Communication method and apparatus

Abstract

The present disclosure relates to a communication method and an apparatus. A terminal device obtains M first downlink channel subdata, each of which corresponds to one data space among the M data spaces. For an i-th first downlink channel subdata among the M first downlink channel subdata, the terminal device determines first information corresponding to the i-th first downlink channel subdata based on a first dictionary corresponding to the i-th data space, and M first information is determined in total. The terminal device transmits first indication information to indicate the M first information. Different data spaces can represent different channel environment information. The terminal device feeds back the first information corresponding to the different data spaces, so that the access network device can determine a correspondence relationship between the first information and the environment information. In this case, the first information fed back by the terminal device can reflect an actual communication environment, and the accuracy of the first information fed back by the terminal device is improved.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to Chinese Patent Application No. 202111663303.8, entitled "Communication Method and Apparatus," filed with the China State Intellectual Property Office on December 31, 2021, the entire contents of which are incorporated herein by reference.

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

[0003] The 5th generation (5G) mobile communication system has higher requirements for system capacity and spectral efficiency, etc. In the 5G communication system, the application of massive multiple-input multiple-output (massive-MIMO) technology plays an important role in improving the spectral efficiency of the system. The base station can use the massive-MIMO technology to provide high-quality services to more user equipment (UE) simultaneously. The key step is for the base station to precode the downlink data of multiple UEs. Through precoding, spatial multiplexing can be implemented, the interference between UEs can be reduced, the signal to interference plus noise ratio (SINR) at the receiver can be increased, and the system throughput can be improved. In order to more accurately precode the downlink data of the UE, the base station may obtain channel state information (CSI) of the downlink channel, reconstruct the downlink channel based on the CSI, and use the reconstructed downlink channel to determine a precoding matrix to perform precoding. Therefore, how to make the CSI fed back by the UE more accurate is a technical problem worth studying. Summary of the Invention

[0004] The present disclosure provides a communication method and apparatus for improving the accuracy of CSI fed back by a UE. [Means for solving the problem]

[0005] According to a first aspect, a first communication method is provided. The method can be executed on a terminal device side. The method can be executed using software, hardware, or a combination of software and hardware. For example, the method is executed by a terminal device, a circuit system, or a large device including a terminal device. The circuit system can implement the function of the terminal device. The method includes the steps of: acquiring M pieces of first downlink channel subdata, each of the first downlink channel subdata corresponding to a data space among the M data spaces, where M is an integer greater than 1; for an i-th first downlink channel subdata among the M pieces of first downlink channel subdata, determining first information corresponding to the i-th first downlink channel subdata based on a first dictionary corresponding to the i-th data space among the M data spaces, where a total of M pieces of first information are determined, where i is an integer from 1 to M, the i-th first downlink channel subdata corresponds to the i-th data space, the first dictionary includes a plurality of elements, and the first information corresponding to the i-th first downlink channel subdata corresponds to P elements among the plurality of elements, where P is a positive integer; and transmitting first indication information, where the first indication information indicates the M pieces of first information.

[0006] In the present disclosure, each of the M first downlink channel subdata acquired by the terminal device may correspond to one data space in the M data spaces, and the first information corresponding to all the first downlink channel subdata may be determined based on dictionaries corresponding to different data spaces. The different data spaces may represent different location information, that is, the different data spaces may represent different channel environment information. The terminal device feeds back the first information corresponding to the different data spaces, so that the access network device can determine the corresponding relationship between the first information and the environment information. In this case, the first information fed back by the terminal device may reflect the actual communication environment, and the accuracy of the first information fed back by the terminal device is improved. The access network device can reconstruct an accurate downlink channel based on the first information fed back by the terminal device.

[0007] In an optional embodiment, the first indication information indicates M identifiers of the first information, and the step of transmitting the first information includes the step of transmitting the M identifiers of the first information in a first order, the first order being an arrangement order of the M data spaces. The first order specifies that the terminal device transmits the identifier of the first information corresponding to a specific data space first, and transmits the identifier of the first information corresponding to a specific data space next. For the terminal device and the access network device, the first order is known. Thus, after receiving the identifiers of the M first information, the access network device can further determine the correspondence relationship between the data space and the identifier of the first information, so as to avoid correspondence error.

[0008] In an optional embodiment, the first order is a predefined order, or the second indication information is received, and the second indication information indicates the first order, or the first order is determined, and the third indication information is sent, and the third indication information indicates the first order. For example, the first order is a predefined order in a protocol, and the terminal device and the access network device can determine the first order according to the protocol. Alternatively, the first order may be preconfigured in the terminal device and the access network device. Alternatively, the first order may be determined by the access network device. After determining the first order, the access network device may send the second indication information to the terminal device, whereby the terminal device can determine the first order based on the second indication information. Alternatively, the first order may be determined by the terminal device. After determining the first order, the terminal device may send the third indication information to the access network device, whereby the access network device can determine the first order based on the third indication information. It can be known that the method of determining the first order is flexible.

[0009] In an optional embodiment, the M first downlink channel subdata are obtained based on the first downlink channel data, where the first downlink channel data is a preprocessing result, or the first downlink channel data includes F consecutive data columns in the preprocessing result, or the first downlink channel data is compressed information obtained by compressing the preprocessing result, and the preprocessing result is obtained by preprocessing the second downlink channel data. The preprocessing result of the second downlink channel data may be directly used as the first downlink channel data, and there is no need to perform excessive processing on the preprocessing result. This is simple. Alternatively, it is considered that in the frequency domain direction (delay domain), generally, energy is mainly concentrated around delay=0, and energy in another domain can be basically ignored. Therefore, the terminal device may select F consecutive columns on both sides of delay=0 as the first downlink channel data, and the coefficients of the remaining parts may be 0 by default. In this case, the complexity of processing the first downlink channel data can be reduced. Alternatively, the pre-processing result may be compressed to obtain the first downlink channel data, thereby reducing the complexity of processing the first downlink channel data. The process of pre-processing the downlink channel data may, for example, include performing a space-frequency joint projection on the downlink channel data.

[0010] In an optional embodiment, the division method of the M data spaces is predefined, or the fourth indication information is received, and the fourth indication information indicates the division method of the M data spaces, or the division method of the M data spaces is determined, and the fifth indication information is sent, and the fifth indication information indicates the division method of the M data spaces. For example, if the division method of the M data spaces is predefined in a protocol, both the terminal device and the access network device may determine the division method of the M data spaces according to the protocol. Alternatively, the division method of the M data spaces is determined by the access network device. The access network device may send the fourth indication information to the terminal device, so that the terminal device can determine the division method of the M data spaces based on the fourth indication information. Alternatively, the division method of the M data spaces may be determined by the UE. The UE may send the fifth indication information to the access network device, so that the access network device can determine the division method of the M data spaces based on the fifth indication information. It can be known that the method of dividing the data spaces is flexible.

[0011] According to a second aspect, a second communication method is provided. The method can be performed on the access network device side. The method can be performed using software, hardware, or a combination of software and hardware. For example, the method is performed by an access network device, a large device including an access network device, or a circuit system. The circuit system can perform the function of the access network device. Alternatively, the method is performed by an access network device or a network element of an access network device with the assistance of an AI module independent of the access network device. This is not limited. For example, the access network device is an access network device, such as a base station. The method includes the steps of receiving first instruction information, where the first instruction information indicates M pieces of first information, where M is an integer greater than 1; reconstructing i-th second downlink channel subdata based on a first dictionary corresponding to an i-th data space in the M data spaces, for an i-th first information among the M pieces of first information, where a total of M pieces of second downlink channel subdata are obtained, the i-th first information corresponds to the i-th data space, where i is an integer from 1 to M, the first dictionary includes a plurality of elements, and the first information corresponding to the i-th second downlink channel subdata corresponds to P elements in the plurality of elements; and reconstructing downlink channel information based on the M pieces of second downlink channel subdata.

[0012] In an optional embodiment, the step of receiving the first indication information includes a step of receiving identifiers of M pieces of first information in a first order, the first order being an arrangement order of the M pieces of data space.

[0013] In optional embodiments, the first order is a predefined order, or second instruction information is sent and the second instruction information indicates the first order, or third instruction information is received and the third instruction information indicates the first order.

[0014] In an optional embodiment, the M data spaces correspond to M dictionaries, each data space corresponds to one dictionary, the M data spaces all correspond to the same dictionary, or the number of dictionaries corresponding to the M data spaces is greater than 1 and less than M. In other words, the data spaces correspond to the dictionaries one-to-one, which can improve the accuracy of the first information determined based on the dictionary, all data spaces can correspond to one dictionary uniformly, which can make the samples used for training to obtain the dictionaries richer, which can make the contents contained in the dictionaries more detailed, or the number of dictionaries corresponding to the data spaces can be less than the number of data spaces, for example, one dictionary can correspond to multiple data spaces, which can reduce the complexity to a certain extent.

[0015] In an optional embodiment, the step of reconstructing the downlink channel information based on the M number of second downlink channel subdata includes the steps of obtaining compressed information based on the M number of second downlink channel subdata, and obtaining the downlink channel information based on the compressed information.

[0016] In optional embodiments, the division method of the M data spaces is predefined, or fourth instruction information is sent, and the fourth instruction information indicates the division method of the M data spaces, or fifth instruction information is received, and the fifth instruction information indicates the division method of the M data spaces.

[0017] For the technical effects provided by the second aspect or various optional implementations of the second aspect, please refer to the description of the technical effects of the first aspect or corresponding implementations.

[0018] According to a third aspect, a communication device is provided. The communication device may perform the method according to the first aspect. The communication device has the functionality of a terminal device. In an optional embodiment, the device may include a one-to-one corresponding module for performing the method / operation / step / action described in the first aspect. The module may be a hardware circuit, or may be software, or may be implemented by a hardware circuit in combination with software. In an optional embodiment, the communication device includes a baseband device and a radio frequency device. In another optional embodiment, 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 may perform a transmitting function and a receiving function. When the transceiver unit performs a transmitting function, the transceiver unit may be referred to as a transmitting unit (sometimes referred to as a transmitting module). When the transceiver unit performs a receiving function, 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 this functional module is referred to as a transceiver unit. This functional module may perform transmitting and receiving functions, or the transmitting unit and the receiving unit may be different functional modules, and the transceiver unit is a collective term for these functional modules.

