Communication method and apparatus

Abstract

The present disclosure relates to a communication method and apparatus. A terminal device obtains M channel estimation results corresponding to M time units, where M is greater than 1. The terminal device performs joint compression on the M channel estimation results by using an encoder network to obtain N pieces of compressed information. The terminal device transmits the N pieces of compressed information to an access network device. Since the terminal device performs joint compression, correlation between downlink channels at different time points is fully utilized. The access network device restores the N pieces of compressed information obtained by joint compression, so that more accurate and effective downlink channel information can be obtained.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to Chinese Patent Application No. 202111590532.1, entitled "Communication Method and Apparatus," filed with the State Intellectual Property Office of China on December 23, 2021, the entire contents of which are incorporated herein by reference.

[0002] The present application relates to the field of communication technologies, and more particularly to communication methods and devices. [Background technology]

[0003] The fifth-generation (5G) mobile communication system places high demands on system capacity and spectral efficiency. In 5G communication systems, the application of massive multiple-input multiple-output (massive-MIMO) technology plays an important role in improving the system's spectral efficiency. By using massive-MIMO technology, base stations can provide high-quality services to a larger number of user equipment (UEs). The key step is for base stations to perform precoding on downlink data. Precoding enables spatial multiplexing, reduces interference between different data streams, and increases the signal-to-interference-plus-noise ratio (SINR) at the receiving end. This helps increase the system throughput rate. To perform precoding more accurately on UE downlink data, the base station can acquire channel state information (CSI) of the downlink channel, recover the downlink channel information based on the CSI, and determine a precoding matrix using the recovered downlink channel information to perform precoding. How to enable the base station to recover more accurate downlink channel information is a technical problem worthy of consideration. Summary of the Invention

[0004] The present disclosure provides a communication method and apparatus for reducing the overhead of reporting high accuracy channel state information by a terminal device. [Means for solving the problem]

[0005] According to a first aspect, a first communication method is provided. The method may be performed on a terminal device. The method may be performed using software, hardware, or a combination of software and hardware. For example, the method may be performed by the terminal device, or by a circuit system, or by a larger device including the terminal device. The circuit system may implement the functions of the terminal device. The method includes the steps of obtaining M channel estimation results, where the M channel estimation results correspond to M time units, where M is an integer greater than 1; performing joint compression on the M channel estimation results to obtain N pieces of compressed information, where N is a positive integer; and transmitting the N pieces of compressed information to an access network device.

[0006] In the present disclosure, a terminal device may perform channel estimation on a downlink channel for M time units, perform joint compression on the obtained M channel estimation results, and report the obtained M channel estimation results to an access network device. Because joint compression is performed, correlation between downlink channels at different times is fully utilized, and the access network device may restore the N pieces of compressed information obtained by joint compression, thereby obtaining more accurate and effective downlink channel information. In addition, because joint compression is performed, the N pieces of compressed information may complement each other. For example, different pieces of compressed information may correspond to different parameters to reduce redundant information and correspondingly reduce transmission overhead.

[0007] In an optional implementation, the step of performing joint compression on the M channel estimation results to obtain N pieces of compressed information may include performing joint compression on the M channel estimation results by using an encoder network to obtain N pieces of compressed information. The terminal device can process the channel estimation results by using the encoder network, so that the access network device can restore the compressed information based on a corresponding decoder network. Compared with conventional solutions, the same size of feedback can include more channel information by using a neural network to reduce information loss in the feedback in compression and improve downlink channel restoration accuracy on the access network device side. Alternatively, compared with conventional solutions, the same channel information can be represented by a smaller amount of feedback to further reduce feedback overhead.

[0008] In an optional implementation, the multiple channel estimation results include a first channel estimation result, which is one of the following: a channel estimation result obtained by performing measurement based on a received downlink reference signal; a processed result obtained by processing a channel estimation result obtained by performing measurement based on a received downlink reference signal; or a channel estimation result obtained by prediction. Some of the M time units may be past time units or current time units. For the time units, the terminal device may perform measurement based on the received downlink reference signal to directly obtain the channel estimation result. However, some time units among the M time units may not arrive. If the terminal device obtains the channel estimation result by performing measurement after the time unit arrives, the previously obtained channel estimation result may be invalid due to an extremely long latency. Alternatively, for the M time units, the terminal device measures only the downlink reference signal for some time units. Thus, the terminal device may process the obtained channel estimation result to obtain a channel estimation result for a future time unit or an unmeasured time unit, or the terminal device may obtain a channel estimation result for a future time unit or an unmeasured time unit through prediction. In this way, the terminal device can obtain more time-unit channel estimation results in a timely manner to reduce latency and improve channel information feedback efficiency. In addition, regardless of whether the future time-unit channel estimation results are obtained based on existing channel estimation results or through prediction, the existing channel estimation results can be used as references. However, because the downlink channel is correlated in the time domain, future channel estimation results of the downlink channel obtained based on previous channel estimation results of the downlink channel are more accurate. This helps the access network device restore the accurate downlink channel.

[0009] In an optional implementation, each of the N pieces of compressed information corresponds to M channel estimation results. Since joint compression is performed on the M channel estimation results, each piece of compressed information can reflect the M channel estimation results, so that a more accurate downlink channel can be reconstructed based on the N pieces of compressed information.

[0010] In an optional implementation, performing joint compression on the M channel estimation results by using an encoder network to obtain N pieces of compressed information includes performing joint compression on the M channel estimation results and history information by using an encoder network to obtain N pieces of compressed information, where the history information includes channel estimation results corresponding to a time unit M time units prior. The downlink channel is correlated in the time domain. Therefore, in addition to the M channel estimation results, channel estimation results corresponding to a time unit M time units prior can be further considered, so that the channel estimation results involved in the compression correspond to more time units. In this way, the channel information represented by the compressed information obtained by the access network device becomes more diverse, and the access network device reconstructs a more accurate downlink channel based on the channel estimation results for more time units.

[0011] In an optional implementation, each of the N pieces of compressed information corresponds to M channel estimation results and historical information. Since joint compression is performed on the M channel estimation results and historical information, each piece of compressed information can reflect the M channel estimation results and historical information, so that a more accurate downlink channel can be restored based on the N pieces of compressed information.

[0012] In an optional implementation, the method further includes receiving configuration information from the access network device, where the configuration information is used to configure the M time units. The M time units may be configured by the access network device, so that the access network device can configure, based on a request, channel estimation results for the requested time units fed back by the terminal device to facilitate restoration operations of the access network device. Alternatively, the M time units may be determined by the terminal device and do not need to be configured by the access network device. In this way, signaling overhead can be reduced.

[0013] In optional implementations, the configuration information includes one or more of the following: a start time-domain position of the M time units, an end time-domain position of the M time units, a duration of the M time units, a number of the M time units, a time-domain position of a first sampling point within the M slots, a sampling period of the M time units, a quantity of sampling points for the M time units, or a time-domain position of a sampling point in the M time units. In addition, the configuration information may further include other information of the M time units. The terminal device can determine the time-domain positions of the sampling points of the M time units based on the configuration information, and can thereby perform channel estimation for the downlink channels of the sampling points.

[0014] In an optional implementation, the method further includes receiving first indication information from the access network device, where the first indication information indicates parameter information of a reference encoder network or an index of the reference encoder network, or sending second indication information to the access network device, where the second indication information indicates parameter information of a reference encoder network or an index of the reference encoder network. The reference encoder network is used to determine an encoder network. The reference encoder network may be indicated to the terminal device by the access network device, and the terminal device does not need to select a reference encoder network. The access network device can select a reference encoder network based on overall requirements, so that the selected reference encoder network is more appropriate. Alternatively, the reference encoder network may be selected by the terminal device, and does not need to be selected by the access network device, thereby reducing the workload of the access network device.

[0015] According to a second aspect, a second communication method is provided. The method may be executed by an access network device. The method may be executed by software, hardware, or a combination of software and hardware. For example, the method may be executed by an access network device, a larger device including the access network device, or a circuit system. The circuit system may implement the functions of the access network device. Alternatively, the method may be executed by an AI module independent of the access network device with the assistance of the access network device or a network element of the access network device. This is not limited to this. For example, the access network device may be a base station. The method includes receiving N pieces of compressed information from a terminal device, where N is a positive integer, and restoring the N pieces of compressed information to obtain K pieces of restored information, where the K pieces of restored information represent M time units of downlink channel information, where K is a positive integer and M is an integer greater than 1.

[0016] In an optional implementation, the step of decompressing the N pieces of compressed information to obtain K pieces of reconstruction information includes the step of decompressing the N pieces of compressed information to obtain K pieces of reconstruction information by using a decoder network.

[0017] In an optional implementation, each of the K pieces of reconstruction information corresponds to N pieces of compression information.

[0018] In an optional implementation, the method further includes transmitting configuration information to the terminal device, where the configuration information is used to configure the M time units.

[0019] In optional implementations, the configuration information includes one or more of the following: a start time-domain position of the M time units, an end time-domain position of the M time units, a duration of the M time units, a number of the M time units, a time-domain position of the first sampling point in the M slots, a sampling period of the M time units, a quantity of sampling points in the M time units, or a time-domain position of a sampling point in the M time units.

[0020] In an optional implementation, the number of sampling points corresponding to the K pieces of restoration information is the same as or different from the number of sampling points corresponding to the N pieces of compression information, and / or the time-domain positions of the sampling points corresponding to the K pieces of restoration information are the same as or different from the time-domain positions of the sampling points corresponding to the N pieces of compression information. In other words, the time-domain positions of the sampling points corresponding to the K pieces of restoration information are located within the M slots, and the number of sampling points corresponding to the K pieces of restoration information, the time-domain positions within the M slots, etc. are not limited. In this way, for an access network device, the restoration process may be flexible.

[0021] In an optional implementation, the method further includes: sending first instruction information to the terminal device, where the first instruction information indicates parameter information of a reference encoder network or an index of the reference encoder network, or receiving second instruction information from the terminal device, where the second instruction information indicates parameter information of a reference encoder network selected by the terminal device or an index of a reference encoder network selected by the terminal device. The reference encoder network and the reference decoder network belong to the same reference network, and the reference decoder network is used to determine the decoder network.

[0022] According to a third aspect, a communication device is provided. The communication device can implement the method according to the first aspect. The communication device has the functionality of a terminal device. In an optional implementation, the device can include one-to-one corresponding modules for performing the method / operation / step / action described in the first aspect. The modules may be hardware circuits or software, or may be implemented by hardware circuits in combination with software. In an optional implementation, the communication device includes a baseband device and a radio frequency device. In another optional implementation, the communication device includes a processing unit (sometimes referred to as a processing module) and a transceiver unit (sometimes referred to as a transceiver module). The transceiver unit can implement transmitting and receiving functions. When the transceiver unit implements transmitting functions, the transceiver unit may be referred to as a transmitting unit (sometimes referred to as a transmitting module). When the transceiver unit implements receiving functions, the transceiver unit may be referred to as a receiving unit (sometimes referred to as a receiving module). The transmitting unit and the receiving unit may be the same functional module, and the functional module is referred to as a transceiver unit. A functional module may implement a transmitting function and a receiving function, or the transmitting unit and the receiving unit may be different functional modules, and the transceiver unit is a generic term for the functional modules.

[0023] The processing unit is configured to obtain M channel estimation results and perform joint compression on the M channel estimation results to obtain N pieces of compressed information, where the M channel estimation results correspond to M time units, where M is an integer greater than 1 and N is a positive integer. The transceiver unit is configured to transmit the N pieces of compressed information to the access network device.

[0024] In another example, a communications device includes a processor coupled to a memory and configured to execute instructions in the memory to implement a method according to the first aspect. Optionally, the communications device further includes other components, such as an antenna, an input / output module, and an interface. The components may be hardware, software, or a combination of software and hardware.

[0025] According to a fourth aspect, a communication device is provided. The communication device can implement the method according to the second aspect. The communication device has functionality of an access network device. The access network device is, for example, a base station or a baseband device in a base station. In an optional implementation, the device can include a one-to-one corresponding module for performing the method / operation / step / action described in the second aspect. The module may be a hardware circuit or software, or may be implemented by a hardware circuit in combination with software. In an optional implementation, the communication device includes a baseband device and a radio frequency device. In another optional implementation, the communication device includes a processing unit (sometimes referred to as a processing module) and a transceiver unit (sometimes referred to as a transceiver module). For an implementation of the transceiver unit, see the relevant description of the third aspect.

[0026] The transceiver unit is configured to receive N pieces of compressed information from a terminal device, where N is a positive integer. The processing unit is configured to decompress the N pieces of compressed information to obtain K pieces of restored information, where each of the K pieces of restored information is M time units of downlink channel information, where K is a positive integer and M is an integer greater than 1.

