WO2025040117A1 - Electronic device and method for channel measurement, and computer program product - Google Patents

Abstract

The present disclosure relates to an electronic device and a method for channel measurement, and a computer program product. Various embodiments for performing channel measurement in a wireless communication system are described. In one embodiment, the electronic device comprises a processing circuit; and the processing circuit is configured to perform, on the basis of receiving a first number of pilot signals from a terminal device, angle measurement for a channel between a base station and the terminal device, and perform, on the basis of receiving a second number of pilot signals from the terminal device and the angle measurement result for the channel, distance measurement for the channel at a particular angle. The processing circuit is further configured to determine, on the basis of the angle measurement result and the distance measurement result for the channel, transmission parameters for communication between the base station and the terminal device.

Description

Electronic device, method and computer program product for channel measurement

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of Chinese patent application 202311070513.5, entitled “Electronic device, method and computer program product for channel measurement”, filed on August 23, 2023, the entire contents of which are incorporated herein by reference.

Technical Field

The present disclosure relates generally to wireless communications, including techniques for performing measurements on a channel between a base station and a terminal device in a wireless communication system.

Background Art

The widespread deployment of wireless communication systems has met people's voice and data communication needs unprecedentedly. In order to realize ubiquitous intelligent information networks, wireless communication systems use various technologies at different levels. In terms of antenna technology, the number of antenna elements on the base station side can be increased to hundreds, thousands or even more, thus forming an antenna array. In such an antenna array, multiple-input multiple-output (MIMO) and beamforming technologies will have greater application space.

In wireless communication systems that have been deployed or are to be developed (such as NR's 5G system and 6G system), antenna arrays will play a greater role in, for example, beam steering, capacity enhancement, etc. Accordingly, it is desirable to accurately obtain characteristics of one or more aspects of the channel in order to better play the role of the antenna array.

Summary of the invention

The first aspect of the present disclosure relates to a method for a base station, comprising: performing angle measurement for a channel between the base station and the terminal device based on receiving a first number of pilot signals from a terminal device; performing distance measurement for the channel at a specific angle based on receiving a second number of pilot signals from the terminal device and the angle measurement result for the channel; and determining a transmission parameter for communication between the base station and the terminal device based on the angle and distance measurement result for the channel. The first aspect of the present disclosure also relates to an electronic device and a base station for performing the method.

The second aspect of the present disclosure relates to a method for a base station, comprising: obtaining a plurality of pilot signal samples based on receiving a plurality of pilot signals from a terminal device; performing a coarse-grained distance measurement of a channel between the base station and the terminal device based on the plurality of pilot signal samples; and performing a fine-grained distance measurement of the channel through an artificial intelligence model based on the coarse-grained distance measurement result to determine a transmission parameter for communication between the base station and the terminal device. The second aspect of the present disclosure also relates to an electronic device and a base station for performing the method.

The third aspect of the present disclosure relates to a method for a base station, comprising: configuring a first number of pilot signals and a second number of pilot signals to a terminal device through signaling, wherein the first number of pilot signals are indicated as angle measurements of a channel between the base station and the terminal device through flag information, and the second number of pilot signals are indicated as distance measurements of the channel. The first aspect of the present disclosure also relates to an electronic device for performing the method. The third aspect of the present disclosure also relates to an electronic device and a base station for performing the method.

The fourth aspect of the present disclosure relates to a method for a terminal device, comprising: receiving a pilot signal configuration from a network, the pilot signal configuration comprising a first number of pilot signals and a second number of pilot signals; based on the pilot signal configuration, sending the first number of pilot signals to a base station for the base station to perform angle measurement of a channel between the base station and the terminal device; receiving angle measurement information from the base station; and based on the pilot signal configuration and the angle measurement information, sending the second number of pilot signals to the base station for the base station to perform distance measurement of the channel. The fourth aspect of the present disclosure also relates to an electronic device and a terminal device for performing the method.

A fifth aspect of the present disclosure relates to a computer-readable storage medium having executable instructions stored thereon, which, when executed by one or more processors, implement the operations of the methods according to various embodiments of the present disclosure.

A sixth aspect of the present disclosure relates to a computer program product, which comprises instructions, which when executed by a computer enable the implementation of the method according to various embodiments of the present disclosure.

The above summary is provided to summarize some exemplary embodiments to provide a basic understanding of various aspects of the subject matter described herein. Therefore, the above features are merely examples and should not be interpreted as narrowing the scope or spirit of the subject matter described herein in any way. Other features, aspects and advantages of the subject matter described herein will become clear from the specific embodiments described below in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the present disclosure may be obtained when the following detailed description of the embodiments is considered in conjunction with the accompanying drawings. The same or similar reference numerals are used in the various drawings to represent the same or similar components. The accompanying drawings, together with the following detailed description, are included in and form a part of this specification and are used to illustrate embodiments of the present disclosure and to explain the principles and advantages of the present disclosure. Among them:

FIG. 1 shows an exemplary block diagram of a wireless communication system according to an embodiment of the present disclosure.

FIG. 2 shows an example of an antenna array and a signal processing architecture thereof for a base station according to an embodiment of the present disclosure.

3A and 3B illustrate examples of significant angular energy scattering effects.

4A to 4C illustrate example electronic devices in which a base station according to an embodiment of the present disclosure may be implemented.

FIG4D shows an example electronic device in which a terminal device according to an embodiment of the present disclosure can be implemented.

FIG. 5A illustrates example operations for performing distance measurement for a channel between a base station and a terminal device according to an embodiment of the present disclosure.

FIG. 5B illustrates an example of the granularity of channel distance measurements according to an embodiment of the disclosure.

FIG6 illustrates example operations for performing angle measurement for a channel between a base station and a terminal device according to an embodiment of the present disclosure.

FIG. 7 illustrates example operations for configuring and sending pilot signals according to an embodiment of the disclosure.

8A and 8B show examples of SRS resource configuration according to an embodiment of the present disclosure.

FIG. 9A shows an example of modeling a holographic MIMO antenna array according to an embodiment of the present disclosure.

FIG. 9B shows an example of an AI model according to an embodiment of the present disclosure.

FIG. 9C shows another example of an AI model for fine-grained distance measurement according to an embodiment of the present disclosure.

FIG. 9D shows an example of AI-based channel angle-distance joint measurement according to an embodiment of the present disclosure.

FIG. 10A shows an example of energy scattering effects after only angle measurement according to an embodiment of the present disclosure.

FIG. 10B shows an example of energy scattering effect after performing angle-distance joint measurement according to an embodiment of the present disclosure.

FIG. 11 shows a performance simulation analysis for an embodiment of the present disclosure.

12A to 12D illustrate example methods for communication according to an embodiment of the disclosure.

FIG. 13 shows an example block diagram of a computer that can be implemented as a terminal device or a base station according to an embodiment of the present disclosure.

FIG. 14 is a block diagram showing a first example of a schematic configuration of a gNB to which the technology of the present disclosure may be applied.

FIG. 15 is a block diagram showing a second example of a schematic configuration of a gNB to which the technology of the present disclosure may be applied.

FIG. 16 is a block diagram showing an example of a schematic configuration of a smartphone to which the technology of the present disclosure can be applied.

FIG. 17 is a block diagram showing an example of a schematic configuration of a car navigation device to which the technology of the present disclosure can be applied.

Although the embodiments described in the present disclosure may be susceptible to various modifications and alternative forms, specific embodiments thereof are shown as examples in the drawings and described in detail herein. However, it should be understood that the drawings and detailed description thereof are not intended to limit the embodiments to the particular forms disclosed, but on the contrary, the purpose is to cover all modifications, equivalents and alternatives within the spirit and scope of the claims.

DETAILED DESCRIPTION

Representative applications of various aspects such as the apparatus and method of the present disclosure are described below. The description of these examples is only to increase the context and help understand the described embodiments. Therefore, it is clear to those skilled in the art that the embodiments described below can be implemented without some or all of the specific details. In other cases, well-known process steps are not described in detail to avoid unnecessarily obscuring the described embodiments. Other applications are also possible, and the solutions of the present disclosure are not limited to these examples.

In general, all terms used herein will be interpreted according to their common meaning in the relevant technical field, unless different meanings and/or implications are clearly given in the context of use. Unless clearly otherwise specified, references to elements, devices, components, units and operations etc. are intended to be openly interpreted as at least one instance in elements, devices, components, units and operations. The operation of any method disclosed herein need not be performed in the precise order disclosed, unless the operation is clearly or implicitly described as after or before another operation. Any feature of any embodiment disclosed herein may be applied to any other appropriate embodiment. Similarly, any advantage of any embodiment may be applicable to any other embodiment, and vice versa. Other purposes, features and advantages of the embodiment will become clear from the following description.

Example Wireless Communication System

FIG1 shows an example block diagram of a wireless communication system according to an embodiment of the present disclosure. It should be noted that FIG1 shows only one of the various types and possible arrangements of wireless communication systems; the features of the present disclosure may be implemented in various systems as needed. Implemented in either.

As shown in Figure 1, the communication system 100 includes base stations 120A, 120B and terminal devices 110A, 110B to 110N. The base station and the terminal can be configured to communicate through uplink and downlink channels. The base stations 120A and 120B can be configured to communicate with a network 130 (e.g., a core network of a cellular service provider, a telecommunication network such as a public switched telephone network (PSTN), and/or the Internet). Therefore, the base stations 120A and 120B can facilitate communication between the terminal devices 110A to 110N and/or between the terminal devices 110A to 110N and the network 130.

1, the coverage area of base stations 120A, 120B may be referred to as a cell. Base stations 120A, 120B may operate according to one or more radio access network technologies to provide continuous or nearly continuous communication signal coverage to terminal devices 110A to 110N over a wide geographic area.

As shown in FIG1 , the communication system 100 includes a cloud 150 and a mobile edge computing node (MEC) 140. The cloud 150 can provide services such as IaaS, PaaS, and SaaS to terminal devices through a connection with a network 130. In the cloud 150 and the MEC 140, computing resources can be deployed to provide support for computing requirements of communication services (e.g., communication computing fusion services).

In the present disclosure, a base station may be a 5G NR base station or a 5G LTE-A base station, such as a gNB and an ng-eNB. A gNB may provide an NR user plane and control plane protocol terminated with a terminal device; an ng-eNB is a node defined for compatibility with a 4G LTE communication system, which may be an upgrade of an evolved Node B (eNB) of an LTE wireless access network, providing an Evolved Universal Terrestrial Radio Access (E-UTRA) user plane and control plane protocol terminated with a UE. In addition, examples of base stations may include, but are not limited to, the following: at least one of a base transceiver station (BTS) and a base station controller (BSC) in a GSM system; at least one of a radio network controller (RNC) and a Node B in a WCDMA system; an access point (AP) in a WLAN, WiMAX system; and corresponding network nodes in a communication system to be or being developed. Some of the functions of the base station herein may also be implemented as an entity having a control function for communication in D2D, M2M, and V2X scenarios, or as an entity that plays a spectrum coordination role in a cognitive radio communication scenario.

In the present disclosure, terminal devices may have the full breadth of their usual meanings, for example, terminal devices may be mobile stations (MS), user equipment (UE), etc. Terminal devices may be implemented as, for example, mobile phones, handheld devices, media players, computers, laptops, tablet computers, on-board units (OBU) or vehicles, road side units (RSU), wearable devices, Internet of Things (IoT) devices, or virtually any type of wireless device. In some cases, terminal devices may use multiple wireless For example, the terminal device may be configured to communicate using one or more of GSM, UMTS, CDMA2000, WiMAX, LTE, LTE-A, WLAN, NR, Bluetooth, etc.