[0019] The processing unit is configured to obtain M first downlink channel subdata, each first downlink channel subdata corresponding to one data space in the M data spaces, where M is an integer greater than 1. For an i-th first downlink channel subdata in the M first downlink channel subdata, the processing unit is further configured to determine first information corresponding to the i-th first downlink channel subdata based on a first dictionary corresponding to the i-th data space in the M data spaces, where a total of M first information is determined, where i is an integer from 1 to M, the i-th first downlink channel subdata corresponds to the i-th data space, the first dictionary includes a plurality of elements, and the first information corresponding to the i-th first downlink channel subdata corresponds to P elements in the plurality of elements, where P is a positive integer. The transceiver unit is configured to transmit first indication information, where the first indication information indicates the M first information.

[0020] In another example, a communication device includes a processor coupled to a memory and configured to execute instructions in the memory to perform a method according to the first aspect. Optionally, the communication device further includes other components, such as an antenna, an input / output module, and an interface. These components may be hardware, software, or a combination of software and hardware.

[0021] According to a fourth aspect, a communication device is provided. The communication device may perform the method according to the second aspect. The communication device has a function 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 embodiment, the device may 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 may be software, or may be implemented by a hardware circuit in combination with software. In an optional embodiment, the communication device includes a baseband device and a radio frequency device. In another optional embodiment, 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 the embodiment of the transceiver unit, please refer to the relevant description of the third aspect.

[0022] The transceiver unit is configured to receive first indication information, where the first indication information indicates M pieces of first information, where M is an integer greater than 1. For an i-th first information among the M pieces of first information, the processing unit is configured to reconstruct an i-th second downlink channel subdata based on a first dictionary corresponding to an i-th data space among the M pieces of data spaces, such that a total of M pieces of second downlink channel subdata are obtained, where the i-th first information corresponds to the i-th data space, where i is an integer from 1 to M, the first dictionary includes a plurality of elements, and the first information corresponding to the i-th second downlink channel subdata corresponds to P elements among the plurality of elements. The processing unit is further configured to reconstruct the downlink channel information based on the M pieces of second downlink channel subdata.

[0023] In another example, a communication device includes a processor coupled to a memory and configured to execute instructions in the memory to perform a method according to the second aspect. Optionally, the communication device further includes other components, such as an antenna, an input / output module, and an interface. These components may be hardware, software, or a combination of software and hardware.

[0024] According to a fifth aspect, there is provided a computer readable storage medium configured to store a computer program or instructions which, when run, perform the method according to the first and / or second aspect.

[0025] According to a sixth aspect there is provided a computer program product comprising instructions, when the computer program product runs on a computer, for performing a method according to the first aspect and / or the second aspect.

[0026] According to a seventh aspect, a chip system is provided. The chip system includes a processor and may further include a memory, the chip system being configured to perform the method according to the first aspect and / or the second aspect. The chip system may include a chip, or may include a chip and another separate component.

[0027] 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 description of the drawings]

[0028] [Figure 1] FIG. 1 is a diagram of a communication system. [Diagram 2] 1 is a flowchart of a CSI feedback mechanism. [Diagram 3] FIG. 1 is a diagram of an application scenario. [Figure 4A]A diagram of an application framework for AI in a communication system. [Figure 4B] A diagram of an application framework for AI in a communication system. [Figure 4C] A diagram of an application framework for AI in a communication system. [Figure 4D] A diagram of an application framework for AI in a communication system. [Figure 4E] A diagram of an application framework for AI in a communication system. [Diagram 5] 1 is a flowchart of a communication method. [Figure 6] A diagram of a dictionary. [Figure 7] A diagram of a communication method used when both the UE and the access network device process compressed information. [Figure 8] 11 is a flowchart of another communication method. [Figure 9A] FIG. 1 is a diagram of the network training and inference stages. [Figure 9B] FIG. 1 is a diagram of the network training phase. [Figure 9C] FIG. 1 is a diagram of the network training phase. [Figure 9D] FIG. 1 is a diagram of the network training phase. [Figure 10] 11 is a flowchart of yet another communication method. [Figure 11] FIG. 2 is another diagram of the network training and inference stages. [Figure 12] FIG. 2 is a block diagram of a communication device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029] In order to make the objectives, technical solutions and advantages of the present disclosure clearer, the following will further describe the present disclosure in detail with reference to the accompanying drawings.

[0030] The technology provided in the present 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 via 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 (also referred to as new radio (NR)) access technology, a wireless fidelity (Wi-Fi) system, a cellular system related to the 3rd generation partnership project (3GPP), a communication system supporting a collection of multiple radio technologies, or a future-oriented evolution system, etc. This is not limited thereto.

[0031] The following provides a detailed description of the terminal device and the RAN in FIG. 1 separately.

[0032] 1. Terminal Device The terminal device may simply be called a terminal. The terminal device may be a device with wireless transceiver capabilities. The terminal device may be mobile or fixed. The terminal device may be located on land, including indoor or outdoor, or handheld or vehicle-mounted, on water (e.g., on a ship), or in the air (e.g., on an aircraft, balloon, or satellite). The terminal device may include a mobile phone, a tablet computer, a computer with wireless transceiver capabilities, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device of industrial control, a wireless terminal device of self driving, a wireless terminal device of remote medical, a wireless terminal device of smart grid, a wireless terminal device of transportation safety, a wireless terminal device of smart city, and / or a wireless terminal device of smart home. Alternatively, the terminal device may be a cellular telephone, a cordless telephone, a session initiation protocol (SIP) telephone, a wireless local loop (WLL) station, a personal digital assistant (PDA), a handheld device or a computing device with wireless communication capabilities, an in-vehicle device, a wearable device, a terminal device in the 5th generation (5G) network, or 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, the terminal device may communicate with an access network device supporting LTE, or may communicate with an access network device supporting 5G, or may implement dual connectivity to an access network device supporting LTE and an access network device supporting 5G. This is not limited in the present disclosure.

[0033] In the present disclosure, the apparatus configured to perform the functions of the terminal device may be a terminal device, or may be an apparatus capable of supporting the terminal device in performing its functions, such as a chip system, a hardware circuit, a software module, or a hardware circuit and a software module. The apparatus may be incorporated in the terminal device or adapted for use in the terminal device. In the technical solution provided in the present disclosure, an example in which the apparatus configured to perform the functions of the terminal device is a terminal device, and the terminal device is a UE, is used to explain the technical solution provided in the present disclosure.

[0034] In the present disclosure, a chip system may include a chip, or may include a chip and another separate component.

[0035] 2.RAN The RAN may include one or more RAN devices, such as the RAN device 20. The interface between the RAN device and the terminal device may be a Uu interface (or called an air interface). In future communications, the names of these interfaces may remain unchanged or may be replaced by other names. This is not a limitation of the present disclosure.

[0036] A RAN device is a node or device that allows a terminal device to access a wireless network. A RAN device may also be referred to as a network device or base station. RAN devices include, but are not limited to, a base station, a 5G generation NodeB (gNB), an evolved NodeB (eNB), a radio network controller (RNC), a NodeB (NodeB, NB), a base station controller (BSC), a base transceiver station (BTS), a home base station (e.g., home evolved NodeB or home NodeB, HNB), a baseband unit (BBU), a transmitting and receiving 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), or a radio controller in a cloud radio access network (CRAN) scenario, 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, or an access network device in a future evolved public land mobile network (PLMN), etc.

[0037] In the present disclosure, the apparatus configured to perform the functions of an access network device may be an access network device, or may be an apparatus capable of supporting the access network device in performing its functions, such as a chip system, a hardware circuit, a software module, or a hardware circuit and a software module. The apparatus may be incorporated in the access network device or adapted for use in the access network device. In the technical solution provided in the present disclosure, an example in which the apparatus configured to perform the functions of an access network device is an access network device, and the access network device is a base station, is used to explain the technical solution provided in the present disclosure.

[0038] (1) Protocol Layer Structure The communication between the access network device and the terminal device complies with a specified 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, or a physical (PHY) layer, 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, and a physical layer, etc.

[0039] The protocol layer structure between the access network device and the terminal device may 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 forward information from the core network device to the terminal device, or is used by the access network device to forward information from the terminal device to the core network device. In this case, it may be considered that there is a logical interface between the terminal device and the core network device. Optionally, the access network device may forward information between the terminal device and the core network device in a transparent transmission manner. For example, the NAS message may be mapped to or included in the RRC signaling as an element of the RRC signaling.

[0040] Optionally, the protocol layer structure between the access network device and the terminal device may further include an artificial intelligence (AI) layer configured to transmit data related to AI functions.

[0041] (2) Central unit (CU) and distributed unit (DU) The RAN device may include a CU and a DU. This design may be referred to as CU and DU separation. Multiple DUs may be centrally controlled by one CU. For example, an interface between a CU and a DU may be referred to as an F1 interface. A control panel (CP) interface may be F1-C, and a user panel (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 classified according to the protocol layers of a wireless network. For example, the functions of the PDCP layer and protocol layers above the PDCP layer (such as the RRC layer and the SDAP layer) are configured in the CU, and the functions of the protocol layers below the PDCP layer (such as 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.

[0042] The above-described division of processing functions of the CU and DU based on protocol layers is merely an example, and the division may alternatively be performed in other manners. For example, the CU or DU may have more protocol layer functions by division, and in another example, the CU or DU may alternatively have a part of the processing functions of the protocol layers by division. In one design, a part of the functions of the RLC layer and the functions of the protocol layers above the RLC layer are configured in the CU, and the remaining functions of the RLC layer and the functions of the protocol layers below the RLC layer are configured in the DU. In another design, the division of the functions of the CU or DU may alternatively be performed based on service type or other system requirements. For example, the division may be performed based on latency. Functions whose processing time needs to meet the latency requirements are configured in the DU, and functions whose processing time does not need to meet the latency requirements are configured in the CU.

[0043] Optionally, the CU may have one or more functions of a core network, for example, the CU may be located on the network side to facilitate centralized management.