[0027] In another example, a communication device includes a processor coupled to a memory and configured to execute instructions in the memory to implement the method of the second aspect. Optionally, the communication device further includes other components, such as an antenna, an input / output module, and an interface. The components may be hardware, software, or a combination of software and hardware.

[0028] According to a fifth aspect, there is provided a computer-readable storage medium configured to store computer programs or instructions that, when executed, implement the method according to the first aspect and / or the method in the second aspect.

[0029] According to a sixth aspect, there is provided a computer program product comprising instructions which, when run on a computer, implement the method according to the first aspect and / or the second aspect.

[0030] According to a seventh aspect, there is provided a chip system. The chip system includes a processor and may further include a memory, and the chip system is configured to implement the method according to the first aspect and / or the second aspect. The chip system may include the chip, or may include a chip and another separate component.

[0031] According to an eighth aspect, there is provided a communication system including a communication device according to the third aspect and a communication device according to the fourth aspect. [Brief explanation of the drawings]

[0032] [Figure 1] FIG. 1 is a diagram of a communication system. [Figure 2] 1 is a flowchart of a CSI feedback mechanism. [Figure 3] 1 is a diagram of the architecture of a communication network in a communication system. [Figure 4A]A diagram of an application framework for AI in a communications system. [Figure 4B] A diagram of an application framework for AI in a communications system. [Figure 4C] FIG. 1 is a diagram of another application framework for AI in communications systems. [Figure 4D] FIG. 1 is a diagram of yet another application framework for AI in communications systems. [Figure 4E] FIG. 1 is a diagram of yet another application framework for AI in communications systems. [Figure 5] 1 is a flowchart of a communication method. [Figure 6A] 1A and 1B are diagrams of several input and output scenarios of an encoder network. [Figure 6B] 1A and 1B are diagrams of several input and output scenarios of an encoder network. [Figure 6C] 1A and 1B are diagrams of several input and output scenarios of an encoder network. [Figure 6D] 1A and 1B are diagrams of several input and output scenarios of an encoder network. [Figure 6E] 1A and 1B are diagrams of several input and output scenarios of an encoder network. [Figure 6F] 1A and 1B are diagrams of several input and output scenarios of an encoder network. [Figure 6G] 1A and 1B are diagrams of several input and output scenarios of an encoder network. [Figure 6H] 1A and 1B are diagrams of several input and output scenarios of an encoder network. [Figure 7] FIG. 1 is a diagram of the CSI feedback process. [Figure 8A] 1 is a diagram of several input and output scenarios of a decoder network. [Figure 8B] 1 is a diagram of several input and output scenarios of a decoder network. [Figure 8C]1 is a diagram of several input and output scenarios of a decoder network. [Figure 8D] 1 is a diagram of several input and output scenarios of a decoder network. [Figure 9] FIG. 1 is a schematic block diagram of a communication device. DETAILED DESCRIPTION OF THE INVENTION

[0033] To make the objectives, technical solutions, and advantages of the present disclosure clearer, the present disclosure will hereinafter be described in detail with reference to the accompanying drawings.

[0034] The techniques provided in this disclosure may be applied to a communication system 10 shown in FIG. 1 . The communication system 10 includes one or more communication devices 30 (e.g., terminal devices). The one or more communication devices 30 are connected to one or more core network (CN) devices using one or more access network (RAN) devices 20 to implement communication between the communication devices. For example, the communication system 10 may be a communication system supporting the 4th generation (4G) (including long term evolution (LTE)) access technology, a communication system supporting 5G (sometimes referred to as new radio (NR)) access technology, a wireless fidelity (Wi-Fi) system, a cellular system associated with the 3rd generation partnership project (3GPP®), a communication system supporting the convergence of multiple wireless technologies, a future-oriented evolutionary system, etc., without limitation.

[0035] The terminal device and the RAN in FIG. 1 will be described in detail below.

[0036] 1. Terminal Device A terminal device may simply be referred to as a terminal. A terminal device may be a device having a wireless transceiver function. A terminal device may be mobile or fixed. A terminal device may be deployed on land, including indoor, outdoor, handheld, or vehicle-mounted deployment, on water (e.g., on a ship), or in the air (e.g., on an aircraft, balloon, or satellite). A terminal device may include a mobile phone, a tablet computer, a computer having a wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device for industrial control, a wireless terminal device for self-driving, a wireless terminal device for remote medical surgery, a wireless terminal device for smart grids, a wireless terminal device for transportation safety, a wireless terminal device for smart cities, and / or a wireless terminal device for smart homes. Alternatively, the terminal device may also be a cellular phone, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA), a handheld or computing device with wireless communication capabilities, an in-vehicle device, a wearable device, a terminal device in a future 5th generation (5G) network, a terminal device in a further evolved public land mobile network (PLMN), etc. The terminal device may also be referred to as user equipment (UE). Optionally, the terminal device may communicate with multiple access network devices using different technologies.For example, a terminal device may communicate with an access network device that supports LTE, or may communicate with an access network device that supports 5G, or may implement dual connectivity to an access network device that supports LTE and an access network device that supports 5G. This is not a limitation in the present disclosure.

[0037] In the present disclosure, an apparatus configured to implement the functions of a terminal device may be a terminal device, or may be an apparatus capable of supporting a terminal device in implementing functions, such as a chip system, a hardware circuit, a software module, or a hardware circuit combined with a software module. The apparatus may be installed in a terminal device or may be matched with a terminal device for use. In the technical solutions provided in the present disclosure, an example in which an apparatus configured to implement the functions of a terminal device is a terminal device and the terminal device is a UE is used to describe the technical solutions provided in the present disclosure.

[0038] In the present disclosure, a chip system may include a chip, or may include a chip and other separate components.

[0039] 2.RAN The RAN may include one or more RAN devices, such as RAN device 20. The interface between the RAN device and the terminal device may be a Uu interface (also called an air interface). In future communications, the name of the interface may remain the same or may be replaced by another name. This is not a limitation in this application.

[0040] A RAN device is a node or device that enables a terminal device to access a wireless network, and may also be referred to as a network device or a base station. Examples of RAN devices include, but are not limited to, a base station, a 5G next-generation NodeB (gNB), an evolved NodeB (eNB), a radio network controller (RNC), a Node B (NB), a base station controller (BSC), a base transceiver station (BTS), a home base station (e.g., a home evolved NodeB, or home node B, HNB), a base band unit (BBU), a transmission reception point (TRP), a transmitting point (TP), and / or a mobile switching center. Alternatively, the access network device may be at least one of a central unit (CU), a distributed unit (DU), a central unit control plane (CU-CP) node, a central unit user plane (CU-UP) node, an integrated access and backhaul (IAB) node, a radio controller of a cloud radio access network (CRAN), etc. Alternatively, the access network device may be a relay station, an access point, an in-vehicle device, a terminal device, a wearable device, an access network device in a 5G network, an access network device in a future evolved public land mobile network (PLMN), etc.

[0041] In the present disclosure, an apparatus configured to implement the functions of an access network device may be an access network device, or may be an apparatus capable of supporting an access network device in implementing the functions, such as a chip system, a hardware circuit, a software module, or a hardware circuit combined with a software module. The apparatus may be installed in the access network device or may be matched with the access network device for use. In the technical solution provided in the present disclosure, an example in which the apparatus configured to implement the functions of an access network device is an access network device, and the access network device is a base station, is used to describe the technical solution provided in the present disclosure.

[0042] (1) Protocol layer structure Communications between the access network device and the terminal device follow a specific protocol layer structure. The protocol layer structure may include a control plane protocol layer structure and a user plane protocol layer structure. For example, the control plane protocol layer structure may include at least one of the following: a radio resource control (RRC) layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, a media access control (MAC) layer, a physical layer (PHY), etc. For example, the user plane protocol layer structure may include at least one of the following: a service data adaptation protocol (SDAP) layer, a PDCP layer, an RLC layer, a MAC layer, a physical layer, etc.

[0043] The protocol layer structure between the access network device and the terminal device can be considered as an access stratum (AS) structure. Optionally, a non-access stratum (NAS) may further exist above the AS and is used by the access network device to transfer information from the core network device to the terminal device, or used by the access network device to transfer information from the terminal device to the core network device. In this case, it can be considered that there is a logical interface between the terminal device and the core network device. Optionally, the access network device may transfer information between the terminal device and the core network device via transparent transmission. For example, NAS messages may be mapped to or included in RRC signaling as elements of the RRC signaling.

[0044] Optionally, the protocol layer structure between the access network device and the terminal device may further include an artificial intelligence (AI) layer for transmitting data related to AI functions.

[0045] (2) Central Unit (CU) and Distributed Unit (DU) A RAN device may include a CU and a DU. This design may be referred to as a CU and DU separated design. Multiple DUs may be centrally controlled by one CU. For example, the interface between the CU and the DU may be called an F1 interface. The control plane (CP) interface may be F1-C, and the user plane (UP) interface may be F1-U. The specific names of the interfaces are not limited in this disclosure. The CU and the DU may be divided based on the protocol layer of the wireless network. For example, the functions of the PDCP layer and protocol layers above the PDCP layer (e.g., the RRC layer and the SDAP layer) are configured in the CU, and the functions of the protocol layers below the PDCP layer (e.g., the RLC layer, the MAC layer, and the PHY layer) are configured in the DU. In another example, the functions of the protocol layers above the PDCP layer are configured in the CU, and the functions of the PDCP layer and protocol layers below the PDCP layer are configured in the DU.

[0046] The division of the CU and DU into processing functions based on protocol layers is merely an example, and the division can be performed in other ways. For example, the CU or DU can be divided to have functions with more protocol layers. In another example, the CU or DU can be further divided into some processing functions that include multiple protocol layers. In a design, some functions of the RLC layer and functions of protocol layers above the RLC layer are set in the CU, and the remaining functions of the RLC layer and functions of protocol layers below the RLC layer are set in the DU. In another design, the division of the CU or DU functions can instead be performed based on service type or another system requirement. For example, the division can be performed based on delay. Functions that require processing time to meet delay requirements are set in the DU, and functions that do not require processing time to meet delay requirements are set in the CU.

[0047] Optionally, the CU may also have one or more functions of a core network, for example, the CU may be located on the network side to facilitate centralized management.

[0048] Optionally, a radio unit (RU) of the DU is remotely located. The RU has radio frequency functions. For example, the DU and the RU may be separated at the PHY layer. For example, the DU may implement upper layer functions of the PHY layer, and the RU may implement lower layer functions of the PHY layer. When transmission is performed, the PHY layer functions may include at least one of the following: adding a cyclic redundancy check (CRC), channel coding, rate matching, scrambling, modulation, layer mapping, precoding, resource mapping, physical antenna mapping, or radio frequency transmission. When reception is performed, the PHY layer functions may include at least one of the following: CRC check, channel decoding, de-rate matching, descrambling, demodulation, layer demapping, channel detection, resource demapping, physical antenna demapping, or radio frequency reception. The upper layer functions of the PHY layer may include some of the functions of the PHY layer. For example, some of the functions are closer to the MAC layer. The lower layer functions of the PHY layer may include other parts of the functions of the PHY layer. For example, some of the functions are closer to radio frequency reception functions. For example, the upper layer functions of the PHY layer may include adding CRC checks, channel coding, rate matching, scrambling, modulation, and layer mapping, while the lower layer functions of the PHY layer may include precoding, resource mapping, physical antenna mapping, and radio frequency transmission functions. Alternatively, the upper layer functions of the PHY layer may include CRC checks, channel coding, rate matching, scrambling, modulation, layer mapping, and precoding, while the lower layer functions of the PHY layer may include resource mapping, physical antenna mapping, and radio frequency transmission functions. For example, the upper layer functions of the PHY layer may include adding CRC checks, channel decoding, de-rate matching, decoding, demodulation, and layer demapping, while the lower layer functions of the PHY layer may include channel detection, resource demapping, physical antenna demapping, and radio frequency reception.Alternatively, the upper layer functions of the PHY layer may include CRC checking, channel decoding, de-rate matching, decoding, demodulation, layer demapping, and channel detection, and the lower layer functions of the PHY layer may include resource demapping, physical antenna demapping, and radio frequency reception.

[0049] Optionally, the functionality of the CU may be further divided, with the control plane and user plane being separated and implemented by using separate entities, i.e., a control plane CU entity (i.e., a CU-CP entity) and a user plane CU entity (i.e., a CU-UP entity), respectively. The CU-CP entity and the CU-UP entity may be separately coupled or connected to the DU to jointly complete the functions of the RAN device.

[0050] In the above-described network architecture, signaling generated by a CU may be transmitted to a terminal device by using a DU, or signaling generated by a terminal device may be transmitted to a CU by using a DU. For example, signaling at the RRC layer or PDCP layer may be ultimately processed as signaling at the physical layer and transmitted to a terminal device, or converted from signaling received from the physical layer. In this architecture, signaling at the RRC layer or PDCP layer may be considered to be transmitted by using a DU, or transmitted by using a DU and a RU.