Example Antenna Array and Signal Processing Architecture

Massive MIMO is a key technology for wireless communication systems such as 5G systems. Generally, a base station can be configured with an antenna array, which includes multiple antenna elements. Beamforming can be used to enhance the directivity of the signal and improve the spectrum efficiency. In wireless communication systems to be developed such as 6G systems, it is expected that larger-scale antenna arrays will be adopted to further improve the spectrum efficiency. Figure 2 shows an example of an antenna array for a base station and its signal processing architecture according to an embodiment of the present disclosure. As shown in Figure 2, the data stream is mapped to a corresponding RF chain via a digital precoder, and multiple RF chains are coupled to multiple antenna elements in the antenna array. Due to the large energy loss of the RF chain, a dedicated RF chain is generally not configured for each antenna element in the antenna array. As shown in Figure 2, the RF chain is generally partially connected to the antenna element, that is, each RF chain is connected to a subarray including a portion of the antenna elements. This is practical in terms of circuit configuration and system performance.

In the present disclosure, according to the distance between the antenna array of the base station and the terminal device, the radiation field of the antenna array can be divided into a near field and a far field with the Rayleigh distance as the boundary. The Rayleigh distance can be calculated by equation 1,

Equation 1:

Where D represents the antenna aperture, and λ represents the wavelength of the electromagnetic wave. It can be seen that the Rayleigh distance R is proportional to the square of the antenna aperture and inversely proportional to the wavelength (i.e., proportional to the carrier frequency). Therefore, as the antenna array scale increases and the communication frequency band increases (i.e., the wavelength of the electromagnetic wave becomes shorter), the Rayleigh distance may increase from a few meters that can be ignored in traditional systems to tens or hundreds of meters that cannot be ignored.

Within the Rayleigh distance, the electromagnetic radiation field is based on spherical waves (not plane waves). Accordingly, the channel state information between the base station and a specific terminal device will be related to the angle (or direction) and distance between the antenna array and the terminal device (rather than just the angle). Therefore, for near-field communication, the traditional far-field channel based on the plane wave assumption is no longer accurate. Figures 3A and 3B show examples of significant angular energy scattering effects.

Assume that there are two scatterers in the environment, and their horizontal significant angles are 0.15π and 0.25π respectively. In far-field communication, with the help of sparse representation in the angle domain (for example, based on the TYPE I codebook), the significant angles corresponding to these two scatterers can be clearly detected, as shown in Figure 3A. It should be understood that the TYPE I codebook is intended to determine near-optimal precoding for a single terminal device based on the assumption of far-field plane waves. Still considering the horizontal significant angles in the environment are 0.15π and For two scatterers of 0.25π, for near-field communication within the Rayleigh distance, the energy peaks of the significant angles of the two scatterers cannot be obtained by using the sparse representation of the angle domain. As shown in Figure 3B, the significant angular energy diffuses in multiple directions, presenting a chaotic scattering effect (i.e., energy scattering effect). It should be understood that within the near-field range, the mutual coupling of angle and distance is the root cause of energy scattering. The energy scattering effect will result in the inability to accurately obtain the significant angular direction of the scatterers in the environment, thereby failing to perform accurate channel measurement. Traditional channel measurement schemes, such as forming a TYPE I codebook based on the terminal device receiving a pilot signal, will not be applicable to channel measurement under near-field communication.

According to the scheme for channel measurement disclosed in the present invention, the angle-distance correlation of the near-field channel can be advantageously removed to achieve more accurate channel estimation. In an embodiment of the present invention, channel measurement can be used to assist channel estimation, and the channel measurement result can essentially reflect the result of channel estimation. According to the channel measurement disclosed in the present invention, it can include measuring the characteristics of one or more aspects of the channel. For example, in some embodiments, the channel measurement will include a distance measurement operation. Further considering the distance factor on the angle (or direction) of the channel between the base station and the terminal device can advantageously reduce the energy scattering effect under near-field communication and improve the accuracy of channel measurement. In some embodiments, the channel measurement will include angle measurement and distance measurement operations. The distance measurement can be performed based on the angle measurement, thereby reducing the pilot signal overhead of the channel measurement. In some embodiments, the uplink channel measurement is performed by receiving multiple pilot signals from the terminal device through the base station, and the state of the downlink channel is determined based on the channel reciprocity. In the case of more antenna elements, channel measurement by the base station can advantageously reduce the processing load of the terminal device. These and other advantages of the present invention will be described in conjunction with the description of the embodiments.

The scheme for channel measurement according to the present disclosure can be applied to near-field communication scenarios. As described above in conjunction with Equation 1, the Rayleigh distance R is proportional to the square of the antenna aperture and inversely proportional to the wavelength (ie, proportional to the carrier frequency). Therefore, as the size of the antenna array increases and the communication frequency band increases (ie, a shorter electromagnetic wave wavelength), the Rayleigh distance may increase to tens of meters or hundreds of meters. In the case where the Rayleigh distance is not negligible, channel distance measurement or channel angle-distance joint measurement according to an embodiment of the present disclosure may be performed, as described below with reference to Figures 5A and 6. Therefore, the embodiments of the present disclosure do not limit the type of antenna array, but can be applied to any type of antenna array that may form a spherical wave. Such antenna arrays include, for example, continuous aperture MIMO, tightly coupled arrays, leaky wave antennas, and the like.

As an example of an antenna array, holographic MIMO is an antenna constructed by integrating a large number of antenna elements onto a limited surface area. In holographic MIMO, a patch attached to a panel surface (such as a smart surface) is used to construct a holographic pattern, which records the interference information between the incident electromagnetic wave (also called the reference wave) generated by holographic MIMO and the target wave. When the reference wave propagates on the antenna surface, its radiation characteristics can be recorded by the holographic The pattern changes to produce the desired radiation pattern. Thanks to the programmability of metamaterials, holographic MIMO can be constructed based on printed circuit board technology to form ultra-thin and lightweight surface antennas. By controlling the electromagnetic response of the metamaterial, the holographic pattern and the corresponding beam direction can be reconfigured. Specifically, the electromagnetic wave can be controlled to transmit on the surface of the array composed of antenna elements to control the radiation amplitude of the reference wave to produce the desired beam based on the holographic pattern. In holographic MIMO, holographic beamforming can be achieved without the need for complex phase shifting circuits.

Example Electronic Devices

FIG. 4A shows an example electronic device that can implement a base station (e.g., 120A) according to an embodiment of the present disclosure. The electronic device 400A may include various units to implement various embodiments of channel measurement according to the present disclosure. In the example of FIG. 4A, the electronic device 400A includes a transceiver unit 402A and a measurement unit 404A. The various operations described below in conjunction with the base station or channel measurement can be implemented by units 402A to 404A of the electronic device 400A or other possible units.

In one embodiment, the transceiver unit 402A may be configured to receive multiple pilot signals from a terminal device (eg, 110A) and obtain multiple pilot signal samples via multiple radio frequency chains. The transceiver unit 402A may also be configured to control or perform operations related to signaling or message transceiving.

In one embodiment, the measuring unit 404A may be configured to perform a coarse-grained distance measurement of a channel between a base station and a terminal device based on a plurality of pilot signal samples. The measuring unit 404A may also be configured to perform a fine-grained distance estimation of the channel based on the coarse-grained distance measurement result to determine a transmission parameter for communication between the base station and the terminal device. In one embodiment, the fine-grained distance estimation of the channel is performed by an artificial intelligence model.

FIG. 4B shows another example electronic device that can implement a base station (e.g., 120A) according to an embodiment of the present disclosure. Electronic device 400B may include various units to implement various embodiments of channel measurement according to the present disclosure. In the example of FIG. 4B , electronic device 400B includes a transceiver unit 402B and a measurement unit 404B. The various operations described below in conjunction with base station or channel measurement may be implemented by units 402B to 404B of electronic device 400B or other possible units.

In one embodiment, the transceiver unit 402B may be configured to receive a first number of pilot signals from a terminal device (e.g., 110A) and receive a second number of pilot signals from the terminal device. The transceiver unit 402B may also be configured to control or perform operations related to signaling or message transceiving.

In one embodiment, the measuring unit 404B may be configured to perform angle measurement of a channel between the base station and the terminal device based on receiving a first number of pilot signals from the terminal device. The measuring unit 404B may be configured The measuring unit 404B may be configured to perform distance measurement for the channel at a specific angle based on receiving a second number of pilot signals from the terminal device and an angle measurement result for the channel. The measuring unit 404B may also be configured to determine a transmission parameter for communication between the base station and the terminal device based on the angle and distance measurement result for the channel.

FIG. 4C shows another example electronic device that can implement a base station (e.g., 120A) according to an embodiment of the present disclosure. Electronic device 400C may include various units to implement various embodiments of channel measurement according to the present disclosure. In the example of FIG. 4C, electronic device 400C includes a transceiver unit 402C and a pilot configuration unit 404C. The various operations described below in conjunction with base station or channel measurement can be implemented by units 402C to 404C of electronic device 400C or other possible units.

In one embodiment, the pilot configuration unit 404C may be configured to configure a first number of pilot signals for angle measurement of a channel between a base station and a terminal device (eg, 110A), and to configure a second number of pilot signals for distance measurement of the channel.

In one embodiment, the transceiver unit 402C may be configured to send signaling to the terminal device to configure the first number of pilot signals and the second number of pilot signals. For example, the first number of pilot signals may be indicated as angle measurements for channels between the base station and the terminal device through flag information, and the second number of pilot signals may be indicated as distance measurements for channels.

In an embodiment, the electronic devices 400A to 400C may be implemented at a chip level, or may be implemented at a device level by including other external components (eg, a radio frequency chain, an antenna, etc.) The electronic devices 400A to 400C may work as a communication device as a whole.

FIG. 4D shows an example electronic device that can implement a terminal device (e.g., 110A) according to an embodiment of the present disclosure. The electronic device 400D may include various units to facilitate implementation of various embodiments of channel measurement according to the present disclosure. In the example of FIG. 4D , the electronic device 400D includes a transceiver unit 402D and a control unit 404D. The various operations described below in conjunction with the terminal device or channel measurement may be implemented by units 402D to 404D of the electronic device 400D or other possible units.

In one embodiment, the transceiver unit 402D may be configured to receive a pilot signal configuration from the network. For example, the pilot signal configuration includes a first number of pilot signals and a second number of pilot signals, which are used for angle measurement and distance measurement of the channel, respectively. The control unit 404D may be configured to control the transceiver unit 402D to send the first number of pilot signals to the base station based on the received pilot signal configuration, so that the base station can perform the angle measurement and distance measurement between the base station and the terminal device. Once the base station performs the angle measurement, the transceiver unit 402D may also be configured to receive angle measurement information from the base station. Accordingly, the control unit 404D may be configured to send a second number of pilot signals to the base station based on the pilot signal configuration and the angle measurement information, so that the base station can perform distance measurement for the channel.

In an embodiment, the electronic device 400D may be implemented at a chip level, or may be implemented at a device level by including other external components (eg, a radio frequency chain, an antenna, etc.) The electronic device 400D may work as a communication device as a whole device.

It should be understood that the above-mentioned units are only logical modules divided according to the specific functions implemented by them, rather than being used to limit the specific implementation mode, for example, they can be implemented in software, hardware or a combination of software and hardware. In actual implementation, the above-mentioned units can be implemented as independent physical entities, or can also be implemented by a single entity (for example, a processor (CPU or DSP, etc.), an integrated circuit, etc.). Among them, the processing circuit can refer to various implementations of a digital circuit system, an analog circuit system or a mixed signal (a combination of analog and digital) circuit system that performs functions in a computing system. The processing circuit may include, for example, circuits such as integrated circuits (ICs), application specific integrated circuits (ASICs), parts or circuits of a separate processor core, the entire processor core, a separate processor, a programmable hardware device such as a field programmable gate array (FPGA), and/or a system including multiple processors.

Distance measurement for channels

5A shows an example operation for performing distance measurement for a channel between a base station and a terminal device according to an embodiment of the present disclosure. The example operation 500 may be performed between a base station 120 (or electronic devices 400A to 400C) and a terminal device 110 (or electronic device 400D). For example, the base station 120 may be any one of the base stations 120A and 120B in FIG. 1 , and the terminal device 110 may be any one of the terminal devices 110A to 110N in FIG. 1 .