[0044] Optionally, the radio unit (RU) of the DU is located remotely. The RU has radio frequency functions. For example, the DU and the RU may be divided at the PHY layer. For example, the DU may perform the functions of the upper layer of the PHY layer, and the RU may perform the functions of the lower layer of the PHY layer. When the PHY layer is used for transmission, the functions of the PHY layer may include at least one of the following functions: adding cyclic redundancy check (CRC) bits, channel coding, rate matching, scrambling, modulation, layer mapping, precoding, resource mapping, physical antenna mapping, or radio frequency transmission. When the PHY layer is used for reception, the functions of the PHY layer may include at least one of the following functions: CRC checking, channel decoding, de-rate matching, descrambling, demodulation, layer demapping, channel detection, resource demapping, physical antenna demapping, or radio frequency reception. The functions of the upper layer of the PHY layer may include some of the functions of the PHY layer. Some of the functions are closer to the MAC layer. The functions of the lower layer 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 functions. For example, the functions of the upper layer of the PHY layer may include CRC bit addition, channel coding, rate matching, scrambling, modulation, and layer mapping, and the functions of the lower layer of the PHY layer may include precoding, resource mapping, physical antenna mapping, and radio frequency transmission. Alternatively, the functions of the upper layer of the PHY layer may include CRC bit addition, channel coding, rate matching, scrambling, modulation, layer mapping, and precoding, and the functions of the lower layer of the PHY layer may include resource mapping, physical antenna mapping, and radio frequency transmission. For example, the functions of the upper layer of the PHY layer may include CRC checking, channel decoding, de-rate matching, decoding, demodulation, and layer demapping, and the functions of the lower layer of the PHY layer may include channel detection, resource demapping, physical antenna demapping, and radio frequency reception.Alternatively, the functions of the upper layer of the PHY layer may include CRC checking, channel decoding, de-rate matching, decoding, demodulation, layer demapping, and channel detection, and the functions of the lower layer of the PHY layer may include the functions of resource demapping, physical antenna demapping, and radio frequency reception.

[0045] Optionally, the functions of the CU may be further divided. Specifically, the control plane and user plane of the CU are separated and implemented by different entities, namely, a control plane CU entity (CU-CP entity) and a user plane CU entity (CU-UP entity). The CU-CP entity and the CU-UP entity may be separately coupled or connected to the DU to jointly perform the functions of the RAN device.

[0046] In the above architecture, the signaling generated by the CU may be transmitted to the terminal device via the DU, or the signaling generated by the terminal device may be transmitted to the CU via the DU. For example, the signaling at the RRC layer or the PDCP layer may be finally processed as the signaling at the physical layer and transmitted to the terminal device, or converted from the signaling received at the physical layer. In this architecture, the signaling at the RRC layer or the PDCP layer may be considered to be transmitted via the DU, or transmitted via the DU and the RU.

[0047] 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. Different entities may exist in different forms, this is not limited. For example, the DU, CU, CU-CP, and CU-UP are software modules, and the RU is a hardware structure. These modules and the methods performed by these modules are also within the protection scope 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 the near real-time RIC described below. The methods performed by the modules are also within the protection scope of the present disclosure.

[0048] Please note that since the network devices in this disclosure are mainly access network devices, hereinafter, unless otherwise specified, "network device" may refer to "access network device".

[0049] It should be understood that the number of devices in the communication system shown in Figure 1 is used only as an example, and the present disclosure is not limited thereto. In practical applications, the communication system may further include more terminal devices and more RAN devices, and may further include other devices, such as core network devices and / or nodes configured to perform artificial intelligence functions.

[0050] The network architecture shown in FIG. 1 is applicable to communication systems of various radio access technologies (RATs), such as a 4G communication system, or a 5G (or called new radio (NR)) communication system, or a transitional system between an LTE communication system and a 5G communication system, or a future communication system, such as a 6G communication system. The transitional system may also be called a 4.5G 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 are also applicable to similar technical problems.

[0051] In addition to communication between an access network device and a terminal device, the method provided in the present disclosure 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, such as communication between a first terminal device and a second terminal device in a sidelink (SL), without being limited thereto. The present disclosure is described using communication between a network device and a terminal device as an example.

[0052] When transmitting data to the terminal device, the access network device may perform precoding based on channel state information (CSI) fed back by the terminal device. In order to facilitate the understanding of the present disclosure, the following briefly describes some technical terms in the present disclosure.

[0053] 1. Precoding Technology When the channel state information is known, the access network device may process the signal to be transmitted using a precoding matrix that matches the channel condition. The precoded signal to be transmitted can be adapted to the channel using this technique, which improves the quality of the signal received by the terminal device (e.g., signal to interference plus noise ratio, SINR) and improves the system throughput. The precoding technique is used, which allows the transmitting device (e.g., access network device) and multiple receiving devices (e.g., terminal devices) to effectively perform transmission on the same time-frequency resource, i.e., multiple user multiple-input multiple-output (MU-MIMO) is effectively implemented. The precoding technique is used, which allows the transmitting device (e.g., access network device) and multiple receiving devices (e.g., terminal devices) to effectively perform multiple data stream transmission on the same time-frequency resource, i.e., single user multiple-input multiple-output (SU-MIMO) is effectively implemented. Please note that the relevant description of the precoding technology is merely an example for easy understanding, and is not intended to limit the scope of protection of the present disclosure. In a specific implementation process, the transmission device may alternatively perform precoding in another manner. For example, when channel information (such as, but not limited to, a channel matrix) cannot be obtained, precoding is performed using a pre-configured precoding matrix or a weighting processing method. For the sake of brevity, the specific content is not described again in this specification.

[0054] 2. CSI feedback The CSI feedback may also be referred to as a CSI report. In a wireless communication system, the CSI feedback is a report by a receiver (e.g., a terminal device) of data (e.g., but not limited to, data carried on a physical downlink shared channel (PDSCH)) to a transmitter (e.g., an access network device) of information used to describe channel attributes of a communication link. For example, the CSI report includes one or more of information such as a downlink channel matrix, a precoding matrix indicator (PMI), a rank indicator (RI), or a channel quality indicator (CQI). The foregoing contents included in the above-mentioned CSI are merely illustrative examples and do not constitute any limitation to the present disclosure. The CSI may include one or more of the foregoing described contents, or may include information different from the foregoing described contents and used to represent the CSI. This is not limited in the present disclosure.

[0055] 3.Neural network (NN). Neural networks are a specific embodiment of machine learning technology. According to the universal approximation theorem, neural networks can theoretically approximate any continuous function, which gives them the ability to learn any mapping. In traditional communication systems, communication modules need to be designed with rich expertise. However, deep learning communication systems based on neural networks can automatically discover implicit pattern structures from multiple data sets, establish mapping relationships between data, and obtain better performance than traditional modeling methods.

[0056] For example, a deep neural network (DNN) is a neural network with multiple layers. Based 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 in this disclosure.

[0057] 4. Auto-encoder (AE) network, or AE for short. The AE network may include an encoder and a corresponding decoder. For example, the encoder and / or the decoder are implemented using a neural network (such as a DNN). In this case, the encoder may be called an encoder network, and the decoder may be called a decoder network. For example, in the AE network, the encoder and the corresponding decoder may be obtained by joint training. The encoder and the decoder obtained by training may be used to encode and decode information.

[0058] In this disclosure, unless otherwise specified, the number of nouns refers to "singular or plural nouns", i.e., "one or more". "At least one" means one or more, "multiple" means two or more, and "and / or" is an association relationship for describing associated objects, and indicates that three relationships may exist. For example, A and / or B may indicate the following 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 describing features, the symbol " / " may represent an "or" relationship between associated objects. For example, A / B indicates A or B. When describing operations, the symbol " / " may further represent division. In addition, in this disclosure, the symbol "x" may also be replaced with the symbol "*".

[0059] Ordinals such as "first" and "second" in the present disclosure are used to distinguish between multiple objects, and are not intended to limit the size, content, order, time sequence, application scenario, priority, or importance 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. In addition, this type of name does not indicate the different size, transmission mode, instruction content, priority, application scenario, or importance of the two instruction information.

[0060] In a possible implementation, the CSI feedback mechanism uses the procedure shown in FIG.

[0061] S21: The base station sends signaling, and correspondingly, the UE receives signaling from the base station.

[0062] The signaling is used to configure channel measurement information, for example, the signaling informs the UE of at least one of the following: 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, and reporting condition of the number of measurements, etc.

[0063] S22: The base station sends a reference signal to the UE, and in response, the UE receives the reference signal from the base station.

[0064] The UE measures the reference signal to obtain the CSI.

[0065] S23: The UE transmits the CSI to the base station, and in response, the base station receives the CSI from the UE.

[0066] S24: The base station sends data to the UE based on the CSI, and correspondingly, the UE receives data from the base station.

[0067] The base station determines a precoding matrix based on the CSI, and uses the precoding matrix to perform precoding on the data to be transmitted to the UE. The data transmitted by the base station to the UE is carried on a downlink channel, for example, a PDSCH.

[0068] When the accuracy of the CSI fed back by the UE is higher, the information is richer, and the downlink channel reconstructed by the base station based on the CSI is more accurate. In this case, the precoding matrix determined by the base station is more accurate, the downlink spatial multiplexing performance is better, the received signal-to-interference-and-noise ratio of the UE is higher, and the system throughput is higher. Meanwhile, as the size of the antenna array of the MIMO system increases continuously, the number of antenna ports that can be supported also increases. Since the size of the complete downlink channel matrix is ​​directly proportional to the number of antenna ports, in a massive MIMO system, a huge feedback overhead is required to make the CSI fed back by the UE have a high accuracy. Due to the huge feedback overhead, the resources available for data transmission are reduced. As a result, the system capacity is reduced. Therefore, in order to improve the system capacity, a method for reducing the CSI feedback overhead needs to be studied. Feeding back the CSI based on a bi-domain compression codebook is a method that can effectively reduce the feedback overhead.

[0069] The two-domain compression codebook is generally designed based on factors such as the expected antenna panel configuration and the number of subbands. However, in a real communication environment, the channel environment is complex and variable, and the real antenna panel configuration is diverse, so the codebook determined for a fixed antenna panel configuration and the number of subbands may not necessarily meet the real communication environment, resulting in a decrease in the accuracy of the CSI fed back by the UE. Therefore, how to make the CSI fed back by the UE more accurate is a technical problem worth studying.

[0070] In consideration of this, the technical solution of the present disclosure is provided. In the present disclosure, each of the M first downlink channel subdata acquired by the UE may correspond to one data space in the M data spaces, and the first information corresponding to all the first downlink channel subdata may be determined based on dictionaries corresponding to different data spaces. The different data spaces may represent different location information, that is, the different data spaces may represent different channel environment information. The UE feeds back the first information corresponding to the different data spaces, so that the access network device can determine the correspondence between the first information and the environment information. In this case, the first information fed back by the UE may reflect the actual communication environment, and the accuracy of the first information fed back by the UE is improved. The access network device can reconstruct an accurate downlink channel based on the first information fed back by the UE.

[0071] 3 shows a communication network architecture in a communication system 10 provided in the present disclosure. Any embodiment provided hereafter is applicable to this 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.

[0072] The present disclosure may relate to machine learning technology. Machine learning technology is a specific embodiment of AI technology. For ease of understanding, the following describes AI technology. It can be understood that the description is not intended to limit the present disclosure.

[0073] AI is a technology that mimics the human brain to perform complex calculations. With improvements in data storage and capabilities, AI is being increasingly applied.