[0051] Optionally, any one of the DU, CU, CU-CP, CU-UP, and RU may be a software module, a hardware structure, or a combination of a software module and a hardware structure. This is not limited thereto. Different entities may exist in different forms. This is not limited thereto. For example, the DU, CU, CU-CP, and CU-UP are software modules, and the RU is a hardware structure. Modules and methods performed by modules also fall within the scope of protection of the present disclosure. For example, when the method of the present disclosure is performed by an access network device, the method may be specifically performed by at least one of the CU, CU-CP, CU-UP, DU, RU, or near-real-time RIC described below. Methods performed by modules also fall within the scope of protection of the present disclosure.

[0052] Please note that since the network devices in the present disclosure are mainly access network devices, hereinafter, unless otherwise specified, "network device" may also be "access network device".

[0053] It should be understood that the number of devices in the communication system shown in Figure 1 is used as an example only, and the present disclosure is not limited thereto. In actual applications, the communication system may further include more terminal devices and more RAN devices, or may further include other devices, such as core network devices and / or nodes configured to implement artificial intelligence functions.

[0054] The network architecture shown in FIG. 1 may be applicable to communication systems of various radio access technologies (RATs), for example, a 4G communication system, a 5G (also called new radio (NR)) communication system, and a transition system between an LTE communication system and a 5G communication system, which may also be called a 4.5G communication system or a future communication system, for example, a 6G communication system. The network architecture and service scenarios described in this disclosure are intended to more clearly explain the technical solutions in this disclosure and do not constitute limitations on the technical solutions provided in this disclosure. Those skilled in the art may know that with the development of network architectures and the emergence of new service scenarios, the technical solutions provided in this disclosure may also be applicable to similar technical problems.

[0055] In addition to communication between an access network device and a terminal device, the method provided in the present application can also be used for communication between other communication devices, such as communication between a macro base station and a micro base station in a wireless backhaul link, or communication between a first terminal device and a second terminal device in a sidelink (SL). This is not limited to this. The present disclosure will be described by using communication between a network device and a terminal device as an example.

[0056] When transmitting data to the terminal device, the access network device can perform precoding based on channel state information (CSI) fed back by the terminal device. In order to facilitate understanding of the present disclosure, the following briefly describes some technical terms in the present disclosure.

[0057] 1. Precoding technology When channel state information is known, an access network device can process a signal to be transmitted by using a precoding matrix that matches the channel conditions. Using this technique, the precoded signal to be transmitted matches the channel, thereby improving the quality of the signal received by the terminal device (e.g., signal-to-interference plus noise ratio (SINR)), and further improving the system throughput rate. Using precoding techniques, a transmitting device (e.g., an access network device) and multiple receiving devices (e.g., terminal devices) can effectively transmit on the same time-frequency resource, i.e., effectively implementing multi-user multiple input multiple output (MU-MIMO). Using precoding techniques, a transmitting device (e.g., an access network device) and receiving devices (e.g., terminal devices) can effectively transmit multiple data streams on the same time-frequency resource, i.e., effectively implementing single-user multiple input multiple output (SU-MIMO). It should be noted that the relevant description of precoding techniques is merely an example for ease of understanding and is not intended to limit the scope of the disclosure of the present disclosure. In a specific implementation process, the transmitting device may alternatively perform precoding in another manner. For example, when channel information (for example, but not limited to, a channel matrix) cannot be obtained, precoding is performed by using a preconfigured precoding matrix or by using a weighting process. For brevity, the specific contents thereof will not be described again in this specification.

[0058] 2. CSI feedback CSI feedback is sometimes referred to as a CSI report. According to CSI feedback, in a wireless communication system, a receiver (e.g., a terminal device) of data (e.g., data carried on a physical downlink shared channel (PDSCH) without limitation) reports information for describing channel attributes of a communication link to a transmitter (e.g., an access network device). The CSI report includes one or more pieces of information, such as a downlink channel matrix, a precoding matrix indicator (PMI), a rank indicator (RI), and a channel quality indicator (CQI). The contents included in the listed CSI are merely examples for explanation and do not constitute any limitation on the present disclosure. The CSI may include one or more of the aforementioned items or may include other information used to represent the CSI in addition to the aforementioned listed information. This is not a limitation in the present disclosure.

[0059] 3. Neural network (NN) Neural networks are a specific implementation of machine learning technology. According to the universal approximation theorem, neural networks can theoretically approximate any continuous function, and as a result, they can learn any mapping. Traditional communication systems require extensive expertise to design communication modules. However, neural network-based deep learning communication systems can automatically discover implicit pattern structures from large datasets, establish mapping relationships between data, and achieve better performance than traditional modeling methods.

[0060] For example, a deep neural network (DNN) is a neural network with a large number of layers. Depending on different network structures and / or usage scenarios, the DNN may include a multi-layer perceptron (MLP), a convolutional neural network (CNN), a recurrent neural network (RNN), etc. The specific form of the DNN is not limited by this disclosure.

[0061] 4. Auto-encoder (AE) network, or AE for short. An AE network may include an encoder and a corresponding decoder. For example, the encoder and / or decoder may be implemented using a neural network (e.g., a DNN). In this case, the encoder may be referred to as an encoder network, and the decoder may be referred to as a decoder network. For example, in an AE network, the encoder and the corresponding decoder may be obtained by joint training. The encoder and decoder obtained by training may be configured to encode and decode information.

[0062] In this disclosure, unless otherwise specified, the quantity of a noun refers to "singular or plural," i.e., "one or more." "At least one" means one or more, and "multiple" means two or more. "And / or" describes an association relationship between associated objects and indicates that three relationships may exist. For example, A and / or B may indicate three cases: only A is present, both A and B are present, and only B is present, where A and B may be singular or plural. When indicating a feature, the character " / " may indicate an "or" relationship between associated objects. For example, A / B indicates A or B. When representing an operation, the symbol " / " may further represent the operation of division. Also, in this disclosure, the symbol "x" may also be replaced with the symbol "*."

[0063] In the present disclosure, ordinal numbers such as "first" and "second" are used to distinguish between multiple objects and are not used to limit the size, content, sequence, time series, application scenario, priority, importance, etc. of the multiple objects. For example, the first instruction information and the second instruction information may be the same instruction information or different instruction information. Furthermore, this type of name does not indicate that the size, transmission mode, instruction content, priority, application scenario, importance, etc. of the two instruction information are different.

[0064] In a possible implementation, the CSI feedback mechanism uses the procedure shown in FIG.

[0065] S21: The base station sends signaling, and the UE correspondingly receives signaling from the base station.

[0066] The signaling is used to configure channel measurement information, for example, the signaling notifies the UE of at least one of time information for performing channel measurement, type of reference signal (RS) for performing channel measurement, time domain resource of the reference signal, frequency domain resource of the reference signal, reporting condition of the measured quantity, etc.

[0067] S22: The base station transmits a reference signal to the UE, and in response, the UE receives a reference signal from the base station.

[0068] The UE measures the reference signal to obtain the CSI.

[0069] S23: The UE transmits the CSI to the base station, and in response, the base station receives the CSI from the UE.

[0070] S24: The base station transmits data to the UE based on the CSI, and the UE receives data from the base station correspondingly.

[0071] Data transmitted by the base station to the UE is carried on a downlink channel, for example, on the PDSCH.

[0072] The CSI may represent information about the downlink channel matrix. After the UE feeds back the CSI to the base station, the base station can reconstruct the downlink channel information based on the CSI to determine information such as a precoding matrix based on the downlink channel information. Higher accuracy of the CSI fed back by the UE indicates more information about the downlink channel matrix and more accurate downlink channel information that can be reconstructed by the base station based on the CSI. Therefore, a more accurate precoding matrix determined by the base station indicates better downlink spatial multiplexing performance, a higher received signal-to-interference-and-noise ratio for the UE, and a higher system throughput rate. Alternatively, after the UE feeds back the CSI to the base station, the base station does not need to reconstruct the downlink channel information but can determine information such as a precoding matrix based on the CSI. In this case, higher CSI accuracy indicates a more accurate precoding matrix determined by the base station. However, as the size of the antenna array in a MIMO system continuously increases, the number of antenna ports that can be supported also increases. Because the size of the complete downlink channel matrix is ​​directly proportional to the number of antenna ports, a massive feedback overhead is required to ensure high accuracy of the CSI fed back by the UE in a massive MIMO system. The huge feedback overhead reduces the resources available for data transmission, thus reducing the system capacity. Therefore, it is necessary to consider how to reduce the CSI feedback overhead in order to increase the system capacity.

[0073] Deep learning (DL) is machine learning based on deep neural networks. CSI is fed back using deep learning. Compared with traditional solutions, this method can represent nearly identical downlink channel information with less feedback, thereby reducing CSI feedback overhead or improving channel reconstruction accuracy by using the same overhead. In deep learning-based CSI compression feedback technology, channel compression feedback is performed using an AE model based on a convolutional neural network (CNN). The AE model includes an encoder model and a decoder model that match each other for use. Specifically, in the UE (i.e., the information transmitting side), the encoder model of the AE maps the downlink channel matrix in a slot to compressed information, and the encoder model is a CNN-type neural network. In the base station (i.e., the information receiving side), the decoder model of the AE can restore the compressed information to downlink channel information, and the decoder model is a CNN-type neural network. In this way, the UE may need to separately feed back CSI for multiple slots to the base station. This still incurs a large overhead.

[0074] In consideration of this, the present disclosure provides a technical solution. In the present disclosure, a UE can perform channel estimation on a downlink channel for M time units, perform joint compression on the obtained M channel estimation results, and report the obtained M channel estimation results to an access network device. Because joint compression is performed, correlation between downlink channels at different times is fully utilized, and the access network device can restore the N pieces of compressed information obtained by joint compression, thereby obtaining more accurate and effective downlink channel information. In addition, because joint compression is performed, the N pieces of compressed information can complement each other. For example, different pieces of compressed information can correspond to different parameters to reduce redundant information and correspondingly reduce transmission overhead.

[0075] 3 illustrates an architecture of a communication network in a communication system 10 according to the present disclosure. Any embodiments provided thereafter are applicable to the architecture. A network device included in FIG. 3 is, for example, an access network device 20 included in the communication system 10, and a terminal device included in FIG. 3 is, for example, a communication device 30 included in the communication system 10. The network device can communicate with the terminal device.

[0076] The machine learning technology of the present disclosure is a specific implementation of artificial intelligence (AI) technology. For ease of understanding, the AI ​​technology will be described below. It can be understood that the description is not intended to limit the present disclosure.

[0077] AI is a technology that performs complex calculations by simulating the human brain, and as data storage and functionality improves, AI is becoming more widely applied.

[0078] In the present disclosure, an independent network element (e.g., referred to as an AI network element, AI node, or AI device) may be introduced into the communication system shown in FIG. 1 to implement AI functions. The AI ​​network element may be directly connected to an access network device or indirectly connected to the access network device by using a third-party network element. Optionally, the third-party network element may be a core network element. Alternatively, an AI entity may be configured or located in another network element within the communication system to implement AI-related operations. The AI ​​entity may also be referred to as an AI module, AI unit, or other name, and is primarily configured to implement some or all of the AI ​​functions. The specific name of the AI ​​entity is not limited in the present disclosure. Optionally, the other network element may be an access network device, a core network device, an operation, administration, and maintenance (OAM), etc. In this case, the network element that performs the AI ​​function is a network element with the AI ​​function built in.

[0079] In this disclosure, AI functions may include at least one of the following: data collection, model training (or model learning), model information publication, model inference (also referred to as model estimation, inference, prediction, etc.), model monitoring or model validation, inference result publication, etc. AI functions may also be referred to as AI (related) operations or AI-related functions.

[0080] In the present disclosure, an OAM is configured to operate, manage, and / or maintain a core network device (OAM of the core network device) and / or to operate, manage, and / or maintain an access network device (OAM of the access network device). For example, the present disclosure includes a first OAM and a second OAM, where the first OAM is an OAM of the core network device and the second OAM is an OAM of the access network device. Optionally, the first OAM and / or the second OAM include an AI entity. In another example, the present disclosure includes a third OAM, where the third OAM is an OAM of both the core network device and the access network device. Optionally, the third OAM includes an AI entity.