As shown in FIG. 5A , at 502 , the terminal device 110 may send a certain number of pilot signals to the base station 120. For example, the certain number of pilot signals may include a plurality of pilot signals that are continuous in the time domain or a plurality of pilot signals within a certain time window. The certain number of pilot signals may be configured or activated by the network (e.g., the base station 120) through signaling. Accordingly, the base station 120 may receive the certain number of pilot signals from the terminal device 110 and obtain a plurality of pilot signal samples via a plurality of radio frequency chains. In one embodiment, the terminal device 110 may send pilot signals omnidirectionally or in multiple directions (e.g., by beam scanning). In one embodiment, the terminal device 110 may send pilot signals only in a direction that matches a specific angle (e.g., by beamforming), for example, when the angle measurement result of the channel between the base station 120 is known. This may advantageously reduce the number of pilot signals required for distance measurement.

As shown in FIG5A , at 504, the base station 120 may perform a distance measurement of a first granularity for a channel between the base station 120 and the terminal device 110 based on a plurality of pilot signal samples. In an embodiment, the distance measurement of the first granularity may be coarse-grained, which is related to the number of the plurality of pilot signal samples obtained. It should be noted that the number of the plurality of pilot signal samples may be based on the number of the plurality of pilot signals received by the base station 120 and the number of the plurality of RF chains of the base station 120 (e.g., based on the product of the two). When the number of pilot signals is sufficient, the distance measurement result at 504 may be sufficient to remove the angle-distance correlation of the near-field channel. Accordingly, the transmission parameters for communication between the base station 120 and the terminal device 110 may be determined based on the distance measurement result. For example, the transmission parameters may be used to configure the antenna array of the base station 120 to form a beam (e.g., including a downlink beam and an uplink beam) for communicating with the terminal device 110.

In order to further improve the accuracy of distance measurement, a second granularity distance estimation can be performed for the channel between the base station 120 and the terminal device 110 based on the coarse-grained distance measurement. In an embodiment, the distance estimation of the second granularity may be fine-grained. As shown in FIG. 5A, at 506, optionally, the base station 120 may perform a fine-grained distance estimation for the channel based on the coarse-grained distance measurement result to determine the transmission parameters for communication between the base station 120 and the terminal device 110. The fine-grained distance estimation may include extracting hidden features about the channel distance from the coarse-grained distance measurement result. In some embodiments, fine-grained distance estimation (e.g., assisting in extracting hidden features) may be performed by an artificial intelligence (AI) model, as described in detail below in this article. It should be understood that the distance measurement result at 506 can further remove the angle-distance correlation of the near-field channel. Accordingly, the transmission parameters for communication between the base station 120 and the terminal device 110 may be determined based on the distance measurement result. For example, the transmission parameters may be used to configure the antenna array of the base station 120 to form a beam (eg, including a downlink beam and an uplink beam) for communicating with the terminal device 110 .

In an embodiment of the present disclosure, the distance measurement for the channel between the base station 120 and the terminal device 110 can be further performed based on the angle measurement result of the channel. In one embodiment, the angle measurement can be similar to the channel measurement under far-field communication. For example, the angle measurement result may include or indicate one or more specific angles of the channel between the base station 120 and the terminal device 110. By causing the terminal device 110 to send a pilot signal at one or more specific angles, the base station 120 can perform the above-mentioned distance measurements 504 and 506 for the channel at one or more specific angles, instead of performing distance measurements at all possible angles. This can advantageously reduce the corresponding overhead of pilot transmission and reception and the base station 120 and the terminal device 110, while reducing the complexity of the base station 120 performing distance measurement and estimation.

It should be understood that the coarse granularity and fine granularity of the channel distance measurement in the present disclosure are relative concepts. The coarse granularity refers to the degree of removing the angle-distance correlation of the near-field channel. FIG. 5B shows an example of the granularity of the channel distance measurement according to an embodiment of the present disclosure. FIG. 5B shows the distance sampling points of two granularities sampled from the continuous distance, namely, S The invention provides a method for measuring the distance of a channel in a coarser granularity and a method for measuring the distance of a channel in a finer granularity. The method comprises: a coarser granularity distance sampling point and U finer granularity distance sampling points. In this example, the coarser granularity S distance sampling points may not meet the accuracy requirement for performing distance measurement on the channel. Accordingly, the distance sampling points need to be increased to U. In one embodiment, the distance sampling points can be increased by increasing the number of pilot signals sent from the terminal device 110 to the base station 120 at 502. Alternatively, the fine-grained distance estimation at 506 can be used to extract hidden features. By measuring or estimating the distance of the channel, the angle-distance correlation of the near-field channel can be advantageously removed, the energy scattering effect under near-field communication can be mitigated, and a more accurate channel measurement can be achieved.

Angle measurement for channels

FIG6 shows an example operation for performing angle measurement for a channel between a base station and a terminal device according to an embodiment of the present disclosure. The example operation 600 may be performed between a base station 120 (or electronic devices 400A to 400C) and a terminal device 110 (or electronic device 400D). For example, the base station 120 may be any one of the base stations 120A and 120B in FIG1 , and the terminal device 110 may be any one of the terminal devices 110A to 110N in FIG1 .

As shown in Figure 6, at 602, the terminal device 110 may send a certain number of pilot signals to the base station 120. For example, the certain number of pilot signals may include a plurality of pilot signals that are continuous in the time domain or a plurality of pilot signals within a certain time window. The certain number of pilot signals may be configured or activated by the network (e.g., the base station 120) through signaling. Accordingly, the base station 120 may receive the certain number of pilot signals from the terminal device 110 and obtain a plurality of pilot signal samples via a plurality of radio frequency chains. In an embodiment, the terminal device 110 may send a pilot signal omnidirectionally or in one or more directions (e.g., by beam scanning).

As shown in Figure 6, at 604, the base station 120 may perform an angle measurement of a first granularity for a channel between the base station 120 and the terminal device 110 based on multiple pilot signal samples. In an embodiment, the angle measurement of the first granularity may be coarse-grained, which is related to the number of multiple pilot signal samples obtained. It should be noted that the number of multiple pilot signal samples may be based on the number of multiple pilot signals received by the base station 120 and the number of multiple RF chains of the base station 120 (for example, based on the product of the two). When the number of pilot signals is sufficient, the angle measurement result at 604 may be sufficiently accurate to describe multiple alternative angles or directions of the channel between the base station 120 and the terminal device 110. Accordingly, the base station 120 may determine the preferred angle for communication between the base station 120 and the terminal device 110 as a specific angle from multiple alternative angles.

To further improve the accuracy of the angle measurement, a second granularity angle measurement may be performed on the channel between the base station 120 and the terminal device 110 based on the coarse granularity angle measurement. In an embodiment, the second granularity angle measurement may be fine granularity. As shown in FIG. 6 , at 606, optionally, the base station 120 may perform a second granularity angle measurement based on the coarse granularity angle measurement result. The result is that a fine-grained angle measurement for the channel is performed to more accurately describe multiple alternative angles or directions of the channel between the base station 120 and the terminal device 110. Fine-grained angle measurement may include extracting hidden features about the angle or direction of the channel from the coarse-grained angle measurement results. In some embodiments, fine-grained angle measurement may be performed by an AI model, as described in detail below in this document. Similarly, the base station 120 may determine a preferred angle for communication between the base station 120 and the terminal device 110 as a specific angle from multiple alternative angles. It should be understood that the coarse-grained and fine-grained angle measurements of the channel in the present disclosure are relative concepts. The coarseness of the granularity refers to the degree of accuracy of describing multiple alternative angles or directions of the channel between the base station 120 and the terminal device 110.

As shown in FIG6 , at 608, once the angle measurement for the channel between the base station 120 and the terminal device 110 is completed, the base station 120 may notify the terminal device 110 of the coarse-grained or fine-grained angle measurement result (e.g., including or indicating the determined specific angle) through signaling. In this way, the terminal device 110 may send a pilot signal based on the specific angle in the distance measurement described, for example, with reference to FIG5A , and perform angle-distance joint measurement of the channel. For example, the base station 120 may notify the terminal device 110 of the angle measurement result through downlink control information (DCI).

For the angle-distance joint measurement of the channel, first, the base station 120 may perform an angle measurement for the channel between the base station 120 and the terminal device 110 based on receiving a first number of pilot signals from the terminal device 110. After the angle measurement is completed, the base station 120 may perform a distance measurement for the channel at a specific angle based on receiving a second number of pilot signals from the terminal device 110 and the angle measurement result for the channel. After the distance measurement is completed, the base station 120 may determine the transmission parameters for communication between the base station 120 and the terminal device 110 based on the angle and distance measurement results for the channel. For example, the transmission parameters may be used to configure the antenna array of the base station 120 to form a beam (e.g., including a downlink beam and an uplink beam) for communicating with the terminal device 110.

Pilot signal configuration

FIG7 shows an example operation for configuring and sending a pilot signal according to an embodiment of the present disclosure. The example operation 500 may be performed between a base station 120 (or electronic devices 400A to 400C) and a terminal device 110 (or electronic device 400D). For example, the base station 120 may be any one of the base stations 120A and 120B in FIG1 , and the terminal device 110 may be any one of the terminal devices 110A to 110N in FIG1 .

As shown in FIG. 7 , at 702, the base station 120 may send a pilot signal configuration message to the terminal device 110 through signaling, so as to configure a pilot signal for at least one of distance measurement and angle measurement for a channel between the base station 120 and the terminal device 110. For example, through the pilot signal configuration message, a first number of pilot signals may be configured for the terminal device 110, and the first number of pilot signals may be indicated as being used for the channel between the base station 120 and the terminal device 110 through, for example, flag information. Additionally or alternatively, a second number of pilot signals may be configured for the terminal device 110, and the second number of pilot signals may be indicated as being used for distance measurement of a channel with the base station through, for example, flag information. In one embodiment, sending the pilot signal configuration message may include configuring the pilot signal through RRC signaling. In one embodiment, sending the pilot signal configuration message may include configuring the pilot signal through RRC signaling, and activating the pilot signal through DCI.

In an embodiment, the pilot signal may include a sounding reference signal (SRS) or a similar uplink reference signal used in a wireless communication system to be developed. Figures 8A and 8B show examples of SRS resource configurations according to an embodiment of the present disclosure. It should be understood that similar settings can be made for other types of pilot signals.

As shown in FIG8A, different pilot signal resource sets can be set through the srs-ResourceSetId field to correspond to and identify a certain number of pilot signals for angle measurement and distance measurement, respectively. The pilot signal resource can be specified as non-periodic, semi-periodic or periodic through the rousourceType field. The usage field can be used as flag information to indicate that the corresponding pilot signal resource is used for channel measurement. For example, the enumeration value of the usage field can be added, such as channelMeasurement as the flag information (only for example, other appropriate names can be used or even beamManagement or antennaSwitching in FIG8A can be used as flag information). For another example, two enumeration values of the usage field can be added, such as channelMeasurement1 for indicating the corresponding pilot signal resource as an angle measurement of the channel between the base station, and channelMeasurement2 for indicating the corresponding pilot signal resource as a distance measurement of the channel between the base station. As shown in FIG8B, different pilot signal resource sets can be set through the SRS-Resource field. The starting position, number of symbols, repetition factor, etc. of the pilot signal resource can be specified through the rousourceMapping field.

In one embodiment, the SRS resource sets for angle measurement and distance measurement can be configured through RRC signaling. In one embodiment, these SRS resource sets can be configured through RRC signaling, and the corresponding SRS resource sets can be activated through DCI. For example, the corresponding SRS resource set can be activated through the SRS-request field in the DCI. In one embodiment, the above angle measurement result can be sent to the terminal device together with the SRS-request field in the DCI signaling.