[0074] 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 perform AI functions. The AI ​​network element may be directly connected to the access network device, or indirectly connected to the access network device through a third-party network element. Optionally, the third-party network element may be a core network element. Alternatively, the AI ​​entity may be configured or disposed in another network element in the communication system to perform AI-related operations. The AI ​​entity may also be referred to as an AI module, an AI unit, or by another name, and is mainly configured to perform 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, or a network management device (operation, administration and maintenance, OAM), etc. In this case, the network element that performs the AI ​​function is a network element with built-in AI functions.

[0075] In this disclosure, AI functions may include at least one of the following: data collection, model training (or model learning), model information release, model inference (or referred to as model inference, inference, prediction, etc.), model monitoring or model checking, or inference result release, etc. AI functions may also be referred to as AI (related) operations or AI-related functions.

[0076] In the present disclosure, an OAM network element (a network management device of a core network device) is configured to operate, manage, and / or maintain a core network device, and / or an OAM network element (a network management device of an access network device) is configured to operate, manage, and / or maintain an access network device. For example, the present disclosure includes a first OAM network element and a second OAM network element, where the first OAM network element is a network management device of a core network device, and the second OAM network element is a network management device of an access network device. Optionally, the first OAM network element and / or the second OAM network element include an AI entity. In another example, the present disclosure includes a third OAM network element, where the third OAM network element is a network management device of both a core network device and an access network device. Optionally, the third OAM network element includes an AI entity.

[0077] FIG. 4A is a diagram of a first application framework of 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 on training data provided by a data source to obtain an AI model, and places the AI ​​model in a model inference host. The AI ​​model represents a mapping relationship between the input and output of the model. Obtaining an AI model by learning with a model training node is equivalent to obtaining a mapping relationship between the input and output of the model by learning with a model training node using training data. The model inference node uses the AI ​​model to perform inference based on inference data provided by a data source to obtain an inference result. This method can also be described as follows, where the model inference node inputs inference data to the AI ​​model and obtains an output through the AI ​​model. The output is an inference result. The inference result may indicate a configuration parameter used (acted upon) by a subject of an action, and / or an operation performed by a subject of an action. The inference result may be evenly planned by an actor entity and sent to one or more targets of the action (e.g., a core network element, a base station, or a UE) for action. Optionally, the model inference node may feed back the inference result of the model inference node to the model training node. This process may be referred to as model feedback. The fed back inference result is used by the model training node to update the AI ​​model, and the updated AI model is placed in the model inference node. Optionally, the target of the action may feed back the network parameters collected by the target of the action to the data source. This process may be referred to as performance feedback, and the fed back network parameters may be used as training data or inference data.

[0078] For example, the AI ​​model includes a decoder network in an AE network. The decoder network is disposed on an access network device side. The inference result of the decoder network is, for example, used to reconstruct a downlink channel matrix. The AI ​​model includes an encoder network in an AE network. The encoder network is disposed on a UE side. The inference result of the encoder network is, for example, used to encode a downlink channel matrix.

[0079] The application framework shown in FIG. 4A may be disposed in the network element shown in FIG. 1. For example, the application framework in FIG. 4A may be disposed in at least one of a terminal device, an access network device, a core network device (not shown), or an independently disposed AI network element (not shown) in FIG. 1. For example, the AI ​​network element (which may be considered as a model training node) may analyze or train training data provided by the terminal device and / or the access network device to obtain a model. At least one of the terminal device, the access network device, or the core network device (which may be considered as a model inference node) may perform inference using the model and inference data to obtain an output of the model. The inference data may be provided by the terminal device and / or the 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, the access network device, or the core network device (which may be considered as a target of the action) may perform a corresponding operation based on the inference result. The model inference node and the target of the action may be the same or different. This is not limited.

[0080] With reference to FIGS. 4B to 4E, the following uses an example to describe a network architecture to which the method provided in the present disclosure can be applied.

[0081] As shown in FIG. 4B, in a first possible embodiment, the access network device includes a near real-time radio access network intelligent controller (RAN intelligent controller, RIC) module configured to perform model training and inference. For example, the near real-time RIC may be configured to train an AI model and perform inference using the AI ​​model. For example, the near real-time RIC may obtain network-side or terminal-side information from at least one of the CU, DU, or RU, and the information may be used as training data or inference data. Optionally, the near real-time RIC may submit an inference result to at least one of the CU, DU, RU, or terminal device. Optionally, the CU and the DU may exchange the inference result. Optionally, the DU and the RU may 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.

[0082] As shown in FIG. 4B, in a second possible embodiment, the non-real-time RIC is located outside the access network device (optionally, the non-real-time RIC may be located in an OAM network element or a core network device) and is configured to perform model training and inference. For example, the non-real-time RIC is configured to train an AI model and perform inference using the model. For example, the non-real-time RIC may obtain network-side and / or terminal-side information from at least one of the CU, DU, or RU. This information may be used as training data or inference data, and the inference result may be submitted to at least one of the CU, DU, RU, or terminal device. Optionally, the CU and DU may exchange the inference result. Optionally, the DU and RU may exchange the inference result. For example, the non-real-time RIC submits the inference result to the DU, and the DU forwards the inference result to the RU.

[0083] As shown in FIG. 4B, in a third possible embodiment, 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 network element or a core network device). As with the non-real-time RIC in the second possible embodiment, the non-real-time RIC may be configured to perform model training and inference, and / or as with the near real-time RIC in the first possible embodiment, the near real-time RIC may be configured to perform model training and inference, and / or the non-real-time RIC may perform model training, and the near real-time RIC may obtain AI model information from the non-real-time RIC, obtain network-side and / or terminal-side information from at least one of the CU, DU, or RU, and obtain an inference result based on the information and the AI ​​model information. Optionally, the near real-time RIC may submit an inference result to at least one of the CU, DU, RU, or terminal device. Optionally, the CU and the DU may exchange inference results. Optionally, the DU and the RU may exchange inference results. For example, the near real-time RIC submits inference results to the DU, which forwards the inference results to the RU. For example, the near real-time RIC is configured to train model A and perform inference using model A. For example, the non-real-time RIC is configured to train model B and perform inference using model B. 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 performs inference using model C.

[0084] 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, in FIG. 4C, the CU is separated into a CU-CP and a CU-UP.

[0085] 4D is an exemplary diagram of a network architecture to which the 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 network element 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. When an OAM network element and a core network device each include an AI entity, the models obtained by training using the AI ​​entities of the OAM and core network devices are different, and / or the models used for inference are different.

[0086] In this disclosure, a difference between models includes a difference in at least one of the following: a structural parameter of the model (e.g., at least one of the number of neural network layers, the width of the neural network, the connection relationships between layers, the weights of the neurons, the activation functions of the neurons, or the biases in the activation functions), an input parameter of the model (e.g., the type of the input parameters and / or the dimensionality of the input parameters), or an output parameter of the model (e.g., the type of the output parameters and / or the dimensionality of the output parameters).

[0087] FIG. 4E is an exemplary diagram of a network architecture to which the method according to the present disclosure can be applied. Compared with FIG. 4D, in FIG. 4E, the access network device is separated into a CU and a DU. Optionally, the CU may include an AI entity, and the function of the AI ​​entity is similar to that of the near real-time RIC. Optionally, the DU may include an AI entity, and the function of the AI ​​entity is similar to that of the near real-time RIC. When the CU and the DU each include an AI entity, the models obtained by training using the AI ​​entities of the CU and the 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 disposed in the CU-CP. Optionally, one or more AI models may be disposed in the CU-UP.

[0088] In Figure 4D or Figure 4E, the OAM network element of the access network device and the OAM network element of the core network device are uniformly arranged. Alternatively, as described above, in Figure 4D or Figure 4E, the OAM network element of the access network device and the OAM network element of the core network device may be separately arranged.

[0089] In the present disclosure, a model may obtain an output by inference, and the output includes one or more parameters. The learning or training processes of different models may be located on different devices or nodes, or on the same device or node. The inference processes of different models may be located on different devices or nodes, or on the same device or node.

[0090] Optionally, the AI ​​model includes a decoder network in the AE network. On the network side, the inference result of the decoder network is used, for example, to reconstruct the downlink channel matrix. Optionally, the AI ​​model includes an encoder network in the AE network, and model information of the encoder network may be transmitted to the UE for the UE to perform inference.

[0091] It should be noted that in the framework of FIG. 4A-FIG. 4E, 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., input matrix) to output parameters (e.g., output matrix). For example, with respect to a decoder network on the network side, the input matrix may be a matrix determined based on the 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 inferred using AI techniques. This is not limited. The inference data includes the input matrix and is used to infer the output matrix 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 the target of the action. The inferred matrix may be transmitted to an access network device, such as a CU, CU-CP, CU-UP, DU, RU, or UE, for further processing, e.g., for reconstruction of the downlink channel matrix.

[0092] In the present disclosure, when the decoder network in the AE network is located on the network side, the decoder network may be located in an access network device (e.g., a base station) or outside the access network device, for example, may be located in an OAM network element, an AI network element, or a core network device, or may be located in an RU, a DU, or a near real-time RIC. This is not limited. The inference result of the decoder network may be obtained by the access network device through inference, or may be sent to the access network device after a non-real-time RIC performs inference. For simplicity of explanation, the present disclosure is described using an example in which the decoder network is located in the access network device.

[0093] In the present disclosure, when an encoder network in an AE network is located on the terminal side, the encoder network may be located in a UE, and the UE may perform inference using the encoder network.

[0094] The following describes the methods provided in the present disclosure with reference to the accompanying drawings. The steps or operations included in these methods are merely examples, and other operations or variations of various operations may be further performed in the present disclosure. In addition, steps may be performed in a different order than the order presented in the present disclosure, and in some cases, all operations need not be performed.

[0095] FIG. 5 is a flow chart of a communication method according to the present disclosure.

[0096] S501: A UE obtains M first downlink channel subdata, where each first downlink channel subdata corresponds to one data space among the M data spaces, where M is an integer greater than 1.

[0097] The M first downlink channel subdata are obtained, for example, based on the first downlink channel data. For example, to obtain the M first downlink channel subdata, the UE may divide and assign the first downlink channel data to M data spaces, or it is understood that the UE may divide the first downlink channel data into M parts. Each first downlink channel subdata corresponds to one data space, which may also be understood as the data space having one-to-one correspondence with the first downlink channel subdata. The first downlink channel data is, for example, original downlink channel data (or called original downlink channel matrix or downlink channel response). In other words, after obtaining the original downlink channel data, the UE may directly divide the original downlink channel data into M parts without performing other processing on the original downlink channel data. In this case, the processing steps can be reduced. Alternatively, the first downlink channel data may be data obtained by pre-processing the second downlink channel data, and the second downlink channel data is obtained based on the original downlink channel matrix. In this case, the original downlink channel data can be simplified by a pre-processing process to simplify the process of processing the first downlink channel data by the UE. Alternatively, the first downlink channel data may be data output by a neural network, for example, the contents of the original downlink channel matrix, etc. are invisible to the UE, and the UE directly obtains the first downlink channel data output by the neural network.