[0081] FIG. 4A is a schematic diagram of a first application framework for AI in a communication system. A data source is used to store training data and inference data. A model training host performs training or update training based on training data provided by the data source to obtain an AI model, and deploys the AI ​​model to a model inference host. The AI ​​model represents a mapping relationship between the model's input and output. Obtaining an AI model through learning by the model training host is equivalent to obtaining a mapping relationship between the model's input and output through learning by the model training host using training data. The model inference host uses the AI ​​model to perform inference based on inference data provided by the data source and obtains an inference result. This method can alternatively be described as follows: the model inference host inputs inference data to the AI ​​model and obtains an output by using the AI ​​model, where the output is the inference result. The inference result may indicate configuration parameters used (acted upon) by an action subject and / or an operation performed by the action subject. The inference results may be uniformly planned by an actor and sent to one or more targets of an action object (e.g., a core network element, a base station, or a UE) for action. Optionally, the model inference host may feed back the inference results of the model inference host to the model training host. This process may be referred to as model feedback. The fed back inference results are used by the model training host to update the AI ​​model, and the updated AI model is deployed to the model inference host. Optionally, the target of the action may feed back network parameters collected by the target of the action to a data source. This process may also be referred to as performance feedback, and the fed back network parameters may be used as training data or inference data.

[0082] For example, the AI ​​model includes a decoder network in the AE network. The decoder network is deployed on the access network device side. The inference result of the decoder network is used, for example, to reconstruct a downlink channel matrix. The AI ​​model includes an encoder network in the AE network. The encoder network is deployed on the UE side. The inference result of the encoder network is used, for example, to encode the downlink channel matrix.

[0083] The application framework shown in FIG. 4A can be deployed on the network elements shown in FIG. 1. For example, the application framework of FIG. 4A may be deployed on at least one of the terminal device, access network device, core network device (not shown), or independently deployed AI network element (not shown) of FIG. 1. For example, the AI ​​network element (which can be considered a model training host) can analyze or train training data provided by the terminal device and / or access network device to obtain a model. At least one of the terminal device, access network device, or core network device (which can be considered a model inference host) can perform inference by using the model and the inference data to obtain an output of the model. The inference data can be provided by the terminal device and / or access network device. The input of the model includes the inference data, and the output of the model is an inference result corresponding to the model. At least one of the terminal device, access network device, or core network device (which can be considered a target of the action) can perform a corresponding operation based on the inference result. The model inference host and the target of the action may be the same or different. This is not a limitation.

[0084] With reference to FIGS. 4B to 4E, the following describes, by using an example, a network architecture to which the method provided in the present disclosure can be applied.

[0085] As shown in FIG. 4B , in a first possible implementation, the access network device includes a near-real-time radio access network intelligent controller (RAN intelligent controller, RIC) module for model training and inference. For example, the near-real-time RIC may be configured to train an AI model and use the AI ​​model for inference. For example, the near-real-time RIC may obtain network-side and / or terminal-side information from at least one of the CU, DU, and RU, which may be used as training data or inference data. Optionally, the near-real-time RIC may submit inference results to at least one of the CU, DU, RU, or terminal device. Optionally, the CU and DU may exchange inference results. Optionally, the DU and RU may exchange inference results. For example, the near-real-time RIC may submit the inference results to the DU, and the DU may forward the inference results to the RU.

[0086] As shown in FIG. 4B , in a second possible implementation, a non-real-time RIC (optionally, the non-real-time RIC may be located in an OAM or core network device) is located outside the access network and is used for model training and inference. For example, the non-real-time RIC is configured to train an AI model and use the model for inference. For example, the non-real-time RIC can obtain network-side and / or terminal-side information from at least one of a CU, a DU, or an RU, and the information can be used as training data or inference data. The inference results may be submitted to at least one of a CU, a DU, an RU, or a terminal device. Optionally, the CU and the DU can exchange the inference results. Optionally, the DU and the RU can exchange the inference results. For example, the non-real-time RIC submits the inference results to the DU, and the DU forwards the inference results to the RU.

[0087] As shown in FIG. 4B , in a third possible implementation, the access network device includes a near-real-time RIC, and the non-real-time RIC is located outside the access network device (optionally, the non-real-time RIC may be located in an OAM or core network device). Similar to the second possible implementation described above, the non-real-time RIC can be used for model training and inference, and / or similar to the first possible implementation, the near-real-time RIC can be used for model training and inference, and / or the non-real-time RIC can perform model training, acquire AI model information from the non-real-time RIC, acquire network-side and / or terminal-side information from at least one of a CU, a DU, or a RU, and acquire an inference result by using the information and the AI ​​model information. Optionally, the near-real-time RIC can submit the inference result to at least one of a CU, a DU, a RU, or a terminal device. Optionally, the CU and the DU can exchange the inference result. Optionally, the DU and the RU can exchange the inference result. For example, the near-real-time RIC submits the inference result to the DU, and the DU forwards the inference result to the RU. For example, the near-real-time RIC is configured to train model A and use model A for inference. For example, the non-real-time RIC is configured to train model B and use model B for inference. For example, the non-real-time RIC is configured to train model C and send information about model C to the near-real-time RIC, which uses model C for inference.

[0088] 4C is an exemplary diagram of a network architecture to which the method according to the present disclosure can be applied. Compared with FIG. 4B, the CU in FIG. 4C is separated into a CU-CP and a CU-UP.

[0089] 4D is an example diagram of a network architecture to which a method according to the present disclosure can be applied. As shown in FIG. 4D, optionally, an access network device includes one or more AI entities, and the functions of the AI ​​entities are similar to those of a near-real-time RIC. Optionally, an OAM includes one or more AI entities, and the functions of the AI ​​entities are similar to those of a non-real-time RIC. Optionally, a core network device includes one or more AI entities, and the functions of the AI ​​entities are similar to those of a non-real-time RIC. If the OAM and the core network device each include an AI entity, the models trained and obtained by the AI ​​entities of the OAM and the core network device are different, and / or the models used for inference are different.

[0090] In the present disclosure, the models differ in at least one of the following: structural parameters of the models (e.g., at least one of the number of neural network layers, neural network width, connectivity between layers, neuron weights, neuron activation functions, or offsets in activation functions); input parameters of the models (e.g., types of input parameters and / or dimensions of input parameters); or output parameters of the models (e.g., types of output parameters and / or dimensions of output parameters).

[0091] FIG. 4E is an exemplary diagram of a network architecture to which a method according to the present disclosure can be applied. Compared with FIG. 4D, the access network device in FIG. 4E is separated into a CU and a DU. Optionally, the CU may include an AI entity, whose function is similar to that of a near-real-time RIC. Optionally, the DU may include an AI entity, whose function is similar to that of a near-real-time RIC. When the CU and DU each include an AI entity, the models obtained by training by the AI ​​entities of the CU and DU are different and / or the models used for inference are different. Optionally, the CU in FIG. 4E may be further divided into a CU-CP and a CU-UP. Optionally, one or more AI models may be deployed in the CU-CP. Optionally, one or more AI models may be deployed in the CU-UP.

[0092] In Figure 4D or 4E, the OAM of the access network devices and the OAM of the core network devices are deployed uniformly. Alternatively, as described above, in Figure 4D or 4E, the OAM of the access network devices and the OAM of the core network devices may be deployed separately.

[0093] In the present disclosure, a model can obtain an output through inference, and the output includes one or more parameters. The learning or training processes of different models may be deployed on different devices or nodes, or may be deployed on the same device or node. The inference processes of different models may be deployed on different devices or nodes, or may be deployed on the same device or node.

[0094] Optionally, the AI ​​model includes a decoder network in the AE network. At the base station side, the inference result of the decoder network is used, for example, to reconstruct a downlink channel matrix. Optionally, the AI ​​model includes an encoder network in the AE network. Model information of the encoder network can be transmitted to the UE for inference.

[0095] Note that in the framework of FIGS. 4A-4D , the AI ​​model may be referred to as a model or a network model for short, and may be considered as a mapping from input parameters (e.g., an input matrix) to output parameters (e.g., an output matrix). For example, in the case of a base station-side decoder network, the input matrix may be a matrix determined based on received CSI. The training data may include a known input matrix, or may include a known input matrix and a corresponding output matrix, and is used to train the AI ​​model. The training data may be data from an access network device, a CU, a CU-CP, a CU-UP, a DU, a RU, a UE, and / or another entity, and / or data obtained by inference using AI techniques. This is not limited to this. The inference data includes an input matrix and is used to infer an output matrix by using the model. The inference data may be data from an access network device, a CU, a CU-CP, a CU-UP, a DU, a RU, a UE, and / or another entity. The inferred matrix may be considered as policy information and is transmitted to a target for action. The matrix obtained by inference may be transmitted to an access network device, CU, CU-CP, CU-UP, DU, RU, or UE for further processing, e.g., reconstruction of the downlink channel matrix.

[0096] In the present disclosure, on the network side, the decoder network in the AE network may be deployed in an access network device (such as a base station) or outside the access network device, for example, in an OAM, an AI network element, a core network device, an RU, a DU, or a near-real-time RIC. This is not limited to this. The inference result of the decoder network may be obtained by inference performed by the access network device, or may be transmitted to the access network device after a non-real-time RIC performs inference. For simplicity, the present disclosure will be described using an example in which the decoder network is deployed in the access network device.

[0097] In the present disclosure, an encoder network in an AE network is deployed in a UE, and the UE can perform inference by using the encoder network.

[0098] The method provided in the present disclosure will be described below with reference to the accompanying drawings. The steps or operations included in the method are merely examples, and other operations or various variations of operations may also be performed in the present disclosure. Furthermore, the steps may be performed in an order different from that presented in the present disclosure, and not all operations of the present disclosure may be performed.

[0099] FIG. 5 is a flowchart of a communication method according to the present disclosure.

[0100] Optionally, S501: An access network device sends configuration information to a UE, and in response, the UE receives configuration information from the access network device.

[0101] The configuration information may have another name, for example, may be referred to as CSI feedback configuration information or feedback configuration information. The name is not limited in this disclosure. The configuration information may be used to configure M time units, where M is an integer greater than 1. The M time units are time units in which downlink channel information that the UE needs to feed back is located. In other words, the UE needs to feed back downlink channel information in M ​​time units. M is greater than 1, that is, the UE can feed back downlink channel information in multiple time units to the access network device. The M time units may be contiguous, that is, the M time units are of continuous duration. In this case, the M time units may be considered to be one time frame, e.g., referred to as a first time frame. In other words, the configuration information is used to configure the first time frame. Alternatively, the M time units may be separate. For example, at least two adjacent time units within the M time units are discontinuous. For example, the time unit is a subframe, a slot, a mini-slot, or an orthogonal frequency division multiplexing (OFDM) symbol. In this disclosure, an example in which the time unit is a slot is used. Therefore, "slot" is used in the following description. That is, "slot" below can be read as "time unit."

[0102] The start time-domain positions of the M slots may be located before, after, or at the current time-domain position. The current time-domain position is, for example, the time-domain position at which the UE receives configuration information. In other words, the downlink channels in the M slots may include one or more of the past downlink channels, the current downlink channels, and the future downlink channels.

[0103] For example, the configuration information may include one or more of the following: start time-domain positions of the M slots, end time-domain positions of the M slots, duration of the M slots, number of the M slots, sampling period in the M slots, quantity of sampling points in the M slots, time-domain positions of the first sampling point in the M slots, or time-domain positions of sampling points (e.g., all sampling points) in the M slots. The number of slots may be, for example, the number of slots in a subframe or the number of slots in a radio frame. A sampling point is a sampling instant at which a UE needs to estimate a downlink channel at the sampling point. The first sampling point in the M slots is the first sampling point in the M slots in the time domain. For example, the sampling point is represented by using a slot, and one sampling point corresponds to one slot. The UE performs channel estimation for the downlink channel at the sampling point, i.e., performs information estimation for the downlink channel in the slot. Alternatively, the sampling point may be represented by using an OFDM symbol.

[0104] For example, if the configuration information includes start time-domain positions of the M slots, end time-domain positions of the M slots, and sampling periods within the M slots, the UE may determine, based on the configuration information, positions of sampling points within the M slots to perform channel estimation for the downlink channel at the sampling points. As another example, if the configuration information includes start time-domain positions (or end time-domain positions) of the M slots, durations of the M slots, and sampling periods within the M slots, the UE may determine, based on the configuration information, positions of sampling points within the M slots to perform channel estimation for the downlink channel at the sampling points. As another example, if the configuration information includes start time-domain positions (or end time-domain positions) of the M slots, sampling periods within the M slots, and the number of sampling points, the UE may determine, based on the configuration information, positions of sampling points to perform channel estimation for the downlink channel at the sampling points. As another example, if the configuration information includes start time-domain positions of the M slots, durations of the M slots, and the number of sampling points within the M slots, the UE may determine, based on the configuration information, positions of sampling points to perform channel estimation for the downlink channel at the sampling points. For example, M slots are consecutive, the configuration information includes the number M of sampling points in the M slots, the start time-domain position of the M slots is n1, and the duration of the M slots is T. In this case, the UE can determine that the sampling period in the M slots is T / M, so that the time-domain positions of the sampling points in the M slots are [n1, n1+T / M, n1+2T / M,..., n1+(N-1)T / M]. Some of the configuration methods described above are applicable when M slots are consecutive.