In an embodiment, different pilot signal resource sets may include multiple pilot signals that are continuous in the time domain, or multiple pilot signals within a certain time window. This enables the channel measurement result to more accurately reflect the channel quality within the corresponding time period. In an embodiment, the number of pilot signals used for angle measurement and distance measurement may include one of the following: 2, 4, 8, 10, 12, 14 and/or any other number of symbols.

As shown in FIG. 7 , at 704, after receiving the pilot signal configuration message from the terminal device 110, the terminal device 110 may send a pilot signal to the base station 120 based on a specific pilot signal configuration. Specifically, the terminal device 110 may send a first number of pilot signals to the base station 120 based on the pilot signal configuration, so that the base station 120 can perform angle measurement for the channel between the base station 120 and the terminal device 110. Alternatively or additionally, the terminal device 110 may send a second number of pilot signals to the base station 120 based on the pilot signal configuration, so that the base station 120 can perform distance measurement for the channel between the base station 120 and the terminal device 110. In the case where the angle measurement has been completed, for the distance measurement, the terminal device 110 may also send a pilot signal in a direction matching the specific angle or direction (e.g., through beamforming) based on the angle measurement information (e.g., a specific angle or direction of the channel).

Channel measurement based on AI model

Generally, the base station obtains pilot signal samples by receiving pilot signals from the terminal device, and thus performs channel measurement based on the pilot signal samples. The more pilot signal samples there are, the higher the accuracy of the channel measurement. As described with reference to the angle measurement and distance measurement in Figures 5A and 6, the number of pilot signal samples obtained is based on the number of pilot signals received by the base station and the number of RF chains configured by the base station (for example, based on the product of the two). For an antenna array with more antenna elements (which can be considered to be high-dimensional), the pilot signal samples obtained under a certain number of pilot signals and RF chains may be low-dimensional in terms of quantity. Accordingly, in an embodiment of the present disclosure, an AI model can be used to extract hidden features of the channel from low-dimensional pilot signal samples, thereby obtaining channel measurement information corresponding to high-dimensional antenna elements.

In an embodiment, after performing coarse-grained angle measurement or distance measurement based on receiving a pilot signal from a terminal device, the base station may perform fine-grained angle measurement or distance estimation for the channel between the base station and the terminal device through an AI model. For example, the AI model may be based on one of the following, namely a convolutional neural network (CNN), a fully connected neural network (FCN), an autoencoder (Autoencoder, such as a variational autoencoder), or a generative adversarial network (GAN). The following will take the CNN model as an example to describe an example of fine-grained channel measurement based on an AI model. The following examples will be described with reference to a holographic MIMO antenna array, but it should be understood that these examples may be similarly applicable to other types of antenna arrays.

FIG9A shows an example of modeling a holographic MIMO antenna array according to an embodiment of the present disclosure. In this example, the holographic MIMO antenna array is modeled as a uniform planar array (UPA), where the number of antenna elements is N = N _y × N _z . Assuming that N _RF feeds are provided on the UPA panel, each feed is connected to an RF chain, the number of RF chains is also N _RF . The horizontal and vertical spacings of the antenna elements on the UPA are defined as δ, and That is, the antenna elements are far apart Less than half a wavelength. Definition is a pair of azimuth and elevation angles, and r is the distance from the terminal device 110 to the center of the UPA (with the center of the UPA as the reference point). Define r _l as the distance from the center of the UPA to the lth scatterer (or terminal device 110) on the lth path (assuming there are L paths in the environment). In particular, l = 0 corresponds to the distance from the terminal device 110 to the center of the UPA. Definition is the distance from the ( _ny , _nz )th antenna element on the UPA to the lth scatterer (or terminal device 110) on the lth path. In particular, l = 0 corresponds to the distance from the terminal device 110 to the ( _ny , _nz )th antenna element on the UPA. It can be defined as are the azimuth and elevation angles corresponding to the lth path.

Based on the UPA model shown in Figure 9, the corresponding channel model and transmission signal model can be obtained. Specifically, within the Rayleigh distance, the spherical wave-based channel from the terminal device 110 to the holographic MIMO antenna array of the base station 120, for example, can be expressed as:

Where β _l is the complex gain of the lth path, and the steering vector It can be expressed as:

where the definition is the additional distance difference that the electromagnetic wave needs to travel to reach the ( _ny , _nz )th antenna element compared to the UPA center, where Indicates the wave number.

Next, the transmission signal model of the holographic MIMO antenna array is given. Specifically, let x = [x ₁ , ..., x _P ] represent the pilot signal sent by the terminal device 110, where P is the number of pilot signals. For any pilot signal p, the uplink signal received at the base station 120 It is expressed as:
y _p ＝F _p D _p hx _p +F _p n _p ,p＝1,…,P

in, represents the receiving matrix at the base station 120, is the holographic pattern of the holographic MIMO antenna array, is the spherical wave-based channel model defined above, is complex Gaussian noise. Define W _p =F _p D _p as the codeword of the holographic MIMO antenna array to be designed. For P pilot signals, the aggregated uplink signal received at the base station 120 is It is expressed as:
y＝Wh+n

Among them, the codeword matrix

It can be seen that each pilot signal p can generate N _RF signal samples y _p , and P pilot signals will generate PN _RF signal samples. Since decoding each signal sample requires its corresponding codeword, PN _RF codewords are required. It is easy to understand that the operation of determining the codeword will be complicated in the presence of a large number of antenna elements. Therefore, it is more practical to perform uplink channel measurement by a base station with stronger processing capabilities. Based on channel reciprocity, downlink channel information can be obtained.

In order to determine the codeword from the signal sample y received from the base station 120, for the UPA of _Ny × _Nz dimensions, the holographic MIMO near-field codebook to be designed can be expressed as:

Among them, angle and distance sampling must meet the following requirements:

Among them, N _y is the number of sampling points of θ, N _z is The number of sampling points is , and S is the number of sampling points at distance r. Therefore, the holographic MIMO near-field codebook The number of codewords included is at least N _y N _z S, and each codeword is a vector of dimension N×1. At the base station 120, only the low-dimensional received signal vector is known. Its dimension is based on the number of pilots P and the number of RF chains N _RF . Therefore, according to the channel measurement scheme of the embodiment of the present disclosure, it is possible to obtain a low-dimensional vector based on the available Design of high-dimensional codebooks for holographic MIMO antenna arrays.

Next, a fine-grained angle measurement scheme based on an AI model for a channel is introduced with reference to the above-mentioned channel model and transmission signal model. FIG9B shows an example of an AI model according to an embodiment of the present disclosure. In this example, the AI model is based on a CNN model. As shown in FIG9B , CNN model I includes four modules, namely, an input module, a preprocessing module, a convolution module, and an output module.

In one embodiment, the input module may be configured to input the pilot signal samples obtained by the base station 120 by receiving P pilot signals. As a vector input to CNN. For the purpose of simplicity, let M = PN _RF , which can represent the number of coarse-grained pilot signal samples used for angle measurement.

In one embodiment, the preprocessing module can be configured to normalize the signal vector. Since the signal y is a complex number with a large dynamic range, normalizing it can facilitate the processing of the subsequent CNN modules. The normalization operation can be expressed as:

Where y ^Norm represents the normalized signal, which is decomposed into the real part Re{y ^Norm } and the imaginary part Im{y ^Norm } and fed into the convolution module.

In one embodiment, the convolution module can be configured to use multiple convolution layers to extract hidden features of the channel from the normalized signal y ^Norm . Each convolution layer is followed by a rectified linear unit (ReLU) activation layer to provide nonlinear fitting capabilities. In order to avoid model complexity, a pooling layer is introduced after the last ReLU activation layer to downsample each feature channel to a scalar. In the CNN model I of Figure 9B, two convolution layers, two ReLU activation layers and one pooling layer are used. Let _fi and f _o represent the number of input feature channels and output feature channels respectively, and Table 1 lists the design parameters of the CNN model.

Table 1 Design parameters of CNN model I for angle measurement

It should be understood that at the input module, the input feature channels generally correspond to different characteristics of the original data (or training set). In an embodiment of the present disclosure, the input feature channels of the input module are used to capture the real and imaginary information (equivalent to two characteristics) of the pilot signal samples received at the base station 120, so _fi takes a value of 2. The number of output channels of each layer mainly depends on the specific tasks of the model and the characteristics of the input data. Generally, as the depth of the network increases (i.e., the number of convolutional layers increases), the number of output channels of each layer may increase. This is, for example, because deeper convolutional layers are generally expected to capture more complex features, and increasing the number of channels can provide more feature representation space. In an embodiment, the above variables P (i.e., the number of pilot signals sent by the terminal device), N (i.e., the number of antenna elements of the antenna array configured by the base station), and N _RF (i.e., the number of RF chains configured by the base station) reflect the complexity of signal processing to a certain extent, and can be used as a design. The reference standard for CNN models.

In one embodiment, the output module can be configured to output an N-dimensional vector This vector includes the probability of the channel at each angle sampling point At this point, the output module can provide probability information corresponding to the fine-grained angle sampling points. In an embodiment, a fully connected layer can be further introduced after the pooling layer to extract one or more candidate angles from the angle sampling points. A specific angle can be selected from the one or more candidate angles as an input for the distance measurement of the channel. As an example, the optimal angle can be expressed as:

In an embodiment of the present disclosure, during the CNN model I training phase, a cross-entropy loss function may be used as an evaluation metric for the classification task, which may be expressed as:

in Indicates the optimal angle of the channel corresponding to the nth angle sampling point; otherwise The CNN model I can be pre-trained and pre-configured to the base station, or trained and configured on-site at the base station. Accordingly, the CNN model I can be trained based on randomly generated channel data or on-site data at the base station.

FIG10A shows an example of energy scattering effect after only angle measurement according to an embodiment of the present disclosure. As shown in FIG10A , due to the coupling between the angle and distance of the near-field channel, the energy scattering effect still exists in the channel measurement result based only on the angle. Therefore, the distance factor needs to be considered in the channel measurement.

Next, the fine-grained distance measurement scheme based on the AI model for the channel is introduced with reference to the above-mentioned channel model and transmission signal model. As an option, an AI model similar to the CNN model I in FIG. 9B can be used for fine-grained distance measurement. For example, the input module can be configured to receive the pilot signal samples obtained by the base station 120 by receiving S pilot signals. As a vector input to the CNN; the output module can be configured to output a U-dimensional vector This vector includes the probability of the channel at each distance sampling point In this way, the output module can provide probability information corresponding to the fine-grained distance measurement sampling points.

As another option, an AI model may be designed so that the base station 120 can perform distance measurement at a specific angle obtained by angle measurement (i.e., angle-distance joint measurement). For example, based on the probability information obtained in the angle measurement, the optimal angle direction of the channel may be determined (e.g., by the corresponding antenna element index The result is used as part of the distance measurement input to improve the efficiency and performance of the distance measurement. The coarse-grained distance sampling criteria are given as follows:

in is a constant used to characterize the angle-distance correlation of the near-field channel. It should be noted that the coarse-grained distance sampling points only describe the optimal angle The maximum value of two distance samples that are not related in direction is a coarser distance sampling point. Since the distance is a continuous value, such coarse-grained sampling may not meet the accuracy of channel measurement, so a fine-grained distance measurement method is required.

Figure 9C shows another example of an AI model for fine-grained distance measurement according to an embodiment of the present disclosure. In this example, the AI model is also based on a CNN model. As shown in Figure 9C, the CNN model II includes five modules, namely, an input module, an attention module, a preprocessing module, a convolution module, and an output module.