[0098] When the first downlink channel data is obtained by pre-processing the second downlink channel data, a pre-processing process is included. The second downlink channel data is obtained based on an original downlink channel matrix. For example, the second downlink channel data is an original downlink channel matrix, or the second downlink channel data is an eigenvector obtained by processing the original downlink channel matrix. In different implementations of the second downlink channel data, the pre-processing process may be different. These will be described below.

[0099] 1. The second downlink channel data is the original downlink channel matrix. For example, the original downlink channel matrix is ​​called the first downlink channel matrix.

[0100] For example, the dimension of the first downlink channel matrix is ​​[N tx ,N rx ,N RB ] and N tx represents the number of antennas or ports at the transmitter (e.g., an access network device) of the downlink signal, and N rx represents the number of antennas or ports at the receiver (e.g., UE) of the downlink signal, and N RB represents the number of frequency domain units, for example, the number of resource blocks (RBs) or the number of subbands.

[0101] Optionally, the UE may further perform a dimension conversion process on the first downlink channel matrix to obtain converted data, i.e., to obtain a converted first downlink channel matrix. The dimension of the converted first downlink channel matrix is ​​[N tx *N rx ,N RB ] or [N tx ,N rx ,N RB For example, a matrix is ​​denoted by H, where H is a complex matrix, i.e.

number

[0102] Optionally, a discrete Fourier transform (DFT) is further performed to obtain two groups of DFT bases, namely, the spatial domain bases

number

number

number

[0103] Complex matrix C complex After S is obtained, the pre-processing process of the second downlink channel data is completed. Here, S H is the Hermitian matrix of S, which is also called the self-conjugate matrix and can be obtained by performing a conjugate transpose on the matrix S. sb represents 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. The common frequency domain subband granularity can be 1RB, 2RB, 4RB, or 8RB, etc., which is not limited here. For example, if the frequency domain subband granularity is 4RB, then N sb =N rb / 4. S represents the space domain basis, and the specific form of S is related to the antenna panel. It is assumed that the antenna panel is dual polarized, with a horizontal component of Nh and a vertical component of Nv, and the expression form of S obtained is as follows:

number

[0104] F represents the frequency domain basis, and the representation of F is the subband N sb For example, F may satisfy the following formula: F = DFT(N sb ) (Formula 3)

[0105] Optionally, an oversampling factor may be further added in the DFT process. For example, a plurality of groups of orthogonal space domain bases {S1, S2, S3...} and a plurality of groups of orthogonal frequency domain bases {F1, F2, F3...} may be generated in an oversampling manner, and S i and F j The groups are selected from these groups as the spatial domain basis and the frequency domain basis of the present disclosure. For example, a group having a correct projection direction may be selected from these groups. For example, the oversampling factors of the spatial domain and the frequency domain are 4, respectively.

[0106] Optionally, the first downlink channel data may be, for example, a complex matrix obtained by pre-processing the second downlink channel data, for example a complex matrix C complex It is.

[0107] 2. The second downlink channel data is an eigenvector obtained by processing the first downlink channel matrix.

[0108] In this case, the first downlink channel matrix needs to be first processed to obtain eigenvectors, and then the eigenvectors are pre-processed to obtain the first downlink channel data. Alternatively, it may be understood that the process of processing the first downlink channel matrix to obtain eigenvectors and the process of pre-processing the eigenvectors to obtain the first downlink channel data are both considered as pre-processing processes of the first downlink channel matrix.

[0109] For example, the dimension of the first downlink channel matrix is ​​[N tx ,N rx ,N RB ], and the dimensionality reduction is performed by singular value decomposition (SVD) to obtain the eigensubspace matrix (or called eigensubspace for short) of the downlink channel, [N tx ,N rx ,N RB ]-dimensional first downlink channel matrix. The dimension of the eigensubspace is [N tx ,N sb ]. When performing dimensionality reduction on the first downlink channel matrix by SVD, the UE may process different ranks of the first downlink channel matrix separately, and different ranks may also be understood as different streams or different layers. One channel information (or one channel estimation result) may correspond to one or more layers. The following describes the process in which the UE processes the Lth layer of the first downlink channel matrix. There may be multiple ways. This is not limited.

[0110] Each subband in the Lth layer may include an RB, and the UE may calculate an equivalent downlink channel of a subband by referring to the downlink channel of the RB. The downlink channel corresponding to the kth RB in the Lth layer subband c is H k , and the equivalent downlink channel for subband c may be expressed as:

number

[0111] UE is

number

number

[0112] in particular,

number

number

number

number

[0113] Optionally, further, the eigenvectors

number

number

number

number

[0114] The obtained complex matrix C complex is a sparse representation of the eigensubspace of the original downlink channel, and the dimensions of the complex matrix match the dimensions of the eigenvectors that existed before the space-frequency joint projection, and N tx *N sbThe complex matrix C complex After S is obtained, the pre-processing process of the second downlink channel data is completed. H , N sb For an explanation of parameters such as , and the spatial domain basis S, see the above discussion.

[0115] Optionally, an oversampling factor may be further added in the DFT process. For example, a plurality of groups of orthogonal space domain bases {S1, S2, S3...} and a plurality of groups of orthogonal frequency domain bases {F1, F2, F3...} may be generated in an oversampling manner, and S i and F j A group of 1000 s x ... complex After obtaining the complex matrix C complex Optionally, the UE may obtain the first downlink channel data based on a complex matrix C complex The method of acquiring the first downlink channel data based on complex directly use as the first downlink channel data, that is, the first downlink channel data is the result of pre-processing the second downlink channel data.

[0116] Alternatively, if the UE is a complex matrix C complex Another method for obtaining the first downlink channel data based on complexFor example, in the frequency domain direction (delay domain), generally, energy is mainly concentrated around delay=0, and energy in other regions can be essentially ignored. Therefore, the UE can select F consecutive columns on both sides of delay=0 as the first downlink channel data, and the coefficients of the remaining part can be defaulted to 0. For example, the UE can select a complex matrix C complex F consecutive columns may be selected from the complex matrix C complex Columns not selected from may not be processed, in which case the energy distribution may be considered and processing overhead may be reduced.

[0117] For example, F is a positive integer, and the value of F may be predefined in the protocol, or different F may be determined based on different overhead. For example, a mapping relationship between overhead and F may be provided in the protocol, so that the UE and the access network device can determine the same F based on the current overhead requirement. Alternatively, the value of F may be indicated by the access network device. For example, the access network device transmits information indicating the value of F to the UE. The UE can determine the value of F after receiving the information. Alternatively, the value of F may be determined by the UE. For example, the UE determines the value of F based on factors such as channel conditions and / or network topology to reduce the impact on air interface transmission. After determining the value of F, the UE may transmit information indicating the value of F to the access network device. The access network device can determine the value of F after receiving the information.

[0118] Alternatively, if the UE is a complex matrix C complex Yet another method for obtaining the first downlink channel data based on complex For example, the UE may perform a compression process on a complex matrix Ccomplex may be input to the encoder network, which may then input a complex matrix C complex The first downlink channel data is obtained by compressing the first downlink channel data, and the encoder network outputs the compressed information. This can reduce the complexity of processing the first downlink channel data by the UE.

[0119] The above process is to obtain first downlink channel data. After obtaining the first downlink channel data, the UE may divide and allocate the first downlink channel data into M data spaces to obtain M first downlink channel subdata. The first downlink channel subdata correspond one-to-one to the data spaces. For example, the i-th first downlink channel subdata in the M first downlink channel subdata corresponds to the i-th data space in the M data spaces, where i may be an integer from 1 to M.

[0120] In the present disclosure, M data spaces are included, and the M data spaces may correspond to dictionaries. For example, the M data spaces may correspond to N dictionaries, and N is an integer greater than or equal to 1 and less than or equal to M. Optionally, N=M, that is, the data spaces correspond to dictionaries one-to-one, and each data space corresponds to one dictionary, or N=1, that is, all M data spaces correspond to the same dictionary, and a dictionary can be considered to correspond to each data space. Optionally, N=M / 2, and one dictionary for every two data spaces. Other possible cases are not described one by one. Different data spaces may correspond to the same dictionary or different dictionaries. This is not limited. The use of dictionaries is described in S502 below. In addition, the M data spaces (or the division method of the M data spaces) are also included in the dictionary training process. The dictionary training process is described in the subsequent embodiment. Therefore, the division method of the M data spaces, etc. are also described in the subsequent embodiment.

[0121] The variables stored in the dictionary include at least one of {data space index, element index, element}, i.e., the variables stored in the dictionary may include one or more of the data space index, element index, or element. In addition, the dictionary may further include other information or may not include other information. This is not limited. The data space index included in the dictionary is the data space index corresponding to the dictionary. For example, if the dictionary corresponds one-to-one to the data space, one dictionary corresponds to one data space, and the dictionary includes the data space index corresponding to the dictionary. Alternatively, if all M data spaces correspond to the same dictionary, the dictionary may correspond to M data spaces, and the dictionary may not include the data space index. The element may be, for example, a vector, and the dictionary may include multiple elements. Each element may have a corresponding index, i.e., the element may correspond one-to-one to the element index. If N is greater than 1, the indexes of elements included in different dictionaries may be reused. For example, the index of an element in each dictionary may start from 1 or 0, i.e., the elements included in different dictionaries are numbered independently. Alternatively, the index of an element included in different dictionaries may be different, i.e., the elements included in different dictionaries are numbered together. For example, the index of an element in a first dictionary is 0 to d-1, and the index of an element in a second dictionary starts from d. FIG. 6 is a diagram of N dictionaries. In FIG. 6, N=M is ​​used as an example, i.e., M dictionaries are included in total. In FIG. 6, 0 to 3 in each dictionary represent the index of an element. Here, it is used as an example that the number of indexes of an element included in each dictionary is 4. This is not limited in practice. In addition, the number of elements included in different dictionaries may be the same or different.