[0105] For example, if the configuration information includes the number of M slots, the time-domain positions of the first sampling points within the M slots, and the sampling period within the M slots, the UE may determine the time-domain positions of the M slots based on the number of M slots, and then determine the time-domain positions of each sampling point within the M slots based on the time-domain positions of the first sampling point within the M slots and the sampling period, to perform channel estimation on the downlink channel at the sampling points. The configuration scheme is applicable when the M slots are separate.

[0106] For example, if the configuration information includes time-domain locations of sampling points within the M slots, the UE may perform channel estimation for the downlink channel at the sampling points based on the configuration information. The configuration scheme is applicable to both the case where the M slots are separate and the case where the M slots are consecutive.

[0107] Optionally, one or more of the following information: the start time-domain positions of the M slots, the end time-domain positions of the M slots, the duration of the M slots, the number of M slots, the sampling period of the M slots, the quantity of sampling points of the M slots, or the time-domain positions of sampling points of the M slots may be predefined in the protocol, preconfigured in the UE, or may use default values. For example, if the start time-domain positions of the M slots are predefined in the protocol, the first offset is predefined in the protocol. For example, the start time-domain positions of the M slots are time-domain positions obtained by adding a first offset at the moment the UE receives the configuration information, where the first offset is a real number. In another example, the sampling period within the M slots is predefined in the protocol as one transmission time interval (TTI). In another example, the duration of the M slots is the duration of the shortest time frame including required sampling points by default. In another example, the quantity of sampling points within the M slots is P by default, where P is a real number. One TTI may be, but is not limited to, one or more subframes, one or more slots, one or more symbols, or another possible configuration.

[0108] The UE may determine the M slots based on configuration information and information predefined in the protocol (or information preconfigured in the UE, or default information). For example, the configuration information may include that the start time-domain positions of the M slots are n1, the duration is T, and the sampling period within the M slots predefined in the protocol is 1 TTI. In this case, the UE may determine the time-domain positions of the sampling points within the M slots based on n1, T, and the sampling period to perform channel estimation for the downlink channel at the sampling points.

[0109] Alternatively, the UE can determine the M slots based on information predefined in the protocol (or preconfigured in the UE, or default information), and the access network device does not need to send configuration information. Therefore, S501 is an optional step.

[0110] Optionally, the access network device may further transmit information used to configure the reference signal to the UE. For example, the information may be referred to as reference signal configuration information. This information may be used to configure time domain resources, frequency domain resources, and / or the like of the reference signal, and the UE may detect the reference signal from the access network device based on this information. The access network device may convey the information used to configure the M slots and the configuration information in one message for transmission, or may transmit the information used to configure the M slots and the configuration information separately. When the access network device transmits the information used to configure the M slots and the configuration information separately, the two pieces of information may be transmitted simultaneously, or the information may be transmitted first, or the configuration information used to configure the M slots may be transmitted first.

[0111] Optionally, the access network device further transmits a downlink reference signal to the UE, and the UE can receive the downlink reference signal based on the information used to configure the downlink reference signal and perform channel estimation for the downlink channel based on the downlink reference signal. The access network device can transmit the downlink reference signal to the UE in M ​​slots, or the access network device can transmit the downlink reference signal to the UE before the start time-domain position of the M slots arrives, or the access network device can transmit the downlink reference signal to the UE in M ​​slots before the start time-domain position of the M slots arrives. The downlink reference signal is, for example, a synchronization signal and physical broadcast channel (PBCH) block (SSB) and a channel state information reference signal (CSI-RS).

[0112] S502: The UE obtains M channel estimation results. The M channel estimation results correspond to M time units. For example, the M channel estimation results may reflect characteristics of a downlink channel in the M time units.

[0113] The UE may determine time-domain locations of the sampling points within the M slots based on the configuration information and / or information used to configure the reference signal, such that the UE performs channel estimation on the downlink channel at the sampling points. For example, the sampling points are represented by using slots. In this disclosure, it is used as an example that one slot corresponds to one sampling point. In this case, the M slots may correspond to the M sampling points. The UE may perform channel estimation on the downlink channel at one sampling point and obtain a channel estimation result. In this case, the UE may perform channel estimation on the downlink channel at the M sampling points and obtain M channel estimation results.

[0114] For example, when one of the M channel estimation results is referred to as a first channel estimation result, the first channel estimation result may be a channel estimation result obtained by the UE by performing measurements based on a received downlink reference signal. For example, the access network device may transmit a downlink reference signal to the UE in M ​​slots, or the access network device may transmit a downlink reference signal to the UE before the starting time-domain position of the M slots arrives, or the access network device may transmit a downlink reference signal to the UE within M slots before the starting time-domain position of the M slots arrives. The UE may receive the downlink reference signal in M ​​slots, or may receive the downlink reference signal before the starting time-domain position of the M slots arrives, or may receive the downlink reference signal within M slots and before the starting time-domain position of the M slots arrives. The time-domain position at which the UE receives the downlink reference signal is not limited in the present disclosure. If the time-domain position of the sampling point within the M slots is the current time-domain position or before the current time-domain position, the UE may measure the downlink reference signal regardless of the specific time-domain position or the specific time-domain position at which the UE receives the downlink reference signal, to obtain a channel estimation result at the sampling point.

[0115] Alternatively, the first channel estimation result may be a processing result obtained by the UE by processing the second channel estimation result, which is obtained by measuring a received downlink reference signal. In other words, the first channel estimation result is obtained after the UE processes the obtained channel estimation result. For example, the time-domain position of the sampling point in the M slots is a time-domain position after the current time-domain position, i.e., the time-domain position of the sampling point has not yet arrived. If the UE performs channel estimation when the time-domain position of the sampling point arrives, the previously obtained channel estimation result may be invalid due to excessively long latency. Alternatively, for the M slots, the UE only measures the downlink reference signal in some slots. Therefore, in the processing scheme, the UE can process the obtained channel estimation result to obtain a channel estimation result for a future slot or an unmeasured slot, or the UE can obtain a channel estimation result for a future time unit or an unmeasured time unit through prediction. In this way, the UE can obtain channel estimation results for more slots in a timely manner to reduce latency and improve channel information feedback efficiency. For the process in which the UE measures the received downlink reference signal to obtain the second channel estimation result, please refer to the description in the previous paragraph. After obtaining the second channel estimation result, the UE can process the second channel estimation result, and the processing result can be used as the first channel estimation result.

[0116] For example, the first channel estimation results may be obtained by using a convolution of the second channel estimation results with a shaped waveform.

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[0117] In Formula 1, j is an integer ranging from 1 to M, and g j (m) represents the coefficient of the shaped waveform. In particular, if the M' second channel estimation results are channel estimation results obtained by equally spaced measurements and the measurement period is T, then:

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[0118] Alternatively, the first channel estimation result may be a channel estimation result obtained through prediction by the UE. As mentioned above, when the time-domain position of the sampling point within the M slots is a time-domain position after the current time-domain position, the UE can obtain the channel estimation result of the future sampling point through corresponding processing, and prediction is another processing method.

[0119] For example, the UE may predict the first channel estimation result by using a Prony algorithm. For example, the UE may perform weighted summation on the channel estimation results of b slots from the (nb)th slot to the (n-1)th slot to obtain the first channel estimation result of the nth slot. Alternatively, the UE may perform channel prediction by using a long short-term memory (LSTM) artificial neural network to obtain the first channel estimation result. The input of the LSTM is, for example, the channel estimation results of b slots from the (nb)th slot to the (n-1)th slot, and the output of the LSTM is, for example, the first channel estimation result of the nth slot. Optionally, parameters of the LSTM may be obtained through training by using a neural network optimizer such as an Adam optimizer. In this case, b is a positive integer. For example, b may be equal to 2 or 4, or may be another value.

[0120] The first channel estimation result can be obtained in the three possible implementations described above. Therefore, the M channel estimation results may include one or more of the following: a channel estimation result obtained by performing measurements based on a received downlink reference signal; a processed result obtained by processing existing channel estimation results; or a channel estimation result obtained by prediction. The methods for determining different channel estimation results may be the same or different. This is not limited. For example, M1 channel estimation results among the M channel estimation results are obtained by performing measurements based on a received downlink reference signal, and the other M-M1 channel estimation results are processed results obtained by processing the M1 channel estimation results, where M1 is an integer greater than 0 and less than M. For simplicity, examples will not be provided one by one.

[0121] S503: The UE performs joint compression on the M channel estimation results to obtain N compressed information, where N is a positive integer.

[0122] In this disclosure, the UE can compress M channel estimation results together by using an encoder network. In consideration of this, the UE first needs to determine the encoder network to be used.

[0123] In a technology in which CSI feedback is performed with reference to a neural network, a typical neural network architecture is a dual architecture. An autoencoder is used as an example. Compressed transmission may be implemented by joint optimization of an encoder and a decoder. For example, one or more groups of reference networks (also referred to as one or more reference networks) may be obtained by training, and the reference networks may be an encoder network (also referred to as a reference encoder network)-decoder network (also referred to as a reference decoder network) pair. That is, the group of reference networks may include a reference encoder network and a corresponding reference decoder network. These reference networks may be trained offline or online. If the training is performed offline, the training may be specified in a protocol. For example, the protocol may provide parameters such as the network structure and / or weights of one or more reference networks (including the reference encoder network and the reference decoder network). A UE or an access network device may implement the reference network with reference to the protocol. Optionally, the protocol may provide evaluation performance corresponding to a specific reference network in an agreed-upon dataset.

[0124] If multiple groups of reference networks exist, the access network device may indicate to the UE a specific group of reference networks or a reference encoder network within the specific group of reference networks to use. For example, the access network device may send first indication information to the UE, where the first indication information indicates parameter information of the encoder network. The first indication information indicates the parameter information of the encoder network. In the indication manner, the first indication information includes parameter information of the encoder network, where the parameter information of the encoder network includes, for example, parameters such as the structure and / or weights of the encoder network. The encoder network indicated by the first indication information is, for example, a reference encoder network, and the UE may determine the corresponding reference encoder network based on the first indication information. Alternatively, the first indication information may indicate an index of the encoder network (e.g., an index of the reference encoder network) or an index of the reference network, and the UE may also determine the corresponding reference encoder network based on the first indication information. After determining the reference encoder network, the UE can directly use the determined reference encoder network. In other words, the reference encoder network is the encoder network ultimately used by the UE. Alternatively, after determining the reference encoder network, the UE does not directly use the reference encoder network, but the characteristics of the encoder network ultimately used by the UE may be determined based on the characteristics of the reference encoder network. For example, the input dimension of the encoder network currently used by the UE is determined based on the input dimension of the reference encoder network. For example, the input dimension of the encoder network used by the UE is equal to the input dimension of the reference encoder network. Similarly, for example, the output dimension of the encoder network used by the UE is also determined based on the output dimension of the reference encoder network.For example, the output dimension of the encoder network used by the UE is the same as the output dimension of the reference encoder network. For example, if the input of the encoder network used by the UE is the same as the input of the reference encoder network, the difference between the output of the encoder network used by the UE and the output of the reference encoder network is less than a threshold.

[0125] Alternatively, the UE may select a specific reference network group or a reference encoder network within a specific reference network group by the UE. For example, the UE may select one of multiple groups of reference networks based on factors such as the evaluation performance of the reference network, where the reference network includes a reference encoder network. For how the UE determines the encoder network to be used after the reference encoder network is determined, see the description in the previous paragraphs. When the UE has determined the reference encoder network, the UE may send second indication information to the access network device, where the second indication information may indicate parameter information of the encoder network selected by the UE. If the encoder network ultimately used by the UE is not the same as the reference encoder network, the second indication information may indicate parameter information of the reference encoder network. The access network device may determine a reference network to which the reference encoder network belongs based on the parameter information of the reference encoder network, and determine a reference decoder network included in the reference network. Alternatively, the second indication information may indicate parameter information of the reference network selected by the UE, and the access network device may determine a reference decoder network included in the reference network based on the parameter information of the reference network. Alternatively, the second indication information may indicate an index of the encoder network selected by the UE. If the encoder network ultimately used by the UE is not the same as the reference encoder network, the second indication information can indicate the index of the reference encoder network or the index of the reference network, so that the access network device can determine the corresponding reference decoder network. The second indication information needs to indicate network parameter information. In the indication manner, the second indication information includes network parameter information.For example, when the second instruction information indicates parameter information of a reference encoder network, the second instruction information may specifically include parameter information of the reference encoder network, for example, parameters such as the structure and / or weight of the encoder network. The second instruction information needs to indicate the index of the network. In the instruction manner, the second instruction information includes the index of the network. For example, when the second instruction information indicates the index of the reference encoder network, the second instruction information may specifically include the index of the reference encoder network.

