communication interface 830, such as a radio unit, comprising hardware and/or software for realizing communication connectivity with one or more wireless communication devices according to one or more communication protocols. The communication interface 830 comprises at least one transmitter (Tx) and at least one receiver (Rx) that may be integrated to the apparatus 800 or that the apparatus 800 may be connected to. The communication interface 830 may provide means for performing some of the blocks and/or functions (e.g., transmitting and receiving) for one or more example embodiments described above. The communication interface 830 may comprise one or more components, such as: power amplifier, digital front end (DFE), analog-to-digital converter (ADC), digital-to-analog converter (DAC), frequency converter, (de)modulator, and/or encoder/decoder circuitries, controlled by the corresponding controlling units.

The communication interface 830 provides the apparatus with radio communication capabilities to communicate in the wireless communication network. The communication interface may, for example, provide a radio interface to the unmanned vehicle 130 and/or to the user device 120 via one or more RAN nodes 210, 211, 212, 213, 214, 215 of the wireless communication network.

It is to be noted that the apparatus 800 may further comprise various components not illustrated in FIG. 8. The various components may be hardware components and/or software components.

As used in this application, the term "circuitry" may refer to one or more or all of the following: a) hardware-only circuit implementations (such as implementations in only analog and/or digital circuitry); and b) combinations of hardware circuits and software, such as (as applicable): i) a combination of analog and/or digital hardware circuit(s) with software/firmware and ii) any portions of hardware processor(s) with software (including digital signal processor(s), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone, to perform various functions); and c) hardware circuit(s) and/or processor(s), such as a microprocessor's) or a portion of a microprocessor's), that requires software (for example firmware) for operation, but the software may not be present when it is not needed for operation.

This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and/or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

The techniques and methods described herein may be implemented by various means. For example, these techniques may be implemented in hardware (one or more devices), firmware (one or more devices), software (one or more modules), or combinations thereof. For a hardware implementation, the apparatus (es) of example embodiments may be implemented within one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), graphics processing units (GPUs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof. For firmware or software, the implementation can be carried out through modules of at least one chipset (for example procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in a memory unit and executed by processors. The memory unit may be implemented within the processor or externally to the processor. In the latter case, it can be communicatively coupled to the processor via various means, as is known in the art. Additionally, the components of the systems described herein may be rearranged and/or complemented by additional components in order to facilitate the achievements of the various aspects, etc., described with regard thereto, and they are not limited to the precise configurations set forth in the given figures, as will be appreciated by one skilled in the art.

It will be obvious to a person skilled in the art that, as technology advances, the inventive concept may be implemented in various ways within the scope of the claims. The embodiments are not limited to the example embodiments described above, but may vary within the scope of the claims. Therefore, all words and expressions should be interpreted broadly, and they are intended to illustrate, not to restrict, the embodiments.

Claims

1. An apparatus comprising at least one processor, and at least one memory storing instructions which, when executed by the at least one processor, cause the apparatus at least to: select, based on a dataset size of a set of historical quality of service measurements, an algorithm to be used for determining a route from a departure point to a destination point for an unmanned vehicle; determine, by using the algorithm selected, the route from the departure point to the destination point for the unmanned vehicle, wherein the determination is based at least on the set of historical quality of service measurements and one or more quality of service requirements related to network performance; and transmit, to the unmanned vehicle or to a user device controlling the unmanned vehicle, information indicating the route.

2. The apparatus according to claim 1, wherein the one or more quality of service requirements comprise at least one of: a maximum network delay, a minimum throughput, a maximum packet loss ratio, or a maximum jitter.

3. The apparatus according to any preceding claim, wherein the set of historical quality of service measurements comprises at least one of: a set of network delay values measured in an area between the departure point and the destination point, a set of throughput values measured in the area between the departure point and the destination point, a set of packet loss ratios measured in the area between the departure point and the destination point, or a set of jitter values measured in the area between the departure point and the destination point.

4. The apparatus according to any preceding claim, further being caused to: receive, from the unmanned vehicle, a set of quality of service measurements measured by the unmanned vehicle while traveling on the route; and store the set of quality of service measurements in a database comprising the set of historical quality of service measurements.

5. The apparatus according to any preceding claim, wherein the set of historical quality of service measurements are associated with a three-dimensional space, and wherein the information indicating the route comprises a set of three- dimensional geographical coordinates from the departure point to the destination point.

6. The apparatus according to claim 5, further being caused to: receive a user input indicating a desired granularity of the route, wherein the desired granularity of the route indicates a desired distance between the set of three-dimensional geographical coordinates, wherein the route is determined by determining the set of three-dimensional geographical coordinates according to the desired granularity of the route.

7. The apparatus according to any preceding claim, further being caused to: receive a user input indicating a time window of the set of historical quality of service measurements to be used for determining the route, wherein the route is determined based on the set of historical quality of service measurements from the time window indicated by the user input.