[0122] Optionally, the dictionary representation method further includes that, when N is between 1 and M, the dictionary may include {dictionary index, element index, element}, and the correspondence between the dictionary index and the data space index may be interoperable between the access network device and the terminal device. When N=1, the dictionary index may be omitted. By default, all data space indexes correspond to the dictionary. When M>N>1, the correspondence between the dictionary index and the data space index may be a default rule predefined in the protocol. For example, when M=4 and N=2, in the rule, data space index 0 and data space index 2 correspond to dictionary index 0, and data space index 1 and data space index 3 correspond to dictionary index 1. Alternatively, when M>N>1, the access network device may indicate the correspondence between the dictionary index and the data space index to the UE. Alternatively, when M>N>1, the UE may report the correspondence between the dictionary index and the data space index to the access network device. If M=N, the dictionary indices may correspond one-to-one to the data space indices, or the dictionary may contain {data space index, element index, element}.

[0123] S502: The UE determines first information corresponding to the i-th first downlink channel subdata among the M first downlink channel subdata based on the first dictionary corresponding to the i-th data space among the M data spaces, where i is an integer from 1 to M. Thus, the UE determines M pieces of first information in total.

[0124] The i-th first downlink channel subdata corresponds to the i-th data space in the M data spaces. For example, when the first downlink channel data is divided and assigned to the M data spaces, M first downlink channel subdata are obtained, and the i-th first downlink channel subdata is a portion of the first downlink channel data divided and assigned to the i-th data space. For example, N=M, and each data space has a dictionary corresponding to the data space. In this case, the first dictionary is, for example, a dictionary corresponding to the i-th data space in the M data spaces, that is, the UE may determine the first information corresponding to the i-th first downlink channel subdata based on the first dictionary corresponding to the i-th data space. When i is an integer from 1 to M, the dictionary corresponding to the different data spaces may be referred to as a first dictionary, and the first dictionaries corresponding to the different data spaces may be the same or different. Alternatively, when N=1, and one dictionary corresponds to the M data spaces, the first dictionary is a dictionary. For any data space within the M data spaces, a first dictionary may be used, and the UE may determine first information corresponding to the i-th first downlink channel subdata based on the first dictionary.

[0125] If N=1, the UE may determine that the dictionary is the first dictionary corresponding to the i-th data space. Alternatively, if M=N and the dictionary corresponds one-to-one to the data space, the UE may determine the first dictionary corresponding to the i-th data space. Alternatively, if M>N>1, the UE may determine the first dictionary corresponding to the i-th data space based on a correspondence between the dictionary index and the data space index. For example, M=4 and N=2. The correspondence specifies that data space index 0 and data space index 2 correspond to dictionary index 0, and data space index 1 and data space index 3 correspond to dictionary index 1. i is equal to the data space index. In this case, the UE may determine the first dictionary corresponding to the i-th data space based on the value of i and the correspondence. For example, if i=1, the UE may determine that the first dictionary corresponding to the first data space is the dictionary indicated by dictionary index 1.

[0126] From the above description of the dictionary, it can be known that the first dictionary may include a plurality of elements, and the UE may determine P elements corresponding to the i-th first downlink channel subdata from the plurality of elements, where P is a positive integer. For example, among the plurality of elements included in the first dictionary, the P elements most relevant to the i-th subdata are the P elements corresponding to the i-th first downlink channel subdata, and the P elements may be used as the first information corresponding to the i-th first downlink channel subdata. If P is greater than 1, the P elements may form the first information in a first combination method. For example, the first combination method is to multiply the P elements, or the first combination method is to perform weighted addition on the P elements (e.g., to average or perform weighted addition using other possible weight values), or to connect the P elements in series. The first combination method is not limited. For example, the first combination method is predefined in a protocol, or is determined by an access network device and notified to the UE, or is determined by the UE and notified to the access network device. For the M first downlink channel subdata, the UE may determine first information corresponding to the M first downlink channel subdata, and the UE may determine a total of M pieces of first information, where the M pieces of first information are M elements.

[0127] S503: The UE sends first indication information. For example, the UE sends the first indication information to the access network device, and the access network device may receive the first indication information from the UE in response. The first indication information may indicate M pieces of first information, and the access network device may determine the M pieces of first information based on the first indication information.

[0128] Optionally, the first indication information includes M first information identifiers, thereby indicating the M first information. One first information identifier is, for example, an index of the first information in a corresponding dictionary. For example, the M first information includes first information corresponding to the i-th first downlink channel subdata, and the first information identifier is an index of the first information in the first dictionary. After determining the M first information, the UE may determine M first information identifiers, for example, the UE may determine M identifiers in total, and the UE may transmit the M identifiers to the access network device. When the UE transmits the M first information identifiers, it may be considered that the UE transmits CSI, i.e., the M first information identifiers may be used as CSI, or the M first information identifiers may be used as PMI, or the M first information identifiers may perform a function similar to that of a PMI or a CSI identifier.

[0129] Alternatively, the first indication information may not include identifiers of the M pieces of first information, and may indicate the first information in another manner. For example, there are different combination relationships between dictionary elements, and each combination relationship may include one element in each of the N dictionaries. Each combination relationship may correspond to one piece of indication information. When the UE transmits specific indication information, it indicates that the combination relationship corresponding to the indication information is indicated. For example, when the first indication information corresponds to the combination relationship of the M pieces of first information, the first indication information transmitted by the UE may indicate the M pieces of first information.

[0130] Optionally, when transmitting the M first information identifiers, the UE may transmit the identifiers in a first order. The first order is the arrangement order of the M data spaces, that is, the first order specifies that the UE transmits the first information identifier corresponding to a specific data space first, and transmits the first information identifier corresponding to a specific data space next. For example, M=4, the M data spaces are data space 1 to data space 4, and the first order is 2-1-4-3. When transmitting the M first information identifiers, the UE transmits the first information identifier corresponding to data space 2 first, then transmits the first information identifier corresponding to data space 1, then transmits the first information identifier corresponding to data space 4, and finally transmits the first information identifier corresponding to data space 3. With respect to the UE and the access network device, the first order is known. Thus, after receiving the M first information identifiers, the access network device can further determine the correspondence relationship between the data space and the first information identifier to avoid correspondence errors.

[0131] For example, the first order is a predefined order in a protocol, and the UE and the access network device can determine the first order according to the protocol. Alternatively, the first order may be preconfigured in the UE and the access network device. Alternatively, the first order may be determined by the access network device. After determining the first order, the access network device may send second indication information to the UE, the second indication information indicating the first order, and the UE may determine the first order based on the second indication information. Alternatively, the first order may be determined by the UE. After determining the first order, the UE may send third indication information to the access network device, the third indication information indicating the first order, and the access network device may determine the first order based on the third indication information.

[0132] S504: For an i-th first information among the M first information, the access network device reconstructs an i-th second downlink channel subdata based on a first dictionary corresponding to an i-th data space among the M data spaces. When i is an integer from 1 to M, the access network device may obtain M second downlink channel subdata in total.

[0133] For example, when the access network device receives M first information identifiers in a first order, the access network device can determine the correspondence between the data space and the first information identifier, so that the access network device can determine the first information corresponding to the first information identifier based on the dictionary corresponding to the data space, and the first information determined by the access network device is considered as the second downlink channel subdata reconstructed by the access network device. For example, N=M, the data space corresponds one-to-one to the dictionary, and the dictionary corresponding to the i-th data space is, for example, the first dictionary. In this case, for the i-th first information identifier, the access network device can determine the i-th first information identifier in the first dictionary to determine the first information corresponding to the i-th first information identifier in the first dictionary, i.e., to reconstruct the second downlink channel subdata (i-th second downlink channel subdata) corresponding to the i-th first information. In another example, N=1, and all of the M data spaces correspond to the first dictionary. In this case, for the i-th first information identifier, the access network device may determine the i-th first information identifier in the first dictionary to determine the i-th first information identifier corresponding to the i-th first information identifier in the first dictionary, i.e., to reconstruct the second downlink channel subdata (i-th second downlink channel subdata) corresponding to the i-th first information. In another example, M>N>1. In this case, for the i-th first information identifier, the access network device may determine the data space corresponding to the i-th first information identifier in a first order, for example, the i-th data space. The access network device may further determine the dictionary corresponding to the i-th data space, for example, the first dictionary, based on the correspondence between the dictionary index and the data space index.In this case, the access network device may determine the first information corresponding to the identifier of the i-th first information in the first dictionary, i.e., may reconstruct the second downlink channel sub-data (the i-th second downlink channel sub-data) corresponding to the i-th first information.

[0134] In an ideal state, the M second downlink channel subdata acquired by the access network device and the M first downlink channel subdata acquired by the UE may be the same data. For example, the i-th first downlink channel subdata and the i-th second downlink channel subdata are the same data. In practical application, there may be a deviation between the M second downlink channel subdata acquired by the access network device and the M first downlink channel subdata acquired by the UE. The process in which the UE acquires the first information based on the dictionary is equivalent to the process in which the M first downlink channel subdata are quantized. In other words, the UE transmits the quantization information to the access network device, and the access network device reconstructs the M second downlink channel subdata based on the quantization information and the dictionary. There may be some loss in the quantization and reconstruction process. Therefore, there may be a certain deviation between the M second downlink channel subdata and the M first downlink channel subdata. For example, the i-th first downlink channel subdata and the i-th second downlink channel subdata may be different data. However, due to improvements in dictionary accuracy and transmission quality, the deviation between the M second downlink channel subdata and the M first downlink channel subdata may tend to decrease.

[0135] S505: The access network device reconstructs downlink channel information according to the M second downlink channel sub-data. In other words, the access network device reconstructs a downlink channel matrix according to the M second downlink channel sub-data, for example, reconstructs a first downlink channel matrix.