As shown in FIG9C , the input of the distance measurement includes two types of data. One type of data is the probability information obtained by angle measurement, which is an N-dimensional vector, namely Another type of data is the pilot signal samples of the coarse-grained distance measurement, which are the pilot signal samples obtained by the base station 120 by receiving S pilot signals, that is, SN _RF may represent the number of coarse-grained pilot signal samples used for distance measurement at the optimal angle. Accordingly, in one embodiment, the input module may be configured to convert the above probability information and the pilot signal samples into Input to CNN as a vector.

It should be noted that the above probability information and pilot signal samples have different importance for fine-grained distance measurement. In order to fuse the features of two types of heterogeneous data, an attention module is introduced in CNN model II, which can be configured to give different weights to the features of the two types of data for effective fusion.

In one embodiment, the preprocessing module can be configured to normalize the signal vector. Since the signal y is a complex number with a large dynamic range, normalizing it can facilitate the processing of the subsequent CNN modules. The normalization operation can be expressed as:

Where y ^II,Norm represents the normalized signal, which is decomposed into the real part Re{y ^II,Norm } and the imaginary part Im{y ^II,Norm } and fed into the convolution module.

In one embodiment, the convolution module can be configured to use multiple convolution layers to extract the normalized signal y ^{II, Norm} Hidden features are extracted from the network. Each convolutional layer is followed by a ReLU activation layer to provide nonlinear fitting capabilities. In order to avoid complicating the model, a pooling layer is introduced after the last ReLU activation layer to downsample each feature channel to a scalar. In the CNN model II of Figure 9C, two convolutional layers, two ReLU activation layers and one pooling layer are used. Similarly, let _fi and f _o represent the number of input feature channels and output feature channels, respectively. Table 2 lists the design parameters of the CNN model.

Table 2 Design parameters of CNN model II for distance measurement

It should be understood that the input feature channel of the input module is used to capture the real and imaginary information of the pilot signal samples received at the base station 120 (equivalent to two characteristics), so _fi takes a value of 2. The number of output channels in each layer mainly depends on the specific tasks of the model and the characteristics of the input data. In an embodiment, the above variables S (i.e., the number of pilot signals sent by the terminal device), U (i.e., the number of fine-grained distance sampling points), and N _RF (i.e., the number of RF chains configured by the base station) reflect the complexity of signal processing to a certain extent, and can be used as a reference standard for designing a CNN model.

In one embodiment, the output module may be configured to output a U-dimensional vector This vector includes the probability of the channel at each fine-grained distance sampling point At this point, the output module can provide probability information corresponding to the fine-grained distance sampling points. In an embodiment, a fully connected layer can be introduced after the pooling layer to extract the optimal distance from the distance sampling points, which can be expressed as:

In an embodiment of the present disclosure, during the CNN model II training phase, a cross entropy loss function may be used as an evaluation metric for the classification task, which may be expressed as:

in Indicates the optimal distance of the channel corresponding to the u-th distance sampling point; otherwise The CNN model II may be pre-trained and pre-configured to the base station, or may be trained and configured on-site at the base station. Accordingly, the CNN model II may be trained based on randomly generated channel data or on-site data at the base station.

Based on the beam training of CNN model II, the codebook of the near-field channel of the holographic MIMO antenna array can be obtained:

The optimal angle of the channel is determined by angle measurement, and the optimal distance of the channel is determined by distance measurement. Both specify the measurement results of the near-field channel of the holographic MIMO antenna array. Figure 9D shows an example of AI-based channel angle-distance joint measurement according to an embodiment of the present disclosure. As shown in Figure 9D, two CNN models are used to process heterogeneous data to extract the main features from the signals received from the base station, thereby promoting efficient channel measurement.

It should be noted that although the probability information obtained by fine-grained angle measurement is used as the input of distance measurement in the description of CNN model II, as an alternative, the probability information of the channel at the angle sampling point obtained by coarse-grained angle measurement can also be used as the input of distance measurement. In such an embodiment, CNN model II can also perform fine-grained distance measurement based on the coarse-grained angle measurement results.

Fig. 10B shows an example of energy scattering effect after performing angle-distance joint measurement according to an embodiment of the present disclosure. As shown in Fig. 10B, the distance measurement advantageously removes the coupling between the angle and distance of the near-field channel.

In the embodiments of the present disclosure, in addition to the CNN model, the AI model can also be based on other types of models, such as a fully connected neural network (FCN), an autoencoder (such as a variational autoencoder) or a generative adversarial network (GAN). It should be understood that the corresponding model will be designed to have the same type of input and output as the above-mentioned CNN model. Taking angle measurement as an example, the input of each model is a low-dimensional vector The output is an N-dimensional vector This vector contains the probability of the channel at each angle sampling point For each model, a loss function such as mean squared error (MSE) or cross entropy can be adopted during the training phase, and an optimization algorithm such as gradient descent is used to adjust the parameters of the network to minimize the loss function.

For example, an FCN model may include an input module, a fully connected module, and an output module. A fully connected module may include multiple fully connected layers, each of which may include a linear transformation and a nonlinear activation function. Multiple fully connected layers may be added as needed to increase the depth of the network. The output dimension of each fully connected layer may be set according to actual needs, and the output dimension of the last fully connected layer should be set to N.

An autoencoder is an unsupervised learning network, and is mainly composed of two parts: an encoder and a decoder. The encoder reduces the input data to a lower dimension, and the decoder restores the low-dimensional data to the original high dimension. In the embodiment of the present disclosure, only the decoder part can be used for angle measurement, and the input is a vector The decoder outputs an N×1 probability vector. For example, an autoencoder-based model may include an input module, a decoder module, and an output module. The decoder module may include one or more fully connected layers. The number and Size to suit actual needs.

A model based on a generative adversarial network (GAN) may include an input module, a GAN module (including a generator and a discriminator, which is composed of a convolutional network or a fully connected network) and an output module. Generally, a GAN module is composed of a generator and a discriminator. The generator is used to generate data starting from a random noise vector, and the discriminator is used to distinguish the generated data from the real data. The generator and the discriminator can both be composed of a convolutional network or a fully connected network. In an embodiment of the present disclosure, for angle measurement, the generator converts the input vector And generate an N×1 probability vector.

A model based on a variational autoencoder (VAE) may include an input module, a VAE module (including an encoder and a decoder), and an output module. The VAE module is a variant of an autoencoder that introduces randomness so that the model can better generate new data. The VAE module may consist of an encoder and a decoder. Generally, the encoder maps the input data to latent variables, and the decoder maps the latent variables to output data. In the embodiment of the present disclosure, for angle measurement, As a latent variable, the training model generates an N×1 probability vector.

Performance Analysis

Combined with Table 3, we can understand the advantages of AI model-based channel measurement in saving pilot signal resources. Due to the feature extraction capability of the AI model, compared with the traditional uniform quantization scheme for building the TYPE I codebook, the AI model saves pilot signals in both angle measurement and distance measurement. Taking distance measurement as an example, the AI model can achieve U distance sampling points based on S pilot signals (refer to Figure 5B to understand the values of S and U).

Table 3 Comparison of pilot signal overhead

FIG11 shows a performance simulation analysis for an embodiment of the present disclosure. Specific simulation parameters are shown in Table 4. FIG11 depicts the variation trend of the achievable rate with the distance between the base station and the terminal device, where the achievable rate is expressed as:

It can be seen that the angle-distance joint measurement scheme disclosed in the present invention has a significantly improved achievable rate compared to the traditional scheme of the TYPE I codebook. Considering the advantages of the angle-distance joint measurement scheme disclosed in the present invention in terms of pilot signal saving, its performance gap with perfect CSI is acceptable.

Table 4 Simulation parameter settings

Example Method

Figure 12A shows a first example method for communication according to an embodiment of the present disclosure. The method may be performed by a base station (e.g., 120) or an electronic device 400A to 400C. As shown in Figure 12A, the method 1200A may include performing angle measurement (box 1202A) for a channel between a base station and a terminal device based on receiving a first number of pilot signals from a terminal device (e.g., 110). The method may include performing distance measurement (box 1204A) for a channel at a specific angle based on receiving a second number of pilot signals from a terminal device and an angle measurement result for the channel. The method may also include determining a transmission parameter (box 1206A) for communication between a base station and a terminal device based on an angle and a distance measurement result for the channel. Further details of the method may be understood with reference to the description of the base station or electronic devices 400A to 400C above.

In one embodiment, receiving the first number of pilot signals from the terminal device includes obtaining a third number of pilot signal samples via a plurality of RF chains. The third number of pilot signal samples is based on the first number of pilot signals and the plurality of RF chains.

In one embodiment, performing angle measurement for the channel includes: obtaining multiple candidate angles of the channel based on a third number of pilot signal samples; and determining a preferred angle for communication between the base station and the terminal device as a specific angle from the multiple candidate angles.

In one embodiment, receiving a second number of pilot signals from the terminal device includes obtaining a fourth number of pilot signal samples via a plurality of RF chains. The fourth number of pilot signal samples is based on the second number of pilot signals and the plurality of RF chains.

In one embodiment, performing distance measurement for a channel includes: performing coarse-grained distance measurement for the channel at a specific angle based on a fourth number of pilot signal samples; and performing fine-grained distance estimation for the channel based on the angle measurement of the channel and the coarse-grained distance measurement results to determine transmission parameters for communication between the base station and the terminal device.

In one embodiment, fine-grained distance estimation for a channel is performed by an AI model. In one embodiment, a specific angle is determined by an AI model. The AI model is based on one of: a convolutional neural network; a fully connected neural network; an autoencoder, including a variational autoencoder; or a generative adversarial network.

In one embodiment, the AI model is pre-trained and pre-configured to the base station, or is trained and configured on-site at the base station. The AI model is trained based on randomly generated channel data or on-site data at the base station.

In one embodiment, the first number of pilot signals and the second number of pilot signals respectively include: a plurality of pilot signals continuous in the time domain or a plurality of pilot signals within a certain time window. The first number and the second number respectively include one of the following: 2, 4, 8, 10, 12 or 14. The pilot signal may be a sounding reference signal SRS.

In one embodiment, the method further comprises: configuring the first number of pilot signals and the second number of pilot signals to the terminal device through signaling; and upon completing the angle measurement for the channel, notifying the terminal device of an indication of a specific angle through signaling.

In one embodiment, the antenna array is configured to implement holographic MIMO.

Figure 12B shows a second example method for communication according to an embodiment of the present disclosure. The method may be performed by a base station (e.g., 120) or an electronic device 400A to 400C. As shown in Figure 12B, the method 1200B may include obtaining multiple pilot signal samples (box 1202B) via multiple RF chains based on receiving multiple pilot signals from a terminal device (e.g., 110). The method may include performing a coarse-grained distance measurement for a channel between the base station and the terminal device based on multiple pilot signal samples (box 1204B). The method may also include performing a fine-grained distance estimation for the channel (e.g., by an AI model) based on the coarse-grained distance measurement result to determine the transmission parameters (box 1206B) for communication between the base station and the terminal device. Further details of the method may be understood with reference to the description of the base station or electronic devices 400A to 400C above.

In one embodiment, distance measurement for the channel is further performed based on the angle measurement result of the channel.

In one embodiment, the number of the plurality of pilot signal samples is based on the number of the plurality of pilot signals and the number of RF chains. The number of

Figure 12C shows a third example method for communication according to an embodiment of the present disclosure. The method may be performed by a base station (e.g., 120) or an electronic device 400A to 400C. As shown in Figure 12C, the method 1200C may include configuring a first number of pilot signals (box 1202C) to a terminal device (e.g., 110) by signaling. The first number of pilot signals may be indicated as angle measurements for channels between a base station and a terminal device by flag information. The method may also include configuring a second number of pilot signals (box 1204C) to the terminal device by signaling. The second number of pilot signals may be indicated as distance measurements for channels by flag information. Further details of the method may be understood with reference to the description of the base station or electronic devices 400A to 400C above.

In one embodiment, the first number of pilot signals and the second number of pilot signals may be configured to the terminal device through the same signaling.