8. The apparatus according to any preceding claim, wherein the route is determined based further on at least one of: regulatory requirements indicating one or more restricted areas to be avoided, a load weight of the unmanned vehicle, a battery level of the unmanned vehicle, an urgency level of getting the unmanned vehicle to the destination point, or weather data.

9. An apparatus comprising at least one processor, and at least one memory storing instructions which, when executed by the at least one processor, cause the apparatus at least to: receive information indicating a route from a departure point to a destination point for an unmanned vehicle, wherein the route is based at least on a set of historical quality of service measurements and one or more quality of service requirements related to network performance; and navigate the unmanned vehicle on the route.

10. The apparatus according to claim 9, further being caused to: obtain a set of quality of service measurements measured by the unmanned vehicle while traveling on the route; and transmit the set of quality of service measurements to a database comprising the set of historical quality of service measurements, wherein the set of quality of service measurements comprises at least one of: a set of network delay values measured by the unmanned vehicle while traveling on the route, a set of throughput values measured by the unmanned vehicle while traveling on the route, a set of packet loss ratios measured by the unmanned vehicle while traveling on the route, or a set of jitter values measured by the unmanned vehicle while traveling on the route.

11. A method comprising: selecting, based on a dataset size of a set of historical quality of service measurements, an algorithm to be used for determining a route from a departure point to a destination point for an unmanned vehicle; determining, by using the algorithm selected, the route from the departure point to the destination point for the unmanned vehicle, wherein the determination is based at least on the set of historical quality of service measurements and one or more quality of service requirements related to network performance; and transmitting, to the unmanned vehicle or to a user device controlling the unmanned vehicle, information indicating the route.

12. The method of claim 11, wherein the one or more quality of service requirements comprise at least one of: a maximum network delay, a minimum throughput, a maximum packet loss ratio, or a maximum jitter.

13. The method of any of claims 11 to 12, wherein the set of historical quality of service measurements comprises at least one of: a set of network delay values measured in an area between the departure point and the destination point, a set of throughput values measured in the area between the departure point and the destination point, a set of packet loss ratios measured in the area between the departure point and the destination point, or a set of jitter values measured in the area between the departure point and the destination point.

14. The method of any of claims 11 to 13, further comprising: receiving, from the unmanned vehicle, a set of quality of service measurements measured by the unmanned vehicle while traveling on the route; and storing the set of quality of service measurements in a database comprising the set of historical quality of service measurements.

15. The method of any of claims 11 to 14, wherein the set of historical quality of service measurements are associated with a three-dimensional space, and wherein the information indicating the route comprises a set of three- dimensional geographical coordinates from the departure point to the destination point.

16. The method of claim 15, further comprising: receiving a user input indicating a desired granularity of the route, wherein the desired granularity of the route indicates a desired distance between the set of three-dimensional geographical coordinates, wherein the route is determined by determining the set of three-dimensional geographical coordinates according to the desired granularity of the route.

17. The method of any of claims 11 to 16, further comprising: receiving a user input indicating a time window of the set of historical quality of service measurements to be used for determining the route, wherein the route is determined based on the set of historical quality of service measurements from the time window indicated by the user input.

18. The method of any of claims 11 to 17, wherein the route is determined based further on at least one of: regulatory requirements indicating one or more restricted areas to be avoided, a load weight of the unmanned vehicle, a battery level of the unmanned vehicle, an urgency level of getting the unmanned vehicle to the destination point, or weather data.

19. A method comprising: receiving information indicating a route from a departure point to a destination point for an unmanned vehicle, wherein the route is based at least on a set of historical quality of service measurements and one or more quality of service requirements related to network performance; and navigating the unmanned vehicle on the route.

20. The method of claim 19, further comprising: obtaining a set of quality of service measurements measured by the unmanned vehicle while traveling on the route; and transmitting the set of quality of service measurements to a database comprising the set of historical quality of service measurements, wherein the set of quality of service measurements comprises at least one of: a set of network delay values measured by the unmanned vehicle while traveling on the route, a set of throughput values measured by the unmanned vehicle while traveling on the route, a set of packet loss ratios measured by the unmanned vehicle while traveling on the route, or a set of jitter values measured by the unmanned vehicle while traveling on the route.

21. A non-transitory computer readable medium comprising program instructions which, when executed by an apparatus, cause the apparatus to perform at least the following: selecting, based on a dataset size of a set of historical quality of service measurements, an algorithm to be used for determining a route from a departure point to a destination point for an unmanned vehicle; determining, by using the algorithm selected, the route from the departure point to the destination point for the unmanned vehicle, wherein the determination is based at least on the set of historical quality of service measurements and one or more quality of service requirements related to network performance; and transmitting, to the unmanned vehicle or to a user device controlling the unmanned vehicle, information indicating the route.

22. A non-transitory computer readable medium comprising program instructions which, when executed by an apparatus, cause the apparatus to perform at least the following: receiving information indicating a route from a departure point to a destination point for an unmanned vehicle, wherein the route is based at least on a set of historical quality of service measurements and one or more quality of service requirements related to network performance; and navigating the unmanned vehicle on the route.