[0126] Whether the access network device sends the first instruction information to the UE or the UE sends the second instruction information to the access network device, when parameter information of the encoder network (or the reference encoder network or the reference network) is transmitted, to reduce transmission overhead, the transmitter (access network device or UE) can optionally compress the parameter information of the encoder network (or the reference encoder network or the reference network), and the first instruction information or the second instruction information can include the compressed parameter information. Compression includes, but is not limited to, one or more of model pruning, model distillation, model quantization, etc. Model pruning indicates that some parameter information of the encoder network (or the reference encoder network or the reference network) is transmitted to the UE, but the remaining parameter information is not transmitted. The UE acquires only some, but not all, parameter information of the encoder network (or the reference encoder network or the reference network). This corresponds to pruning some parameters to reduce transmission overhead. Model distillation indicates that a first model is acquired based on the parameter information of the encoder network (or the reference encoder network or the reference network). The scale of the first model is smaller than the scale of the encoder network (or the reference encoder network or the reference network). It can be understood that the encoder network (or the reference encoder network or the reference network) is a large-scale model, and the encoder network (or the reference encoder network or the reference network) may have a large number of parameters. However, the first model is a small-scale model, and the number of parameters corresponding to the first model may be small. In this case, the first instruction information or the second instruction information may include the first model (or may include parameter information of the first model). In this way, the transmission overhead can also be reduced.After receiving the first model, the receiving side (UE or access network device) directly uses the first model. It should be understood that the receiving side uses the first model as the encoder network (or reference encoder network or reference network). Model quantization means that the parameter information of the encoder network (or reference encoder network or reference network) is, for example, a floating-point number, and the transmitting side can quantize the parameter information of the encoder network (or reference encoder network or reference network). The parameter information of the encoder network (or reference encoder network or reference network) included in the first instruction information or the second instruction information may be a quantization parameter to reduce transmission overhead.

[0127] When the UE determines the encoder network, an implementation form in which the UE performs joint compression on M channel estimation results is as follows: the UE inputs the M channel estimation results to the encoder network, and the encoder network can perform joint compression on the M channel estimation results. After the joint compression, the encoder network can output N pieces of compressed information. Since the N pieces of compressed information are obtained by performing joint compression on the M channel estimation results, each of the N pieces of compressed information corresponds to some or all of the M channel estimation results, and the channel estimation results corresponding to different pieces of compressed information may be the same, different, or not completely the same. Different pieces of compressed information may correspond to the same parameters of the same channel estimation result, or different parameters of the same channel estimation result, or different pieces of compressed information may correspond to parameters that are not completely the same in the same channel estimation result. The fact that one piece of compressed information corresponds to one channel estimation result may be understood as meaning that the compressed information reflects characteristics of the channel estimation result, or that the process of generating the compressed information is related to the channel estimation result, or that the compressed information corresponds to all or some parameters of the channel estimation result.

[0128] For example, when M=3, the M channel estimation results are channel estimation result 1, channel estimation result 2, and channel estimation result 3, respectively, and all three channel estimation results correspond to parameters A to E; when N=3, the N pieces of compressed information are compressed information 1, compressed information 2, and compressed information 3, respectively. Compression information 1 may reflect parameters A and B corresponding to channel estimation result 1, parameters C and D corresponding to channel estimation result 2, and parameters A, D, and E corresponding to channel estimation result 3. Compression information 2 may reflect parameters C and E corresponding to channel estimation result 1, parameters C and D corresponding to channel estimation result 2, and parameters B and C corresponding to channel estimation result 3. Compression information 3 may reflect parameter D corresponding to channel estimation result 1, parameters A, B, and E corresponding to channel estimation result 2, and parameters A and B corresponding to channel estimation result 3. In this case, different pieces of compressed information correspond to the same channel estimation result, and the parameters corresponding to different pieces of compressed information include the following several cases: Different pieces of compressed information may correspond to the same parameters of the same channel estimation result (e.g., compressed information 1 corresponds to parameters C and D of channel estimation result 2, and compressed information 2 also corresponds to parameters C and D of channel estimation result 2). Different pieces of compressed information may correspond to different parameters of the same channel estimation result (e.g., compressed information 1 corresponds to parameters A and B of channel estimation result 1, and compressed information 2 corresponds to parameters C and E of channel estimation result 1). Different pieces of compressed information may also correspond to parameters that are not completely the same in the same channel estimation result (e.g., compressed information 1 corresponds to parameters A, D, and E of channel estimation result 3, and compressed information 3 corresponds to parameters A and B of channel estimation result 3). Also, in this example, for example, the parameters corresponding to M channel estimation results are all the same (i.e., all three channel estimation results correspond to parameters A to E). In practice, the parameters corresponding to different channel estimation results may be different or may not be completely the same. This is not a limitation herein.Also, in this example, for example, the parameters corresponding to the N pieces of compressed information are all the same. In reality, the parameters corresponding to different pieces of compressed information may also be different or may not be completely the same. For example, N=2, compressed information 1 may correspond to M parameters A of M channel estimation results, and compressed information 2 no longer corresponds to parameter A.

[0129] Optionally, to ensure that the downlink channel recovered by the access network device is more accurate, when performing compression, the UE can not only perform joint compression on the M channel estimation results but also perform processing on historical information. In other words, the UE can perform joint compression on the M channel estimation results and historical information to obtain N pieces of compressed information. The historical information, for example, includes channel estimation results corresponding to L slots M slots before. For example, the historical information includes L channel estimation results. L is a positive integer, and the L slots may be consecutive or distinct. For example, the L slots are configured using configuration information. For example, the number of L slots can be configured using the configuration information. Alternatively, the L slots may be determined by the UE. For a method by which the UE obtains the historical information, see the aforementioned method by which the UE obtains M channel estimation results. For example, the historical information includes the L channel estimation results. For example, the UE can input both the M channel estimation results and the L channel estimation results to an encoder network, and the encoder network can perform joint compression on the M channel estimation results and the L channel estimation results. After the joint compression, the encoder network can output N pieces of compressed information. The N pieces of compressed information are obtained by performing joint compression on the M channel estimation results and the L channel estimation results, so that each of the N pieces of compressed information corresponds to some or all of the M channel estimation results and the L channel estimation results, and the channel estimation results corresponding to different pieces of compressed information may be the same, different, or not completely the same. The different pieces of compressed information may correspond to the same parameter of the same channel estimation result, or different parameters of the same channel estimation result, or different pieces of compressed information may correspond to parameters that are not completely the same in the same channel estimation result. See the previous example for an example of this.For example, any one or two channel estimation results in the above example are considered to be channel estimation results included in the history information.

[0130] For example, the value of N may be N = 1. Specifically, the UE may obtain one piece of compressed information based on the M channel estimation results (or based on the M channel estimation results and the L channel estimation results).

[0131] In an implementation of the channel estimation result, the channel estimation result is channel information, and the UE can obtain one piece of compressed information based on the M pieces of channel information (or based on the M pieces of channel information and the L pieces of channel information). The channel information may also be called a channel response, a channel matrix, etc. The dimension of the channel information is, for example, [N tx ,N rx ,N RB ], where N tx represents the number of antennas or ports on the transmitting side (e.g., access network device), and N rx represents the number of antennas or ports at the receiving end (e.g., UE), and N RB represents the number of frequency domain units, e.g., the number of resource blocks (RB), and N tx , N rx , and N RB is a positive integer. The dimension reduction is performed by using singular value decomposition (SVD) to obtain the eigensubspace matrices of the downlink channel [N tx ,N rx ,N RB ], and the dimensions of the eigensubspace matrices are [N tx ,N sb ]. N sb is the number of frequency domain subbands, e.g., N sb =N RB / a, where a represents the frequency domain subband granularity or subband bandwidth, i.e., the number of RBs included in each subband. Common granularities of frequency domain subbands are 1 RB, 2 RBs, or 4 RBs. When the granularity is 4 RBs, N sb =N RB / 4. One RB includes a positive integer number of subcarriers, for example, 12 or 16 subcarriers. When performing dimensionality reduction of channel information by using SVD, the UE can process different ranks, which can also be understood as different streams or different layers. One piece of channel information (or one channel estimation result) can correspond to one or more layers. The following describes the UE processing process for the L'th layer, where L' is a positive integer.

[0132] If each subband in the L'th layer includes an RB, the UE can calculate an equivalent downlink channel based on the downlink channel of the RB. i Assuming that , the equivalent downlink channel within a subband can be expressed as:

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[0133] UE is

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[0134] That is,

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[0135] In Equation 2 and Equation 3, Hi The dimension of is [N tx ,N rx ], where H i H is H i represents the conjugate transpose of

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[0136] The UE combines the eigenvectors of all subbands in the L'th layer to obtain the eigensubspace matrix V = [V1V2...V Nsb ], where V is [N tx ,N sb ]. Two groups of DFT bases are the discrete Fourier transform (DFT), i.e., the spatial bases

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[0137] S H is the Hermitian matrix of S, also known as the self-conjugate matrix, which can be obtained by performing a conjugate transpose on the matrix S, where S represents the spatial basis and the specific form of S is related to the antenna panel. Assuming that the antenna panel is dual polarized, with a horizontal component of Nh and a vertical component of Nv, the representation form of S can be obtained as follows:

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[0138] F represents the frequency basis, and the representation of F is the subband N sb For example, F can satisfy the following equation: F=DFT(N sb ) (Formula 6)

[0139] Optionally, an oversampling factor may be further added in the DFT process. For example, a group of multiple orthogonal spatial bases {S1, S2, S3...} and a group of multiple orthogonal frequency bases {F1, F2, F3...} may be generated in an oversampling manner, and S i and F j A set of the above groups is selected as the spatial and frequency bases of the present disclosure. For example, a group with a more accurate projection direction may be selected. For example, the oversampling factor for both the spatial and frequency domains is 4.

[0140] The complex matrix C obtained by performing the above operations complexis a sparse representation of the eigensubspace of the original channel, and the dimensions of the complex matrix match those of the eigensubspace matrix before projection, [N tx ,N sb ]. One channel estimation result can correspond to one or more layers, and processing one layer results in one complex matrix C complex Then, multiple complex matrices C can be obtained based on one channel estimation result. complex The UE can obtain all the complex matrices C corresponding to the M channel estimation results (or the M channel estimation results and the L channel estimation results). complex may be input to the encoder network, or the UE may input each complex matrix C corresponding to the M channel estimation results (or the M channel estimation results and the L channel estimation results) complex One may perform corresponding processing on C (e.g., converting the complex matrix to a 3D real-valued tensor) and then input the complex matrix to the encoder network. complex The process of converting into a 3D real-valued tensor is described below.

[0141] In another implementation of the channel estimation result, the channel estimation result is an eigenvector, and the UE can obtain one compressed information based on the M eigenvectors (or based on the M eigenvectors and the L eigenvectors). When the channel estimation result is an eigenvector, each layer is represented by an eigensubspace matrix V = [V1V2...V Nsb ] and V has [N tx ,N sb ] is a complex matrix having a dimension of [ ]. The UE can perform data preprocessing on the eigensubspace matrix in each layer. For example, the calculation process for performing data preprocessing on the eigensubspace matrix of a layer is as follows:

[0142] UE is the DFT formula: spatial basis

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[0143] Optionally, an oversampling factor may be further added in the DFT process, see above for details.

[0144] The UE calculates the complex matrix C based on one channel estimation result. complex and M complex matrices C based on the M channel estimation results. complex If there are still L channel estimation results, the UE can obtain L complex matrices C complex We can further obtain the complex matrix C complex After obtaining the complex matrix C, the UE complex may be directly input to the encoder network, or the UE may perform corresponding processing on the obtained complex matrix and then input the obtained complex matrix to the encoder network. For example, in the processing method, the UE may complex into a 3D real-valued tensor, and then input the obtained 3D real-valued tensor into the encoder network. For example, complex In this case, the UE tx ,N sb To obtain a real matrix with dimensions complex Similarly, UE extracts the real part of each element of [N tx ,N sbTo obtain a real matrix with dimensions complex Furthermore, the UE can concatenate two real matrices to create a matrix with dimensions [2,N tx ,N sb ], i.e., H[0]=real(C complex ), H[1]=imag(C complex ), where the tensor H is a 3D real-valued tensor.

[0145] The above process for determining the channel estimation result is used only as an example, and in the present disclosure, the channel estimation may be performed in other possible ways, which is not limited.

[0146] For example, Figure 6A shows an example of an input / output scenario of an encoder network. Figure 6A uses an example where the input information of the encoder network does not include history information. In Figure 6A,

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[0147] Optionally,

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[0148] The finally output compressed information c is a [C, 1]-dimensional real vector, where C represents the length of the compressed information, and can be obtained based on the requirement of feedback overhead.

[0149] Furthermore, Figure 6C shows an example of an input and output scenario for the encoder network. Figure 6C uses an example where the input information for the encoder network includes historical information. In Figure 6C,

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[0150] Optionally,

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[0151] The compressed information c that is finally output is a [C, 1]-dimensional real vector.