In one embodiment, the first number of pilot signals and the second number of pilot signals respectively include a plurality of pilot signals that are continuous in the time domain, or a plurality of pilot signals within a certain time window. The first number and the second number respectively include one of the following: 2, 4, 8, 10, 12 or 14. The pilot signal may be a sounding reference signal SRS.

Figure 12D shows a fourth example method for communication according to an embodiment of the present disclosure. The method may be performed by a terminal device (e.g., 110) or an electronic device 400D. As shown in Figure 12D, the method 1200D may include receiving a pilot signal configuration from a network (e.g., a base station 120), the pilot signal configuration including a first number of pilot signals and a second number of pilot signals (box 1202D). The method may include sending a first number of pilot signals to a base station (e.g., 120) based on the pilot signal configuration, for the base station to perform an angle measurement for a channel between the base station and the terminal device (box 1204D). The method may also include receiving angle measurement information from the base station (box 1206D), and sending a second number of pilot signals to the base station based on the pilot signal configuration and the angle measurement information, for the base station to perform a distance measurement for the channel (box 1208D). Further details of the method may be understood with reference to the description of the terminal device or electronic device 400D above.

In one embodiment, the angle measurement information indicates a preferred specific angle between the base station and the terminal device.Sending the second number of pilot signals to the base station may include sending the second number of pilot signals to the base station in a direction matching the specific angle.

The above describes the exemplary electronic devices and methods according to the embodiments of the present disclosure. It should be understood that the operations or functions of these electronic devices can be combined with each other to achieve more or less operations or functions than described. The operation steps of each method can also be combined with each other in any appropriate order to similarly achieve more or less operations or functions than described. Fewer operations.

It should be understood that the machine executable instructions in the machine readable storage medium or program product according to the embodiments of the present disclosure can be configured to perform operations corresponding to the above-mentioned device and method embodiments. When referring to the above-mentioned device and method embodiments, the embodiments of the machine readable storage medium or program product are clear to those skilled in the art, so they are not described repeatedly. Machine readable storage media and program products for carrying or including the above-mentioned machine executable instructions also fall within the scope of the present disclosure. Such storage media may include, but are not limited to, floppy disks, optical disks, magneto-optical disks, memory cards, memory sticks, and the like. In addition, it should be understood that the above-mentioned series of processes and devices may also be implemented by software and/or firmware.

In addition, it should be understood that the above series of processes and devices can also be implemented by software and/or firmware. In the case of implementation by software and/or firmware, the program constituting the software is installed from a storage medium or a network to a computer with a dedicated hardware structure, such as a general-purpose computer 1300 shown in FIG. 13, and the computer can perform various functions when various programs are installed. FIG. 13 shows an example block diagram of a computer that can be implemented as a terminal device or a base station according to an embodiment of the present disclosure.

In FIG13 , a central processing unit (CPU) 1301 performs various processes according to a program stored in a read-only memory (ROM) 1302 or a program loaded from a storage section 1308 to a random access memory (RAM) 1303. In the RAM 1303, data required when the CPU 1301 performs various processes, etc., is also stored as needed.

The CPU 1301, the ROM 1302, and the RAM 1303 are connected to each other via a bus 1304. An input/output interface 1305 is also connected to the bus 1304.

The following components are connected to the input/output interface 1305: an input section 1306 including a keyboard, a mouse, etc.; an output section 1307 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1308 including a hard disk, etc.; and a communication section 1309 including a network interface card such as a LAN card, a modem, etc. The communication section 1309 performs communication processing via a network such as the Internet.

A drive 1310 is also connected to the input/output interface 1305 as needed. A removable medium 1311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 1310 as needed so that a computer program read therefrom is installed into the storage section 1308 as needed.

In the case where the above-described series of processing is realized by software, a program constituting the software is installed from a network such as the Internet or a storage medium such as the removable medium 1311 .

Those skilled in the art will appreciate that such storage media are not limited to the removable media 1311 shown in FIG. 13 in which the program is stored and distributed separately from the device to provide the program to the user. Examples of the removable media 1311 include Magnetic disks (including floppy disks (registered trademark)), optical disks (including compact disk read only memory (CD-ROM) and digital versatile disks (DVD)), magneto-optical disks (including minidiscs (MD) (registered trademark)), and semiconductor memories. Alternatively, the storage medium may be a ROM 1302, a hard disk included in the storage section 1308, or the like, in which the programs are stored and distributed to users together with the devices containing them.

An application example according to the present disclosure will be described below with reference to FIGS. 14 to 17 .

Application examples for base stations

First application example

FIG14 is a block diagram showing a first example of a schematic configuration of a gNB to which the technology of the present disclosure can be applied. The gNB 1400 includes a plurality of antennas 1410 and a base station device 1420. The base station device 1420 and each antenna 1410 can be connected to each other via an RF cable. In one implementation, the gNB 1400 (or base station device 1420) herein can correspond to the electronic device 300A described above.

Each of the antennas 1410 includes a single or multiple antenna elements (such as multiple antenna elements included in a multiple-input multiple-output (MIMO) antenna) and is used for base station device 1420 to transmit and receive wireless signals. As shown in FIG. 14 , gNB 1400 may include multiple antennas 1410. For example, the multiple antennas 1410 may be compatible with multiple frequency bands used by gNB 1400.

The base station device 1420 includes a controller 1421 , a memory 1422 , a network interface 1423 , and a wireless communication interface 1425 .

The controller 1421 may be, for example, a CPU or a DSP, and operates various functions of the higher layers of the base station device 1420. For example, the controller 1421 generates a data packet based on the data in the signal processed by the wireless communication interface 1425, and transmits the generated packet via the network interface 1423. The controller 1421 may bundle data from a plurality of baseband processors to generate a bundled packet, and transmit the generated bundled packet. The controller 1421 may have a logical function to perform the following control: the control may be such as radio resource control, radio bearer control, mobility management, admission control, and scheduling. The control may be performed in conjunction with a nearby gNB or core network node. The memory 1422 includes a RAM and a ROM, and stores programs executed by the controller 1421 and various types of control data (such as a terminal list, transmission power data, and scheduling data).

The network interface 1423 is a communication interface for connecting the base station device 1420 to the core network 1424. The controller 1421 can communicate with the core network node or another gNB via the network interface 1423. In this case, the gNB 1400 The core network node or other gNB can be connected to each other through a logical interface (such as an S1 interface and an X2 interface). The network interface 1423 can also be a wired communication interface or a wireless communication interface for a wireless backhaul line. If the network interface 1423 is a wireless communication interface, the network interface 1423 can use a higher frequency band for wireless communication than the frequency band used by the wireless communication interface 1425.

The wireless communication interface 1425 supports any cellular communication scheme such as Long Term Evolution (LTE) and LTE-Advanced, and provides wireless connection to a terminal located in a cell of the gNB 1400 via the antenna 1410. The wireless communication interface 1425 may generally include, for example, a baseband (BB) processor 1426 and an RF circuit 1427. The BB processor 1426 may perform, for example, encoding/decoding, modulation/demodulation, and multiplexing/demultiplexing, and perform various types of signal processing of layers such as L1, medium access control (MAC), radio link control (RLC), and packet data convergence protocol (PDCP). Instead of the controller 1421, the BB processor 1426 may have a part or all of the above-mentioned logical functions. The BB processor 1426 may be a memory storing a communication control program, or a module including a processor configured to execute a program and related circuits. Updating the program may change the function of the BB processor 1426. The module may be a card or a blade inserted into a slot of the base station device 1420. Alternatively, the module may also be a chip mounted on a card or a blade. Meanwhile, the RF circuit 1427 may include, for example, a mixer, a filter, and an amplifier, and transmit and receive wireless signals via the antenna 1410. Although FIG. 14 shows an example in which one RF circuit 1427 is connected to one antenna 1410, the present disclosure is not limited to this illustration, but one RF circuit 1427 may be connected to multiple antennas 1410 at the same time.

As shown in FIG. 14 , the wireless communication interface 1425 may include multiple BB processors 1426. For example, the multiple BB processors 1426 may be compatible with multiple frequency bands used by the gNB 1400. As shown in FIG. 14 , the wireless communication interface 1425 may include multiple RF circuits 1427. For example, the multiple RF circuits 1427 may be compatible with multiple antenna elements. Although FIG. 14 shows an example in which the wireless communication interface 1425 includes multiple BB processors 1426 and multiple RF circuits 1427, the wireless communication interface 1425 may also include a single BB processor 1426 or a single RF circuit 1427.

Second application example

15 is a block diagram showing a second example of a schematic configuration of a gNB to which the technology of the present disclosure can be applied. The gNB 1530 includes a plurality of antennas 1540, a base station device 1550, and an RRH 1560. The RRH 1560 and each antenna 1540 can be connected to each other via an RF cable. The base station device 1550 and the RRH 1560 can be connected to each other via a high-speed line such as an optical fiber cable. In one implementation, the gNB 1530 (or base station device 1550) herein may correspond to the electronic device 300A described above.

Each of antennas 1540 includes a single or multiple antenna elements (such as those included in a MIMO antenna). The gNB 1530 may include multiple antennas 1540. For example, the multiple antennas 1540 may be compatible with multiple frequency bands used by the gNB 1530.

Base station device 1550 includes controller 1551, memory 1552, network interface 1553, wireless communication interface 1555, and connection interface 1557. Controller 1551, memory 1552, and network interface 1553 are the same as controller 1421, memory 1422, and network interface 1423 described with reference to FIG.

The wireless communication interface 1555 supports any cellular communication scheme (such as LTE and LTE-Advanced), and provides wireless communication to terminals located in a sector corresponding to the RRH 1560 via the RRH 1560 and the antenna 1540. The wireless communication interface 1555 may generally include, for example, a BB processor 1556. The BB processor 1556 is the same as the BB processor 1426 described with reference to FIG. 14, except that the BB processor 1556 is connected to the RF circuit 1564 of the RRH 1560 via the connection interface 1557. As shown in FIG. 15, the wireless communication interface 1555 may include a plurality of BB processors 1556. For example, the plurality of BB processors 1556 may be compatible with a plurality of frequency bands used by the gNB 1530. Although FIG. 15 shows an example in which the wireless communication interface 1555 includes a plurality of BB processors 1556, the wireless communication interface 1555 may also include a single BB processor 1556.

The connection interface 1557 is an interface for connecting the base station device 1550 (wireless communication interface 1555) to the RRH 1560. The connection interface 1557 can also be a communication module for connecting the base station device 1550 (wireless communication interface 1555) to the communication in the above-mentioned high-speed line of the RRH 1560.

RRH 1560 includes a connection interface 1561 and a wireless communication interface 1563.

The connection interface 1561 is an interface for connecting the RRH 1560 (wireless communication interface 1563) to the base station device 1550. The connection interface 1561 can also be a communication module for communication in the above-mentioned high-speed line.

The wireless communication interface 1563 transmits and receives wireless signals via the antenna 1540. The wireless communication interface 1563 may generally include, for example, an RF circuit 1564. The RF circuit 1564 may include, for example, a mixer, a filter, and an amplifier, and transmits and receives wireless signals via the antenna 1540. Although FIG. 15 shows an example in which one RF circuit 1564 is connected to one antenna 1540, the present disclosure is not limited to this illustration, but one RF circuit 1564 may be connected to multiple antennas 1540 at the same time.

As shown in FIG15 , the wireless communication interface 1563 may include multiple RF circuits 1564. For example, multiple RF circuits 1564 may support multiple antenna elements. Although FIG15 shows an example in which the wireless communication interface 1563 includes multiple RF circuits 1564, the wireless communication interface 1563 may also include a single RF circuit 1564.