[0136] In S501, the UE receives a complex matrix C as a first downlink channel data. complex or use the complex matrix C as the first downlink channel data complex In the case of selecting F consecutive columns from, after obtaining the M second downlink channel subdata, the access network device may concatenate the M second downlink channel subdata, and the obtained information is, for example, called an angle delay region coefficient, and the angle delay region coefficient is a matrix:

number

[0137] Alternatively, in S501, the UE calculates a complex matrix C complex , to obtain compressed information, and use the compressed information as the first downlink channel data, the M second downlink channel sub-data obtained by the access network device are actually M compressed sub-information. Optionally, the access network device may reconstruct the M compressed sub-information to obtain K reconstructed information, where K is a positive integer, and K may be equal to M or may not be equal to M. For example, when the UE obtains the compressed information through an encoder network, a decoder network corresponding to the encoder network may be set on the access network device side. The access network device may input the M second downlink channel sub-data to the decoder network, and the decoder network may output the K reconstructed information. The access network device may:

number

[0138] FIG. 7 is a diagram in which the UE uses the compressed information as the first downlink channel data, and the access network device needs to reconstruct the compressed information. In FIG. 7, for example, the UE inputs the second downlink channel data to the encoder network, the encoder network compresses the second downlink channel data, and the encoder network outputs the compressed information, and the compressed information can be used as the first downlink channel data. Alternatively, in FIG. 7, the UE inputs the complex matrix C complex may be input to the encoder network, which then receives the complex matrix C complex , and the encoder network outputs compressed information, which may be used as the first downlink channel data. The UE divides the compressed information and allocates it to M data spaces. In FIG. 7, M=4 is used as an example, in which the UE obtains four first downlink channel sub-data. The UE processes the four first downlink channel sub-data based on four dictionaries to obtain identifiers of the four first information. In FIG. 7, a circle represents a dictionary, and C M represents the number of elements in the Mth dictionary, and log2C M represents the number of transmission bits corresponding to the Mth dictionary. Optionally, in FIG. 7, the number of transmission bits corresponding to the dictionary may be obtained by rounding up. For example, in FIG. 7, log2C M teeth

number

number

[0139] Regardless of how the angular delay region coefficients are obtained, the access network device may

number

[0140] For example, the first downlink channel data is a first downlink channel matrix

number

number

number

number

[0141]

number

number

number

number

[0142] In another example, the first downlink channel data is represented by a first downlink channel matrix

number

number

number

number

[0143]

number

number

number

[0144] In the present disclosure, the UE may divide and allocate the first downlink channel data into M data spaces, and may determine the first information corresponding to each first downlink channel subdata based on the dictionary corresponding to the different data spaces. Different data spaces may represent different location information, that is, different channel environment information. The UE feeds back the first information corresponding to the different data spaces, so that the access network device can determine the corresponding relationship between the first information and the environment information. In this case, the first information fed back by the UE can reflect the actual communication environment, and the accuracy of the first information fed back by the UE is improved. The access network device can reconstruct an accurate downlink channel based on the first information fed back by the UE.

[0145] The embodiment shown in FIG. 5 describes a network inference process. A dictionary is included in the network inference process, and the dictionary can be obtained by network training. There can be multiple ways to train to obtain the dictionary. For example, when the encoder network is not located on the UE side and the decoder network is not located on the access network device side, or even when the encoder network is located on the UE side and the decoder network is located on the access network device side, the encoder network and the decoder network may or may not be trained with the dictionary. When only the dictionary needs to be obtained by training and the encoder / decoder network does not need to be obtained, reference can be made to another communication method described below in this disclosure. In this method, a network training process is described, and the dictionary can be obtained in the training process. FIG. 8 is a flowchart of the method.

[0146] S801: A first node obtains M pieces of third downlink channel subdata. Each of the third downlink channel subdata corresponds to one data space among the M data spaces. The M data spaces in the present disclosure and the M data spaces in the embodiment shown in FIG. 5 may have the same characteristics.

[0147] The M third downlink channel sub data are obtained, for example, based on the third downlink channel data. For example, to obtain the M third downlink channel sub data, the UE or the first node may divide and allocate the third downlink channel data to M data spaces, or it is understood that the UE or the first node may divide the third downlink channel data into M parts. The third downlink channel data is, for example, the original downlink channel data. For example, the original downlink channel data in this embodiment is called a third downlink channel matrix. Alternatively, the third downlink channel data may be data obtained by pre-processing the fourth downlink channel data, and the fourth downlink channel data is obtained based on the third downlink channel matrix. Alternatively, the third downlink channel data may be data output by a neural network. The third downlink channel matrix may be considered as training data or may be called a training sample. For example, the third downlink channel matrix may be considered to include one or more training data. For example, the third downlink channel matrix actually includes one or more downlink channel sub-matrices, and one downlink channel sub-matrix can be considered as one training data. The third downlink channel sub-matrices here can be independent of each other and are not included in a large matrix. In other words, the third downlink channel matrix is ​​not considered as a large matrix, and the third downlink channel matrix can be understood as a combined name of one or more third downlink channel sub-matrices.

[0148] If the third downlink channel data is obtained by pre-processing the fourth downlink channel data, a pre-processing process is included. For the pre-processing process of the fourth downlink channel data, please refer to the description of the pre-processing process of the second downlink channel data in S501 of the embodiment shown in Figure 5.

[0149] In the present disclosure, the first node may be, for example, a UE or an access network device, or a third party device (e.g., an AI node). The training process may be an online training process or an offline training process.

[0150] After acquiring the third downlink channel data, the first node may divide and allocate the third downlink channel data to M data spaces to acquire M third downlink channel subdata. The third downlink channel subdata correspond one-to-one to the data spaces. For example, the i-th third downlink channel subdata in the M third downlink channel subdata corresponds to the i-th data space in the M data spaces, where i may be an integer from 1 to M.

[0151] In order to divide and allocate the third downlink channel data to the M data spaces, the first node needs to first determine the M data spaces, i.e., determine how to divide the M data spaces. For example, the first node is a UE or an access network device. For example, if the division method of the M data spaces is predefined in a protocol, both the UE and the access network device may determine how to divide the M data spaces according to the protocol. Alternatively, the division method of the M data spaces is determined by the access network device. The access network device may send fourth instruction information to the UE, the fourth instruction information may indicate how to divide the M data spaces, and the UE may determine how to divide the M data spaces based on the fourth instruction information. Alternatively, the division method of the M data spaces may be determined by the UE. The UE may send fifth instruction information to the access network device, the fifth instruction information may indicate how to divide the M data spaces, and the access network device may determine how to divide the M data spaces based on the fifth instruction information.

[0152] In the present disclosure, for example, the division method of M data spaces is specified in the protocol as follows, where M=4, and these four data spaces each include four parts of one data. The four parts are the real part of the polarization 1 included in the data, the imaginary part of the polarization 1 included in the data, the real part of the polarization 2 included in the data, and the imaginary part of the polarization 2 included in the data. For example, when the third downlink channel data is divided and assigned to four data spaces, the four third downlink channel subdata obtained by the division respectively include the real part of the polarization 1 included in the third downlink channel data, the imaginary part of the polarization 1 included in the third downlink channel data, the real part of the polarization 2 included in the third downlink channel data, and the imaginary part of the polarization 2 included in the third downlink channel data. From the viewpoint of antenna form, the antenna element is dual-polarized, and the polarization 1 and the polarization 2 represent two polarization directions. The two polarization directions may be considered to be independent of each other. From the perspective of complex numbers, data includes real and imaginary parts, and the processing process of the real part and the processing process of the imaginary part are independent of each other. Therefore, the data space can be divided based on the antenna polarization direction and the real and imaginary parts of the complex number, so that each data space can be processed independently. The data space is divided, and the size of each data space is 1 / M of the original data, and different data spaces can represent different environmental information. Optionally, the division method of the data space may be an unequal division method instead. This is not limited.

[0153] Optionally, in the network inference stage, the UE and the access network device may also determine the division method of the M data spaces, and the determination method is similar to that in the present disclosure. Alternatively, the first node may indicate the division method of the M data spaces to the UE and / or the access network device. In other words, in the embodiment shown in FIG. 5, in order to divide and allocate the first downlink channel data to the M data spaces, the UE also needs to first determine the division method of the M data spaces. In this case, the division method of the M data spaces provided in the present disclosure may be used. In the network inference stage and the network training stage, the applied division method of the M data spaces is consistent.

[0154] S802: The first node performs clustering training to obtain N dictionaries.

[0155] As described in the embodiment shown in Figure 5, N may be equal to M, or may be equal to 1, or M>N>1. In these solutions, the training process of the first node may be different. The following will describe them separately.

[0156] 1.N=M, i.e., the data space corresponds one-to-one to the dictionary.

[0157] The first node may perform clustering training based on the i-th third downlink channel subdata in the M third downlink channel subdata to obtain a dictionary (e.g., a first dictionary) corresponding to the i-th data space, and the i-th third downlink channel subdata corresponds to the i-th data space. In other words, the first node may perform training in each data space separately to obtain a dictionary corresponding to each data space, and M dictionaries may be obtained in total. Clustering is to divide a data set into different classes or clusters based on a certain criterion (e.g., distance), such that data objects in the same cluster are as similar as possible, and data objects that are not in the same cluster are as different as possible. In other words, after clustering, data of the same class can be aggregated as much as possible, and data of different classes can be separated as much as possible. Each class of data has a class center value. When the network model training is performed in the clustering method in the present disclosure, the elements included in the dictionary may also be called class center values.

[0158] Training in a data space refers to obtaining elements corresponding to the data space, which can be used as elements to be included in a dictionary corresponding to the data space.

[0159] The number of elements included in the dictionary corresponding to one data space may be related to the bit overhead corresponding to the data space. For example, the bit overhead is 48 bits, which is the total transmission overhead corresponding to M data spaces. For example, the bit overhead corresponding to all data spaces is equal. It is assumed that there are four data spaces in total, and the transmission overhead corresponding to each data space is 1 / M of the total bit overhead. In this case, the bit overhead corresponding to one data space is 12 bits. 12 bits is a maximum of 2 12 The number of elements in the first dictionary is therefore 2 12It should be less than or equal to. It can be known that different bit overheads correspond to different numbers of elements. Optionally, the first node may train different dictionaries separately based on different bit overheads, and the bit overheads may correspond one-to-one to the dictionaries. In other words, the first node may train one or more dictionaries for one data space. When multiple dictionaries are trained, the multiple dictionaries may correspond to different bit overheads, so that the UE may select an appropriate dictionary based on the current bit overhead when performing network inference.

[0160] For example, in the network inference process, the bit overhead corresponding to the data space is generally determined on the network side, for example, by an access network device. The access network device may determine the bit overhead based on the real-time channel condition between the access network device and the UE. When the UE executes the network inference process in the embodiment shown in FIG. 5, the access network device may first deliver information to the UE to determine the bit overhead of the current transmission. This information may indicate the total bit overhead corresponding to M data spaces, or may determine the bit overhead corresponding to one data space. The UE may select an appropriate dictionary based on the bit overhead indicated by the access network device. For example, if the access network device indicates that the total bit overhead is 48 bits and M=4, the UE may determine that the transmission overhead of each data space is 48 / 4=12 bits. For example, when the UE determines the dictionary corresponding to the i-th data space, if the i-th data space corresponds to multiple dictionaries (different dictionaries correspond to different bit overheads), the UE may select a dictionary corresponding to 12 bits from the dictionary to perform network inference.