[0152] In this case, the UE can obtain one piece of compressed information by using an encoder network. Specifically, for a downlink channel within M slots, the UE can represent the downlink channel by using one piece of compressed information. The UE only needs to transmit one piece of compressed information to the access network device. In addition, since the compressed information can reflect the characteristics of the downlink channel within M slots, the UE does not need to transmit compressed information within each slot. In this way, transmission overhead can be significantly reduced. In addition, the compressed information is obtained by performing joint compression on the M channel estimation results (or the M channel estimation results and the L channel estimation results) and can reflect the state of the downlink channel within the M slots. In addition, the downlink channel is correlated in the time domain. The compressed information can better reflect the relative status of the downlink channel in the time domain, so that the access network device can more accurately restore the downlink channel based on the compressed information.

[0153] In another example, the value of N may be N>1. Specifically, the UE can obtain multiple pieces of compression information based on M channel estimation results (or based on M channel estimation results and L channel estimation results). When N is greater than 1, N may be equal to M, smaller than M, or greater than M. It can be seen that the number of channel estimation results input by the encoder network may or may not be equal to the number of multiple pieces of compression information output by the encoder network, thereby making the channel compression process more flexible. Even when N is greater than 1, each of the N pieces of compression information corresponds to M channel estimation results or M channel estimation results and L channel estimation results. The value of N is related to the change speed of the downlink channel. In a low-speed scenario (e.g., in the 3GPP channel model, the indoor speed is 3 km / h (80%) and the outdoor speed is 30 km / h (20%)), N=M=10 is an appropriate value. In this case, the M consecutive channel estimation results change slightly, and the change rule of the M consecutive channel estimation results has been learned by the network for channel compression. When N=M, the network structure is better designed.

[0154] For example, Figure 6E shows an example of an input / output scenario of an encoder network. Figure 6E uses an example where the input information of the encoder network does not include history information. In Figure 6E,

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[0155] Optionally,

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[0156]

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[0157] Furthermore, Figure 6G shows an example of an input and output scenario for the encoder network. Figure 6G uses an example where the input information for the encoder network includes historical information. In Figure 6G,

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[0158] Optionally,

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[0159]

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[0160] In this case, the N pieces of compressed information are obtained by performing joint compression on the M channel estimation results (or the M channel estimation results and the L channel estimation results) and can reflect the state of the downlink channel within the M slots. In addition, the downlink channel is correlated in the time domain. The compressed information can better reflect the relative status of the downlink channel in the time domain, so that the access network device can more accurately restore the downlink channel based on the compressed information. In addition, since joint compression is performed on the M channel estimation results (or the M channel estimation results and the L channel estimation results), each piece of compressed information does not need to represent all parameters of the channel estimation results involved in the compression. For example, the N pieces of compressed information can correspond to different parameters of the M channel estimation results (or the M channel estimation results and the L channel estimation results), respectively. As a result, the amount of information in the N pieces of compressed information can be reduced to reduce transmission overhead. Also, in this case, the input information of the encoder network includes multiple channel estimation results, and the output information includes multiple pieces of compressed information. Compared with a solution in which a single piece of compressed information is output, the multiple-input multiple-output solution can simplify the structure of the encoder network.

[0161] S504: The UE sends N pieces of compressed information to the access network device. Correspondingly, the access network device receives N pieces of compressed information from the UE. The N pieces of compressed information may be regarded as CSI or PMI.

[0162] FIG. 7 is a diagram of a CSI feedback process. In FIG. 7, the access network device transmits configuration information to the UE and then transmits a downlink reference signal to the UE. For example, the downlink reference signal is CSI-RS. The UE can transmit compressed information to the access network device multiple times. For example, after performing channel estimation on the downlink channel for M slots, the UE can transmit N pieces of compressed information to the access network device. The UE then continues to perform channel estimation on the downlink channel within M slots and then transmits the obtained compressed information to the access network device. In FIG. 7, the UE transmits compressed information to the access network device twice or feeds back CSI twice. The CSI fed back by the UE to the access network device for the first time may include one or more of the following channel estimation results: a channel estimation result obtained by the UE based on the first received CSI-RS (the first CSI-RS from left to right in FIG. 7 ), a channel estimation result corresponding to a downlink channel before the downlink channel corresponding to the first CSI-RS, or a channel estimation result corresponding to a downlink channel after the downlink channel corresponding to the first CSI-RS. The slot in which the UE feeds back CSI to the access network device for the first time is later than the slot in which the UE receives CSI-RS for the first time (or later than the slot in which the access network device delivers CSI-RS for the first time). The slot in which the UE feeds back CSI to the access network device for the first time may be earlier or later than the slot in which the UE receives CSI-RS for the second time (or earlier or later than the slot in which the access network device delivers CSI-RS for the second time). FIG. 7 uses an earlier slot than the slot in which the UE receives CSI-RS for the second time as an example.Similarly, the CSI fed back by the UE to the access network device for the kth time may include one or more of the following channel estimation results: a channel estimation result obtained by the UE based on the CSI-RS received for the kth time, a channel estimation result corresponding to a downlink channel before the downlink channel corresponding to the CSI-RS received for the kth time, or a channel estimation result corresponding to a downlink channel after the downlink channel corresponding to the CSI-RS received for the kth time. The slot in which the UE feeds back the CSI to the access network device for the kth time may be later than the slot in which the UE receives the CSI-RS for the kth time (or later than the slot in which the access network device delivers the CSI-RS for the kth time). The slot in which the UE feeds back the CSI to the access network device for the kth time may be earlier or later than the slot in which the UE receives the CSI-RS for the (k+1)th time (or earlier or later than the slot in which the access network device delivers the CSI-RS for the (k+1)th time). In addition, it can be seen from FIG. 7 that the M slots indicated by the configuration information may include the moment the configuration information is received, and may further include the moment before the configuration information is received and the moment after the configuration information is received.

[0163] S505: The access network device restores the N pieces of compressed information to obtain K pieces of restored information. The K pieces of restored information are information about the downlink channel within M slots, that is, the K pieces of restored information can represent the downlink channel within M slots. K is a positive integer. For example, K may be equal to 1 or greater than 1. If K is greater than 1, K may be equal to N, greater than N, or less than N. K may be equal to M, greater than M, or less than M. K may be equal to M+L, greater than M+L, or less than M+L. That is, the number of channel estimation results input by the UE to the encoder network may or may not be equal to the number of pieces of response information output by the decoder network. This is more flexible.

[0164] In the present disclosure, a UE can perform joint compression on multiple channel estimation results by using an encoder network, and an access network device can restore N pieces of compressed information by using corresponding decoder networks. Therefore, the access network device needs to first determine the decoder network to be used. If the access network device sends first indication information to the UE to indicate a reference encoder network, the access network device can determine the reference decoder network corresponding to the reference encoder network. Alternatively, if the UE sends second indication information to the access network device to indicate a reference encoder network, the access network device can determine the reference decoder network corresponding to the reference encoder network. Alternatively, if the UE sends second indication information to the access network device to indicate a reference network, the access network device can determine the reference decoder network included in the reference network.

[0165] After determining the reference decoder network, the access network device can directly use the determined reference decoder network. In other words, the reference decoder network is the decoder network ultimately used by the access network device. Alternatively, after determining the reference decoder network, the access network device does not directly use the reference decoder network, but the characteristics of the decoder network ultimately used by the access network device may be determined based on the characteristics of the reference decoder network. For example, the input dimension of the decoder network used by the access network device may be determined based on the input dimension of the reference decoder network. For example, the input dimension of the decoder network used by the access network device is equal to the input dimension of the reference decoder network. Similarly, for example, the output dimension of the decoder network used by the access network device may also be determined based on the output dimension of the reference decoder network. For example, the output dimension of the decoder network used by the access network device is equal to the output dimension of the reference decoder network. For example, if the input of the decoder network used by the access network device is the same as the input of the reference decoder network, the difference between the output of the decoder network used by the access network device and the output of the reference decoder network is less than a threshold.

[0166] After determining the decoder network, the access network device can restore the N pieces of compressed information based on the decoder network. For example, the access network device inputs the N pieces of compressed information into the decoder network, and the decoder network outputs K pieces of restored information. Each of the K pieces of restored information corresponds to some or all of the N pieces of compressed information. The compressed information corresponding to different pieces of restored information may be the same, different, or not completely identical. The different pieces of restored information may correspond to the same parameters of the same compressed information, or different parameters of the same compressed information, or not completely identical parameters of the same compressed information. The fact that one piece of restored information corresponds to one piece of compressed information may be understood as the restoration information reflecting the characteristics of the compressed information, or the process of generating the restoration information being related to the compressed information, or the restoration information corresponding to all or some parameters of the compressed information.

[0167] For example, when N=3, the N pieces of compression information are compression information 1, compression information 2, and compression information 3, and all three pieces of compression information correspond to parameters A to E; when K=3, the K pieces of restoration information are restoration information 1, restoration information 2, and restoration information 3. Restoration information 1 may reflect parameters A and B corresponding to compression information 1, parameters A and C corresponding to compression information 2, and parameters D and E corresponding to compression information 3. Restoration information 2 may reflect parameters A, B, and C corresponding to compression information 1, parameters A and C corresponding to compression information 2, and parameters C and E corresponding to compression information 3. Restoration information 3 may reflect parameters D and E corresponding to compression information 1, parameters B, D, and E corresponding to compression information 2, and parameters A and B corresponding to compression information 3. In this case, different pieces of restoration information correspond to the same piece of compression information, and the parameters corresponding to different pieces of restoration information include the following cases: Different pieces of restoration information may correspond to the same parameters of the same compression information (e.g., restoration information 1 corresponds to parameters A and C of compression information 2, and restoration information 2 also corresponds to parameters A and C of compression information 1). Different pieces of restoration information may correspond to different parameters of the same compression information (e.g., restoration information 1 corresponds to parameters A and B of compression information 1, and restoration information 3 corresponds to parameters D and E of compression information 1). Different pieces of restoration information may also correspond to parameters that are not completely the same of the same compression information (e.g., restoration information 1 corresponds to parameters A and B of compression information 1, and restoration information 2 corresponds to parameters A, B, and C of compression information 1). In this example, for example, the parameters corresponding to the N pieces of compression information are all the same (i.e., all three pieces of compression information correspond to parameters A to E). In practice, the parameters corresponding to different pieces of compression information may be different or may not be completely the same. This is not a limitation here. Also, in this example, for example, the parameters corresponding to the K pieces of restoration information are all the same. In practice, the parameters corresponding to different pieces of reconstruction information may also be different or may not be exactly the same.For example, when K=2, reconstruction information 1 may correspond to N compressed information of M parameters A, and reconstruction information 2 no longer corresponds to parameter A.

[0168] Figure 8A shows an example of input / output information of the decoder network when N = 1. In Figure 8A, c represents the compressed information input by the decoder network,

number

[0169] The compressed information input by the network is represented as above. Optionally, c is

number

number

[0170] Also, Figure 8C shows an example of input / output information for the decoder network when N>1. In Figure 8C,

number

number

[0171] The compressed information input by the network is represented as above.

number

number

number

[0172] The K pieces of restoration information obtained by the decoder network can be considered as CSI, or PMI, or information having a function similar to CSI or PMI. Optionally, the decoder network may further process the K pieces of restoration information to obtain restored downlink channel information. In other words, the access network device can obtain restored downlink channel information based on the K pieces of restoration information. The downlink channel information is, for example, a downlink channel matrix or a parameter of the downlink channel matrix (e.g., a parameter such as a weight of the downlink channel matrix).

[0173] Sampling points corresponding to K pieces of reconstruction information are arranged in the M slots, and sampling points corresponding to N pieces of compressed information (or sampling points corresponding to M channel estimation results) are also arranged in the M slots. However, the number of sampling points corresponding to the K pieces of reconstruction information may or may not be equal to the number of sampling points corresponding to the N pieces of compressed information.

[0174] In addition, the time-domain positions of the sampling points corresponding to the K pieces of reconstruction information may be the same as, different from, or not exactly the same as the time-domain positions of the sampling points corresponding to the N pieces of compressed information. For example, the K pieces of reconstruction information correspond to three sampling points, with time-domain positions T1, T2, and T3, respectively. The N pieces of compressed information correspond to three sampling points, with time-domain positions T1, T4, and T3, respectively. It can be seen that the time-domain positions of the sampling points corresponding to the K pieces of reconstruction information are not exactly the same as the time-domain positions of the sampling points corresponding to the N pieces of compressed information. In another example, the K pieces of reconstruction information correspond to two sampling points, with time-domain positions T1 and T2, respectively. The N pieces of compressed information correspond to three sampling points, with time-domain positions T4, T5, and T6, respectively. It can be seen that the time-domain positions of the sampling points corresponding to the K pieces of reconstruction information are different from the time-domain positions of the sampling points corresponding to the N pieces of compressed information. In another example, the K pieces of reconstruction information correspond to three sampling points, with time-domain positions T1, T2, and T3, respectively. The N compressed information pieces correspond to three sampling points, whose time-domain positions are T1, T2, and T3, respectively. It can be seen that the time-domain positions of the sampling points corresponding to the K restored information pieces are the same as the time-domain positions of the sampling points corresponding to the N compressed information pieces.