Application examples for terminal devices

First application example

16 is a block diagram showing an example of a schematic configuration of a smartphone 1600 to which the technology of the present disclosure can be applied. The smartphone 1600 includes a processor 1601, a memory 1602, a storage device 1603, an external connection interface 1604, a camera device 1606, a sensor 1607, a microphone 1608, an input device 1609, a display device 1610, a speaker 1611, a wireless communication interface 1612, one or more antenna switches 1615, one or more antennas 1616, a bus 1617, a battery 1618, and an auxiliary controller 1619. In one implementation, the smartphone 1600 (or the processor 1601) herein may correspond to the electronic device 300B described above.

The processor 1601 may be, for example, a CPU or a system on chip (SoC), and controls the functions of the application layer and other layers of the smartphone 1600. The memory 1602 includes a RAM and a ROM, and stores data and programs executed by the processor 1601. The storage device 1603 may include a storage medium such as a semiconductor memory and a hard disk. The external connection interface 1604 is an interface for connecting an external device (such as a memory card and a universal serial bus (USB) device) to the smartphone 1600.

The camera device 1606 includes an image sensor such as a charge coupled device (CCD) and a complementary metal oxide semiconductor (CMOS), and generates a captured image. The sensor 1607 may include a group of sensors such as a measurement sensor, a gyro sensor, a geomagnetic sensor, and an acceleration sensor. The microphone 1608 converts the sound input to the smart phone 1600 into an audio signal. The input device 1609 includes, for example, a touch sensor, a keypad, a keyboard, a button, or a switch configured to detect a touch on the screen of the display device 1610, and receives an operation or information input from a user. The display device 1610 includes a screen such as a liquid crystal display (LCD) and an organic light emitting diode (OLED) display, and displays an output image of the smart phone 1600. The speaker 1611 converts an audio signal output from the smart phone 1600 into sound.

The wireless communication interface 1612 supports any cellular communication scheme (such as LTE and LTE-Advanced) and performs wireless communication. The wireless communication interface 1612 may generally include, for example, a BB processor 1613 and an RF circuit 1614. The BB processor 1613 may perform, for example, encoding/decoding, modulation/demodulation, and multiplexing/demultiplexing, and perform various types of signal processing for wireless communication. Meanwhile, the RF circuit 1614 may include, for example, a mixer, a filter, and an amplifier, and transmit and receive wireless signals via an antenna 1616. The wireless communication interface 1612 may be a chip module on which the BB processor 1613 and the RF circuit 1614 are integrated. As shown in FIG. 16 , the wireless communication interface 1612 may include a plurality of BB processors 1613 and a plurality of RF circuits 1614. Although FIG. 16 shows a case in which the wireless communication interface 1612 includes The wireless communication interface 1612 is an example of a plurality of BB processors 1613 and a plurality of RF circuits 1614 , but the wireless communication interface 1612 may also include a single BB processor 1613 or a single RF circuit 1614 .

In addition, in addition to the cellular communication scheme, the wireless communication interface 1612 can support other types of wireless communication schemes, such as short-range wireless communication schemes, near field communication schemes, and wireless local area network (LAN) schemes. In this case, the wireless communication interface 1612 can include a BB processor 1613 and an RF circuit 1614 for each wireless communication scheme.

Each of the antenna switches 1615 switches a connection destination of the antenna 1616 between a plurality of circuits (eg, circuits for different wireless communication schemes) included in the wireless communication interface 1612 .

Each of the antennas 1616 includes a single or multiple antenna elements (such as multiple antenna elements included in a MIMO antenna) and is used for the wireless communication interface 1612 to transmit and receive wireless signals. As shown in FIG16 , the smart phone 1600 may include multiple antennas 1616. Although FIG16 shows an example in which the smart phone 1600 includes multiple antennas 1616, the smart phone 1600 may also include a single antenna 1616.

In addition, the smartphone 1600 may include an antenna 1616 for each wireless communication scheme. In this case, the antenna switch 1615 may be omitted from the configuration of the smartphone 1600.

The bus 1617 connects the processor 1601, the memory 1602, the storage device 1603, the external connection interface 1604, the camera 1606, the sensor 1607, the microphone 1608, the input device 1609, the display device 1610, the speaker 1611, the wireless communication interface 1612, and the auxiliary controller 1619 to each other. The battery 1618 supplies power to the various blocks of the smart phone 1600 shown in FIG. 16 via a feeder, which is partially shown as a dotted line in the figure. The auxiliary controller 1619 operates the minimum necessary functions of the smart phone 1600, for example, in a sleep mode.

Second application example

17 is a block diagram showing an example of a schematic configuration of a car navigation device 1720 to which the technology of the present disclosure can be applied. The car navigation device 1720 includes a processor 1721, a memory 1722, a global positioning system (GPS) module 1724, a sensor 1725, a data interface 1726, a content player 1727, a storage medium interface 1728, an input device 1729, a display device 1730, a speaker 1731, a wireless communication interface 1733, one or more antenna switches 1736, one or more antennas 1737, and a battery 1738. In one implementation, the car navigation device 1720 (or the processor 1721) here may correspond to the above-mentioned electronic device 300B.

The processor 1721 may be, for example, a CPU or a SoC, and controls the navigation function of the car navigation device 1720 and other The memory 1722 includes a RAM and a ROM, and stores data and programs executed by the processor 1721.

The GPS module 1724 measures the position (such as latitude, longitude and altitude) of the car navigation device 1720 using GPS signals received from GPS satellites. The sensor 1725 may include a group of sensors such as a gyro sensor, a geomagnetic sensor and an air pressure sensor. The data interface 1726 is connected to, for example, the vehicle network 1741 via an unshown terminal and acquires data (such as vehicle speed data) generated by the vehicle.

The content player 1727 reproduces content stored in a storage medium such as a CD and a DVD, which is inserted into the storage medium interface 1728. The input device 1729 includes, for example, a touch sensor, a button, or a switch configured to detect a touch on the screen of the display device 1730, and receives an operation or information input from a user. The display device 1730 includes a screen such as an LCD or an OLED display, and displays an image of a navigation function or reproduced content. The speaker 1731 outputs a sound of a navigation function or reproduced content.

The wireless communication interface 1733 supports any cellular communication scheme (such as LTE and LTE-Advanced) and performs wireless communication. The wireless communication interface 1733 may generally include, for example, a BB processor 1734 and an RF circuit 1735. The BB processor 1734 may perform, for example, encoding/decoding, modulation/demodulation, and multiplexing/demultiplexing, and perform various types of signal processing for wireless communication. Meanwhile, the RF circuit 1735 may include, for example, a mixer, a filter, and an amplifier, and transmit and receive wireless signals via an antenna 1737. The wireless communication interface 1733 may also be a chip module on which a BB processor 1734 and an RF circuit 1735 are integrated. As shown in Figure 17, the wireless communication interface 1733 may include multiple BB processors 1734 and multiple RF circuits 1735. Although Figure 17 shows an example in which the wireless communication interface 1733 includes multiple BB processors 1734 and multiple RF circuits 1735, the wireless communication interface 1733 may also include a single BB processor 1734 or a single RF circuit 1735.

In addition, in addition to the cellular communication scheme, the wireless communication interface 1733 can support other types of wireless communication schemes, such as short-range wireless communication schemes, near field communication schemes, and wireless LAN schemes. In this case, for each wireless communication scheme, the wireless communication interface 1733 can include a BB processor 1734 and an RF circuit 1735.

Each of the antenna switches 1736 switches a connection destination of the antenna 1737 between a plurality of circuits included in the wireless communication interface 1733 , such as circuits for different wireless communication schemes.

Each of the antennas 1737 includes a single or multiple antenna elements (such as multiple antenna elements included in a MIMO antenna) and is used for the wireless communication interface 1733 to transmit and receive wireless signals. As shown in FIG. 17, the car navigation device 1720 may include multiple antennas 1737. Although FIG. 17 shows a car navigation device 1720 including multiple antennas 1737 , but the car navigation device 1720 may also include a single antenna 1737 .

In addition, the car navigation device 1720 may include an antenna 1737 for each wireless communication scheme. In this case, the antenna switch 1736 may be omitted from the configuration of the car navigation device 1720.

The battery 1738 supplies power to the respective blocks of the car navigation device 1720 shown in Fig. 17 via a feeder line which is partially shown as a dotted line in the figure. The battery 1738 accumulates the power supplied from the vehicle.

The technology of the present disclosure may also be implemented as an in-vehicle system (or vehicle) 1740 including a car navigation device 1720, an in-vehicle network 1741, and one or more blocks in a vehicle module 1742. The vehicle module 1742 generates vehicle data (such as vehicle speed, engine speed, and fault information), and outputs the generated data to the in-vehicle network 1741.

It should be understood that the technical solution of the present disclosure can be implemented through the following example implementations.

1. An electronic device for a base station, wherein the base station comprises an antenna array and a plurality of radio frequency chains, the electronic device comprises a processing circuit, the processing circuit being configured to:

performing an angle measurement of a channel between the base station and the terminal device based on receiving a first number of pilot signals from the terminal device;

Based on receiving a second number of pilot signals from the terminal device and the angle measurement result for the channel, performing distance measurement for the channel at a specific angle; and

Based on the angle and distance measurement results for the channel, transmission parameters for communication between the base station and the terminal device are determined.

2. An electronic device according to clause 1,

Receiving a first number of pilot signals from the terminal device includes: obtaining a third number of pilot signal samples via the multiple RF chains, wherein the number of the third number of pilot signal samples is based on the first number of pilot signals and the number of the multiple RF chains.

3. The electronic device of clause 2, wherein performing an angle measurement for the channel comprises:

obtaining a plurality of candidate angles of the channel based on the third number of pilot signal samples; and

A preferred angle for communication between the base station and the terminal device is determined from the multiple candidate angles as the specific angle.

4. An electronic device according to clause 1,

Receiving a second number of pilot signals from the terminal device includes: obtaining a fourth number of pilot signal samples via the multiple RF chains, wherein the number of the fourth number of pilot signal samples is based on the second number of pilot signals and the number of the multiple RF chains.

5. The electronic device of clause 4, wherein performing a distance measurement for the channel comprises:

Based on the fourth number of pilot signal samples, a coarse-grained distance measurement is performed on the channel at the specific angle; based on the angle measurement and the coarse-grained distance measurement results of the channel, a fine-grained distance estimation is performed on the channel to determine the transmission parameters of the communication between the base station and the terminal device.

6. An electronic device according to clause 5,

Wherein a fine-grained distance estimation for the channel is performed by an artificial intelligence model, the artificial intelligence model being based on one of:

Convolutional Neural Networks;

Fully connected neural network;

Autoencoders, including variational autoencoders; or

Generative Adversarial Networks.

7. An electronic device according to clause 1 or 3,

The specific angle is determined by an artificial intelligence model, and the artificial intelligence model is based on one of the following:

Convolutional Neural Networks;

Fully connected neural network;

Autoencoders, including variational autoencoders; or

Generative Adversarial Networks.

8. An electronic device according to clause 6 or 7,

The artificial intelligence model is pre-trained and pre-configured to the base station, or is trained and configured on-site at the base station, and the artificial intelligence model is trained based on randomly generated channel data or on-site data at the base station.

9. An electronic device according to clause 1,

The first number of pilot signals and the second number of pilot signals respectively include: a plurality of pilot signals continuous in the time domain; or a plurality of pilot signals within a certain time window,

The first number and the second number respectively include one of the following: 2, 4, 8, 10, 12 or 14, and/or the pilot signal is a sounding reference signal SRS.

10. The electronic device of clause 1, wherein the processing circuit is further configured to:

Configuring the first number of pilot signals and the second number of pilot signals to the terminal device through signaling; and

Once the angle measurement for the channel is completed, an indication of the specific angle is notified to the terminal device through signaling.

11. The electronic device of clause 1, wherein the antenna array is configured to implement holographic MIMO.

12. An electronic device for a base station, wherein the base station comprises an antenna array and a plurality of radio frequency chains, the electronic device comprises a processing circuit, the processing circuit being configured to:

Based on receiving a plurality of pilot signals from a terminal device, obtaining a plurality of pilot signal samples via the plurality of RF chains; performing a coarse-grained distance measurement for a channel between the base station and the terminal device based on the plurality of pilot signal samples; and

Based on the coarse-grained distance measurement results, fine-grained distance estimation for the channel is performed through an artificial intelligence model to determine the transmission parameters for communication between the base station and the terminal device.

13. The electronic device of clause 12, wherein the processing circuit is further configured to:

A distance measurement for the channel is further performed based on the angle measurement result of the channel.

14. The electronic device of clause 12, wherein the number of the plurality of pilot signal samples is based on the number of the plurality of pilot signals and the number of the plurality of radio frequency chains.

15. An electronic device for a base station, comprising a processing circuit, wherein the processing circuit is configured to:

configuring a first number of pilot signals and a second number of pilot signals to the terminal device through signaling,

The first number of pilot signals is indicated as being used for angle measurement of a channel between the base station and the terminal device through flag information, and the second number of pilot signals is indicated as being used for distance measurement of the channel.

16. An electronic device according to clause 15,

The first number of pilot signals and the second number of pilot signals respectively include: a plurality of pilot signals continuous in the time domain; or a plurality of pilot signals within a certain time window,

The first number and the second number each include one of the following: 2, 4, 8, 10, 12 or 14,

The pilot signal is a sounding reference signal SRS.

17. An electronic device for a terminal device, comprising a processing circuit, wherein the processing circuit is configured to:

receiving a pilot signal configuration from a network, the pilot signal configuration comprising a first number of pilot signals and a second number of pilot signals;

Based on the pilot signal configuration, sending a first number of pilot signals to a base station, so that the base station can perform an angle measurement on a channel between the base station and the terminal device;

receiving angle measurement information from the base station; and

Based on the pilot signal configuration and the angle measurement information, a second number of pilot signals are sent to a base station, so that the base station can perform distance measurement on the channel.

18. An electronic device according to clause 17, wherein the angle measurement information indicates a preferred specific angle between the base station and the terminal device, and wherein sending a second number of pilot signals to the base station includes sending a second number of pilot signals to the base station in a direction matching the specific angle.

19. A method for a base station, comprising:

performing an angle measurement of a channel between the base station and the terminal device based on receiving a first number of pilot signals from the terminal device;

Based on receiving a second number of pilot signals from the terminal device and the angle measurement result for the channel, performing distance measurement for the channel at a specific angle; and

Based on the angle and distance measurement results for the channel, transmission parameters for communication between the base station and the terminal device are determined.

20. A method for a base station, comprising:

Based on receiving a plurality of pilot signals from a terminal device, obtaining a plurality of pilot signal samples;

performing a coarse-grained distance measurement for a channel between the base station and the terminal device based on the plurality of pilot signal samples; and

Based on the coarse-grained distance measurement result, fine-grained distance measurement for the channel is performed through an artificial intelligence model to determine the transmission parameters of the communication between the base station and the terminal device.

21. A method for a base station, comprising:

configuring a first number of pilot signals and a second number of pilot signals to the terminal device through signaling,

The first number of pilot signals is indicated as being used for angle measurement of a channel between the base station and the terminal device through flag information, and the second number of pilot signals is indicated as being used for distance measurement of the channel.

22. A method for a terminal device, comprising:

receiving a pilot signal configuration from a network, the pilot signal configuration comprising a first number of pilot signals and a second number of pilot signals;

Based on the pilot signal configuration, sending a first number of pilot signals to a base station, so that the base station can perform an angle measurement on a channel between the base station and the terminal device;

receiving angle measurement information from the base station; and

Based on the pilot signal configuration and the angle measurement information, a second number of pilot signals are sent to a base station, so that the base station can perform distance measurement on the channel.

23. A computer program product comprising instructions which, when executed by a computer, cause the method according to any of clauses 19 to 22 to be implemented.

The exemplary embodiments of the present disclosure are described above with reference to the accompanying drawings, but the present disclosure is certainly not limited to the above examples. Those skilled in the art may obtain various changes and modifications within the scope of the appended claims, and it should be understood that these changes and modifications will naturally fall within the technical scope of the present disclosure.

For example, a plurality of functions included in one unit in the above embodiments may be implemented by separate devices. Alternatively, a plurality of functions implemented by a plurality of units in the above embodiments may be implemented by separate devices, respectively. In addition, one of the above functions may be implemented by a plurality of units. Needless to say, such a configuration is included in the technical scope of the present disclosure.

In this specification, the steps described in the flowchart include not only processing executed in time series in the order described, but also processing executed in parallel or individually rather than necessarily in time series. It goes without saying that the order of the processing steps can be changed appropriately.

Although the present disclosure and its advantages have been described in detail, it should be understood that various changes, substitutions and transformations can be made without departing from the spirit and scope of the present disclosure as defined by the appended claims. Moreover, the terms "including", "comprising" or any other variants of the embodiments of the present disclosure are intended to cover non-exclusive inclusions, so that the process, method, article or equipment including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of further restrictions, the elements defined by the statement "including one..." do not exclude the presence of other identical elements in the process, method, article or equipment including the elements.

Claims (23)

An electronic device for a base station, wherein the base station comprises an antenna array and a plurality of radio frequency chains, the electronic device comprises a processing circuit, wherein the processing circuit is configured to: performing an angle measurement of a channel between the base station and the terminal device based on receiving a first number of pilot signals from the terminal device; Based on receiving a second number of pilot signals from the terminal device and the angle measurement result for the channel, performing distance measurement for the channel at a specific angle; and Based on the angle and distance measurement results for the channel, transmission parameters for communication between the base station and the terminal device are determined. The electronic device according to claim 1, Receiving a first number of pilot signals from the terminal device includes: obtaining a third number of pilot signal samples via the multiple RF chains, wherein the number of the third number of pilot signal samples is based on the first number of pilot signals and the number of the multiple RF chains. The electronic device of claim 2, wherein performing an angle measurement for the channel comprises: obtaining a plurality of candidate angles of the channel based on the third number of pilot signal samples; and A preferred angle for communication between the base station and the terminal device is determined from the multiple candidate angles as the specific angle. The electronic device according to claim 1, Receiving a second number of pilot signals from the terminal device includes: obtaining a fourth number of pilot signal samples via the multiple RF chains, wherein the number of the fourth number of pilot signal samples is based on the second number of pilot signals and the number of the multiple RF chains. The electronic device of claim 4, wherein performing a distance measurement for the channel comprises: performing a coarse-grained distance measurement for the channel at the specific angle based on the fourth number of pilot signal samples; Based on the angle measurement and the coarse-grained distance measurement results of the channel, fine-grained distance estimation for the channel is performed to determine transmission parameters for communication between the base station and the terminal device. The electronic device according to claim 5, The fine-grained distance estimation for the channel is performed by an artificial intelligence model, wherein the artificial intelligence model Based on one of the following: Convolutional Neural Networks; Fully connected neural network; Autoencoders, including variational autoencoders; or Generative Adversarial Networks. The electronic device according to claim 1 or 3, The specific angle is determined by an artificial intelligence model, and the artificial intelligence model is based on one of the following: Convolutional Neural Networks; Fully connected neural network; Autoencoders, including variational autoencoders; or Generative Adversarial Networks. The electronic device according to claim 6 or 7, The artificial intelligence model is pre-trained and pre-configured to the base station, or is trained and configured on-site at the base station, and the artificial intelligence model is trained based on randomly generated channel data or on-site data at the base station. The electronic device according to claim 1, The first number of pilot signals and the second number of pilot signals respectively include: a plurality of pilot signals continuous in the time domain; or a plurality of pilot signals within a certain time window, The first number and the second number each include one of the following: 2, 4, 8, 10, 12 or 14, and/or The pilot signal is a sounding reference signal SRS. The electronic device according to claim 1, wherein the processing circuit is further configured to: Configuring the first number of pilot signals and the second number of pilot signals to the terminal device through signaling; and Once the angle measurement for the channel is completed, an indication of the specific angle is notified to the terminal device through signaling. The electronic device of claim 1, wherein the antenna array is configured to implement holographic MIMO. An electronic device for a base station, wherein the base station comprises an antenna array and a plurality of radio frequency chains, The sub-device comprises a processing circuit configured to: Based on receiving a plurality of pilot signals from a terminal device, obtaining a plurality of pilot signal samples via the plurality of radio frequency chains; performing a coarse-grained distance measurement for a channel between the base station and the terminal device based on the plurality of pilot signal samples; and Based on the coarse-grained distance measurement results, fine-grained distance estimation for the channel is performed through an artificial intelligence model to determine the transmission parameters for communication between the base station and the terminal device. The electronic device according to claim 12, wherein the processing circuit is further configured to: A distance measurement for the channel is further performed based on the angle measurement result of the channel. The electronic device of claim 12, wherein the number of the plurality of pilot signal samples is based on the number of the plurality of pilot signals and the number of the plurality of RF chains. An electronic device for a base station, comprising a processing circuit, wherein the processing circuit is configured to: configuring a first number of pilot signals and a second number of pilot signals to the terminal device through signaling, The first number of pilot signals is indicated as being used for angle measurement of a channel between the base station and the terminal device through flag information, and the second number of pilot signals is indicated as being used for distance measurement of the channel. The electronic device according to claim 15, The first number of pilot signals and the second number of pilot signals respectively include: a plurality of pilot signals continuous in the time domain; or a plurality of pilot signals within a certain time window, The first number and the second number each include one of the following: 2, 4, 8, 10, 12 or 14, The pilot signal is a sounding reference signal SRS. An electronic device for a terminal device comprises a processing circuit, wherein the processing circuit is configured to: receiving a pilot signal configuration from a network, the pilot signal configuration comprising a first number of pilot signals and a second number of pilot signals; Based on the pilot signal configuration, sending a first number of pilot signals to a base station, so that the base station can perform an angle measurement on a channel between the base station and the terminal device; receiving angle measurement information from the base station; and Based on the pilot signal configuration and the angle measurement information, a second number of pilot signals are sent to a base station, so that the base station can perform distance measurement on the channel. An electronic device according to claim 17, wherein the angle measurement information indicates a preferred specific angle between the base station and the terminal device, and wherein sending a second number of pilot signals to the base station includes sending a second number of pilot signals to the base station in a direction matching the specific angle. A method for a base station, comprising: performing an angle measurement of a channel between the base station and the terminal device based on receiving a first number of pilot signals from the terminal device; Based on receiving a second number of pilot signals from the terminal device and the angle measurement result for the channel, performing distance measurement for the channel at a specific angle; and Based on the angle and distance measurement results for the channel, transmission parameters for communication between the base station and the terminal device are determined. A method for a base station, comprising: Based on receiving a plurality of pilot signals from a terminal device, obtaining a plurality of pilot signal samples; performing a coarse-grained distance measurement for a channel between the base station and the terminal device based on the plurality of pilot signal samples; and Based on the coarse-grained distance measurement result, fine-grained distance measurement for the channel is performed through an artificial intelligence model to determine the transmission parameters of the communication between the base station and the terminal device. A method for a base station, comprising: configuring a first number of pilot signals and a second number of pilot signals to the terminal device through signaling, The first number of pilot signals is indicated as being used for angle measurement of a channel between the base station and the terminal device through flag information, and the second number of pilot signals is indicated as being used for distance measurement of the channel. A method for a terminal device, comprising: receiving a pilot signal configuration from a network, the pilot signal configuration comprising a first number of pilot signals and a second number of pilot signals; Based on the pilot signal configuration, sending a first number of pilot signals to a base station, so that the base station can perform an angle measurement on a channel between the base station and the terminal device; receiving angle measurement information from the base station; and Based on the pilot signal configuration and the angle measurement information, a second number of pilot signals are sent to a base station, so that the base station can perform distance measurement on the channel. A computer program product comprising instructions which, when executed by a computer, cause the method according to any one of claims 19 to 22 to be implemented.