[0175] In other words, the time domain positions of the sampling points corresponding to the K pieces of restoration information are located within the M slots, and the quantity of the sampling points corresponding to the K pieces of restoration information, the time domain positions within the M slots, etc. are not limited. In this way, for an access network device, the restoration process can be flexible.

[0176] Optionally, after obtaining the downlink channel information based on the K pieces of restoration information, the access network device may determine, based on the downlink channel information, one or more pieces of information, such as the number of streams to be used when data is transmitted to the UE, the modulation order to be used when data is transmitted to the UE, or the code rate of a channel carrying data (e.g., a physical downlink control channel (PDCCH)). In addition, the access network device may further determine, based on the downlink channel information, a precoding matrix to be used when data is transmitted to the UE, or the like. Alternatively, the access network device does not restore the downlink channel information based on the K pieces of restoration information, but directly uses the K pieces of restoration information. In this case, the access network device may determine, based on the K pieces of restoration information, one or more pieces of information, such as the number of streams to be used when data is transmitted to the UE, the modulation order to be used when data is transmitted to the UE, or the coding rate of a channel carrying data. In addition, the access network device may further determine, based on the K pieces of restoration information, a precoding matrix to be used when data is transmitted to the UE, or the like.

[0177] In the present disclosure, a UE may perform channel estimation on downlink channels within M slots, perform joint compression on the obtained channel estimation results, and report the channel estimation results to an access network device. Because joint compression is performed, correlation between downlink channels within different slots is fully utilized, and the access network device may restore N pieces of compressed information obtained by joint compression, thereby obtaining more accurate and effective downlink channel information. Furthermore, because joint compression is performed, the N pieces of compressed information may complement each other. For example, different pieces of compressed information may correspond to different parameters to reduce redundant information and correspondingly reduce transmission overhead. In addition, during joint compression, in addition to the M channel estimation results, historical information may also be considered, so that the compressed information can reflect the characteristics of the downlink channel within more slots, and the access network device may restore a more accurate downlink channel matrix.

[0178] The communication device provided in the present disclosure is described according to the above method embodiments.

[0179] The present disclosure provides a communications device. The communications device includes, for example, a processing unit and a transceiver unit (also referred to as a communications unit). The processing unit may be configured to implement the processing functions of the UE in the embodiment shown in Figure 5, and the transceiver unit may be configured to implement all or some of the transmission and reception functions of the UE in the embodiment shown in Figure 5. Alternatively, the processing unit may be configured to implement the processing functions performed by the access network device in the embodiment shown in Figure 5, and the transceiver unit may be configured to implement all or some of the transmission and reception functions of the access network device in the embodiment shown in Figure 5.

[0180] Optionally, the processing unit and / or the transceiver unit may be implemented by using virtual modules. For example, the processing unit may be implemented by using a software functional unit or a virtual device, and the transceiver unit may be implemented by using a software functional unit or a virtual device. Alternatively, the processing unit and / or the transceiver unit may be implemented by using a physical device (e.g., a circuit system and / or a processor). The following describes the case where the processing unit and the transceiver unit are implemented by using a physical device.

[0181] 9 is a diagram of the structure of a communication device according to the present disclosure. The communication device 900 may be the UE in the embodiment shown in FIG. 5, a circuit system of the UE, a circuit system usable in the UE, etc., and is configured to implement a method corresponding to the UE in the aforementioned method embodiment. Alternatively, the communication device 900 may be the access network device in the embodiment shown in FIG. 5, a circuit system of the access network device, a circuit system usable in the access network device, etc., and is configured to implement a method corresponding to the access network device in the aforementioned method embodiment. For specific functions, please refer to the description of the aforementioned method embodiment. For example, the circuit system is a chip system.

[0182] The communication device 900 includes one or more processors 901. The processor 901 may implement specific control functions. The processor 901 may be a general-purpose processor or a special-purpose processor, etc. For example, the processor 901 may include a baseband processor, a central processing unit, etc. The baseband processor may be configured to process communication protocols and communication data. The central processing unit may be configured to control the communication device 900, execute software programs, and / or process data. The different processors may be independent components or may be located in one or more processing circuits, for example, integrated into one or more application-specific integrated circuits.

[0183] Optionally, the communication device 900 includes one or more memories 902 configured to store instructions 904. The instructions 904 may be executed by a processor, thereby enabling the communication device 900 to perform the methods described in the preceding method embodiments. Optionally, the memory 902 may further store data. The processor and memory may be located separately or may be integrated.

[0184] Optionally, the communication device 900 may store instructions 903 (which may sometimes be referred to as code or programs), which may be executed on the processor, thereby enabling the communication device 900 to perform the methods described in the previous embodiments. The processor 901 may store data.

[0185] For example, the processing unit may be implemented using one or more processors 901, or the processing unit may be implemented using one or more processors 901 and one or more memories 902, or the processing unit may be implemented using one or more processors 901, one or more memories 902, and instructions 903.

[0186] Optionally, the communications device 900 may further include a transceiver 905 and an antenna 906. The transceiver 905 may also be referred to as a transceiver unit, a transceiver machine, a transceiver circuit, a transceiver machine, an input / output interface, etc., and is configured to implement transceiver functionality of the communications device 900 by using the antenna 906. For example, the transceiver unit is implemented by using the transceiver 905, or the transceiver unit is implemented by using the transceiver 905 and the antenna 906.

[0187] Optionally, communication device 900 may further include any one or more of the following components: a wireless communication module, an audio module, an external memory interface, an internal memory, a universal serial bus (USB) interface, a power management module, an antenna, a speaker, a microphone, an input / output module, a sensor module, a motor, a camera, a display, etc. It will be understood that in some embodiments, communication device 900 may include more or fewer components, or some components may be integrated, or some components may be separated. The components may be implemented by hardware, software, or a combination of software and hardware.

[0188] The processor 901 and transceiver 905 described in the present disclosure may be implemented in an integrated circuit (IC), an analog IC, a radio frequency integrated circuit (RFID), a mixed-signal IC, an application specific integrated circuit (ASIC), a printed circuit board (PCB), an electronic device, or the like. The communication apparatus described herein may be an independent device (e.g., an independent integrated circuit or a mobile phone) or may be part of a larger device (e.g., a module that can be incorporated into another device). For details, please refer to the descriptions of the UE and the access network device in the preceding embodiments. Details will not be repeated here.

[0189] The present disclosure provides a terminal device, which can be used in the aforementioned embodiments. The terminal device includes corresponding means, units, and / or circuits for implementing the functions of the UE in the embodiment shown in Figure 5. For example, the terminal device includes a transceiver module (also referred to as a transceiver unit) configured to support the terminal device in implementing transceiver functions, and a processing module (also referred to as a processing unit) configured to support the terminal device in processing signals.

[0190] The present disclosure further provides an access network device, which may be used in the aforementioned embodiments. The access network device includes corresponding means, units, and / or circuits for implementing the functions of the access network device in the embodiment shown in Figure 5. For example, the access network device includes a transceiver module (also referred to as a transceiver unit) configured to support the access network device in implementing a transmission function, and a processing module (also referred to as a processing unit) configured to support the access network device in processing signals.

[0191] All or part of the technical solutions provided in the present disclosure may be implemented by using software, hardware, firmware, or any combination thereof. When implementing embodiments using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded into a computer and executed, the procedures or functions according to the present disclosure are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, an access network device, a terminal device, an AI node, or another programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from a computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, or digital subscriber line (DSL)) or wireless (e.g., infrared, radio wave, or microwave) transmission. A computer-readable storage medium may be any available medium that can be accessed by a computer, or may be a data storage device, such as a server or data center, that incorporates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disk drives, magnetic tapes), optical media (e.g., digital video discs (DVDs)), semiconductor media, etc.

[0192] The above description is merely a specific implementation of the present disclosure and is not intended to limit the scope of protection of the present disclosure. Any modifications or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present disclosure shall fall within the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure shall be subject to the scope of protection of the claims. [Explanation of symbols]

[0193] 10. Communication Systems 20 RAN devices, access network devices 30 Communication equipment 900 Communication Equipment 901 processor 902 memory 903 Instructions 904 Command 905 Transceiver 906 Antenna

Claims

1. A communication method executed by a first device, comprising: obtaining M channel estimation results corresponding to M time units, where M is an integer greater than 1; performing joint compression on the M channel estimation results by using an encoder network to obtain N pieces of compressed information, where N is a positive integer; sending the N compressed information to an access network device; A communication method comprising: receiving configuration information from the access network device, the configuration information being used to configure the M time units; The communication method further comprises:

2. The M channel estimation results include a first channel estimation result, and the first channel estimation result is: a channel estimation result obtained by performing measurements based on a received downlink reference signal; a processed result obtained by processing a channel estimation result obtained by performing measurements based on a received downlink reference signal; or Channel estimation results obtained by prediction The communication method according to claim 1, wherein the method is one of:

3. The communication method of claim 1 , wherein each of the N pieces of compressed information corresponds to the M channel estimation results.

4. performing joint compression on the M channel estimation results by using an encoder network to obtain N compressed information; performing joint compression on the M channel estimation results and history information by using the encoder network to obtain the N compressed information, wherein the history information includes channel estimation results corresponding to a time unit prior to the M time units; The communication method of claim 1, comprising:

5. The communication method of claim 4 , wherein each of the N pieces of compressed information corresponds to the M channel estimation results and the history information.

6. The configuration information includes the following: a starting time-domain position of said M time units; an end time domain position of said M time units; the duration of said M time units, the number of said M time units, a time-domain location of a first sampling point within the M time units; a sampling period of the M time units; the number of sampling points in the M time units, or Time domain positions of the sampling points in the M time units 10. The communication method of claim 1, comprising one or more of:

7. The communication method comprises: receiving first indication information from the access network device, the first indication information indicating parameter information of a reference encoder network or an index of a reference encoder network; or sending second indication information to the access network device, the second indication information indicating parameter information of a reference encoder network or an index of a reference encoder network; further comprising the reference encoder network is used to determine the encoder network; The communication method according to claim 1.

8. A communication method performed by a second device, comprising: receiving N pieces of compressed information from a terminal device, where N is a positive integer, the N pieces of compressed information correspond to M channel estimation results, and the M channel estimation results correspond to M time units; Decompressing the N pieces of compressed information by using a decoder network to obtain K pieces of restored information, where the K pieces of restored information are M time units of downlink channel information, where K is a positive integer and M is an integer greater than 1; The communication method includes: transmitting configuration information to the terminal device, the configuration information being used to configure the M time units; The communication method further comprises:

9. The communication method according to claim 8 , wherein each of the K pieces of restoration information corresponds to the N pieces of compression information.

10. The configuration information includes the following: a starting time-domain position of said M time units; an end time domain position of said M time units; the duration of said M time units, the number of said M time units, a time-domain location of a first sampling point within the M time units; a sampling period of the M time units; the number of sampling points in the M time units, or 9. The communication method of claim 8, including one or more of the time domain locations of the sampling points in the M time units.

11. The communication method comprises: sending first indication information to the terminal device, wherein the first indication information indicates parameter information of a reference encoder network or an index of a reference encoder network; or receiving second indication information from the terminal device, the second indication information indicating parameter information of a reference encoder network selected by the terminal device or an index of a reference encoder network selected by the terminal device; further comprising The reference encoder network and the reference decoder network belong to the same reference network, and the reference decoder network is used to determine the decoder network. The communication method according to claim 8.

12. A communication device configured to implement a communication method according to any one of claims 1 to 7.

13. A communication device comprising a processor and a memory, the memory being connected to the processor, the processor being configured to perform the communication method of any one of claims 1 to 7.

14. 8. A computer-readable storage medium configured to store a computer program, the computer program enabling the computer to perform the communication method of any one of claims 1 to 7, when run on the computer.

15. A communication system comprising an apparatus for performing the communication method according to any one of claims 1 to 7 and an apparatus for performing the communication method according to any one of claims 8 to 11.

16. A communication device configured to implement a communication method according to any one of claims 8 to 11.

17. A communication device comprising a processor and a memory, wherein the memory is connected to the processor, and the processor is configured to execute the communication method described in any one of claims 8 to 11.

18. A computer-readable storage medium configured to store a computer program, which, when run on a computer, enables the computer to perform the communication method described in any one of claims 8 to 11.

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