[0161] In addition, the first node may further determine the dimension of the elements included in the first dictionary based on the dimension of the i-th third downlink channel subdata. The dimension of the elements included in the first dictionary may also be considered as the depth of the first dictionary and is related to the dimension of the downlink channel subdata used to train the first dictionary. Thus, the first node may determine the dimension of the elements included in the first dictionary based on the dimension of the i-th third downlink channel subdata. For example, the first node may convert the i-th third downlink channel subdata into a vector, and the length of the vector is the dimension of the elements included in the first dictionary. For example, the i-th third downlink channel subdata is the real part of the polarization 1 included in the third downlink channel data, and this part is, for example, a matrix with a dimension of [16,13]. The first node may convert this matrix into a vector with a length of 16×13. In this case, the dimension of the elements included in the first dictionary is 16×13.

[0162] The first node may convert the matrix to a vector row by row, or may convert the matrix to a vector column by column. In the network inference process, the UE also needs to perform this conversion process. In order to enable the access network device to reconstruct accurate downlink channel information, the conversion sequence of the UE needs to be known by both the UE and the access network device. For example, the conversion sequence of the UE may be predefined in a protocol, or the conversion sequence of the UE may be determined by the access network device and notified to the UE, or the conversion sequence of the UE may be determined by the UE and notified to the access network device. The conversion sequence in the network inference process may be consistent with the conversion sequence in the network training process.

[0163] After determining the number of elements included in the first dictionary and the dimensions of the elements included in the first dictionary, the first node may perform clustering training based on the i-th third downlink channel sub data to obtain a first dictionary. For each data space, the first node may perform training in a similar manner to obtain M dictionaries.

[0164] 2. N=1, that is, M data spaces correspond to one dictionary. For example, this dictionary is called the first dictionary.

[0165] The first node may perform clustering training based on the M third downlink channel sub-data to obtain a dictionary (e.g., a first dictionary) corresponding to the M data spaces. In other words, after obtaining the M third downlink channel sub-data, the first node may perform joint training to obtain a dictionary, and the dictionary corresponds to all of the M data spaces. The M third downlink channel sub-data are used to train the dictionary. In terms of the dictionary, it is equivalent to the sampled data (or training data) being increased by M-1 times, and the training data is richer, so that the elements included in the dictionary are richer and more detailed, which helps the access network device to reconstruct more accurate downlink channel information.

[0166] The M third downlink channel subdata are used to perform training to obtain elements corresponding to the M data spaces, and these elements may be used as elements included in the dictionary obtained by training.

[0167] Optionally, the first node may separately train different dictionaries based on different bit overheads, and the bit overheads may correspond one-to-one to the dictionaries. In other words, the first node may train one or more dictionaries. When multiple dictionaries are trained, the multiple dictionaries may correspond to different bit overheads, so that the UE may select an appropriate dictionary based on the current bit overhead when performing network inference.

[0168] In addition, the first node may further determine the dimension of the elements included in the first dictionary based on the dimension of the third downlink channel data. For the determination method, please refer to the above description. The first dictionary is, for example, a dictionary obtained by the first node through training.

[0169] After determining the number of elements included in the first dictionary and the dimensions of the elements included in the first dictionary, the first node may perform clustering training based on the M third downlink channel sub-data to obtain the first dictionary.

[0170] Alternatively, when N=1, the first node may perform training in all data spaces separately. For example, the first node uses the training method used when N=M, but a condition needs to be added during training, that is, the dictionaries obtained by training in all data spaces are the same. In this case, the first node may obtain N dictionaries by training, but the N dictionaries are the same. This is equivalent to the first node still obtaining one dictionary.

[0171] 3. M>N>1, i.e., M data spaces correspond to N dictionaries.

[0172] The first node may perform clustering training based on at least one third downlink channel subdata in the M third downlink channel subdata to obtain one dictionary (e.g., the first dictionary) in the N dictionaries. For example, when the third downlink channel subdata corresponds one-to-one to the data space, the at least one third downlink channel subdata corresponds to at least one data space. One dictionary may correspond to one or more data spaces. For example, there is a correspondence between the index of the dictionary and the index of the data space. In this case, when training the dictionary, the first node performs training based on the third downlink channel subdata in the data space corresponding to the dictionary.

[0173] Optionally, the first node may separately train different dictionaries based on different bit overheads, and the bit overheads may correspond one-to-one to the dictionaries. In other words, one dictionary corresponds to one or more data spaces. For one or more data spaces, the first node may train one or more dictionaries based on different bit overheads. When multiple dictionaries are trained, the multiple dictionaries may correspond to different bit overheads, so that the UE may select an appropriate dictionary based on the current bit overhead during network inference.

[0174] In addition, the first node may further determine the dimension of the elements included in the first dictionary based on the dimension of the third downlink channel data. For the determination method, please refer to the above description. The first dictionary is, for example, a dictionary obtained by the first node through training.

[0175] After determining the number of elements included in the first dictionary and the dimensions of the elements included in the first dictionary, the first node may perform clustering training based on the at least one third downlink channel sub-data to obtain the first dictionary.

[0176] There may be multiple clustering training methods that may be applied in the present disclosure, such as the K-Means clustering method. In addition, in the neural network training process, a loss function may be defined, which describes the gap or difference between an ideal target value and the output value of the neural network. In the clustering training process, a loss function may not be used, or a loss function may be used. For example, the loss function uses the minimum value of the distance between the clustering center and each of the multiple training samples as the target, or uses the training sample that is most related to the clustering center and is within the multiple training samples as the target. In addition, the loss function may be another function, and the implementation of the loss function is not limited in the present disclosure. The dictionary training process is a process in which the parameters of the dictionary are adjusted so that the value of the loss function is less than a threshold value or the value of the loss function meets the target requirement. Adjusting the parameters of the dictionary includes, for example, adjusting the elements of the dictionary.

[0177] The contents contained in the dictionary have been explained in the embodiment shown in FIG. 5 and will not be explained in detail again.

[0178] In the above process, the first node obtains N dictionaries through training, so that the UE can use the N dictionaries in the network inference process in the embodiment shown in Figure 5, and the access network device can also use the N dictionaries during reconstructing downlink channel information. The data space and the N dictionaries are divided, so that the environment information corresponding to the downlink channel can be reflected, which helps the access network device to reconstruct more accurate downlink channel information.

[0179] For ease of understanding, the following uses some accompanying drawings as examples to explain the network training process and the network inference process in the present disclosure.

[0180] FIG. 9A is a diagram of a training process and a network inference process according to the present disclosure. In the training process in FIG. 9A and the subsequent attached drawings, an example is used in which the UE performs the training process. The process from the third downlink channel data to q1 to q4, i.e., the process before the information is transmitted to the access network device, can be considered as a training process. The entire process in FIG. 9A can also be considered as a network inference process, and the network inference process can also be considered as a data processing process. Of course, the data is not actually the training data used for training, but the data processing process is similar to the training data processing process. The training process includes obtaining a dictionary by performing clustering training on multiple training data. The processing process can be considered to represent the downlink channel data using the obtained dictionary.

[0181] In the training process, it is assumed that the training data is the original downlink channel data, and the original downlink channel data actually includes multiple training data (or called training samples). The UE processes each training data in the original downlink channel data to obtain an eigenvector, and the dimension of the eigenvector is [N tx =32,N sb= 13]. The UE pre-processes the eigenvector to obtain sparse coefficients of the eigenvector, and the sparse coefficients of the eigenvector corresponding to the multiple training data may be used as the third downlink channel data. Since real training is used for network training, the data input is divided into two parts, a real part and an imaginary part, and the dimension of the third downlink channel data is, for example, [E, 2, 32, 13]. The "E" in the dimension of the third downlink channel data is considered to be the amount of training data. In other words, in this case, the third downlink channel data may be considered to include E training data, and E is a positive integer. In the dimension of the third downlink channel data, "2" represents the real part and the imaginary part, and "32" represents the N tx "13" stands for N sb Represents.

[0182] The third downlink channel data is divided and allocated to M data spaces, i.e., the third downlink channel data is divided into M parts. In FIG. 9A, M=4 is used as an example, in which case, four third downlink channel subdata can be obtained by division. The four third downlink channel subdata are y1, y2, y3, and y4. The dimensions of y1, y2, y3, and y4 are [S, 16×13], respectively, where S represents the amount of training data corresponding to one third downlink channel subdata, and 16×13 is, for example, the dimension of the training target dictionary. q1 to q4 in FIG. 9A represent four training target dictionaries, i.e., N=M is ​​used as an example in FIG. 9A. The UE trains the four dictionaries in a clustering method. Optionally, in an offline training method, information about the four dictionaries may be agreed upon in a protocol or may be transmitted by the UE to an access network device. In the online training method, information about the four dictionaries may be sent by the UE to the access network device.

[0183] In the inference process, for example, the UE may obtain four pieces of first information based on four first downlink channel sub-data and four dictionaries obtained by training, and one piece of first information is an element corresponding to one piece of first downlink channel sub-data in the corresponding dictionary.

[0184] For example, the UE may send four first information identifiers to the access network device, and each first information identifier may occupy X bits. After receiving the four first information identifiers, the access network device may reconstruct the four first information based on the four dictionaries. Then, the access network device performs processing such as concatenation on the four first information to reconstruct downlink channel information, i.e., to reconstruct a downlink channel matrix.

[0185] 9B is another diagram of the training process according to the present disclosure. The UE processes each training data in the original downlink channel data to obtain an eigenvector, and the dimension of the eigenvector is [N tx =32,N sb It is assumed that the dimension of the third downlink channel data is [E, 2, 32, 13]. The UE pre-processes the eigenvector to obtain sparse coefficients of the eigenvector, and the sparse coefficients of the eigenvector corresponding to the multiple training data included in the original downlink channel data can be used as the third downlink channel data. Since real training is used for network training, the data input is divided into two parts, a real part and an imaginary part, and the dimension of the third downlink channel data is, for example, [E, 2, 32, 13]. Here, E is the amount of training data, and E is a positive integer.

[0186] The third downlink channel data is divided and allocated to M data spaces, i.e., the third downlink channel data is divided into M parts. In FIG. 9B, M=4 is used as an example, in which the four third downlink channel subdata obtained by division can be uniformly represented as y1, i.e., y1 can be considered to include four third downlink channel subdata. The dimension of y1 is [4×S, 16×13], where S represents the amount of training data corresponding to one third downlink channel subdata, and the amount of training data corresponding to four third downlink channel subdata is 4×S. In addition, 16×13 is, for example, the dimension of the training target dictionary. In FIG. 9B, q1 represents the training target dictionary to which the M data spaces uniformly correspond, i.e., N=1 is used as an example in FIG. 9B. The UE trains the dictionary by a clustering method.

[0187] In the network inference process, for each data space, the UE may independently find the corresponding first information in the corresponding dictionary. As shown in FIG. 9B, when the M data spaces correspond to one dictionary uniformly, in the network inference process, for each data space, the UE may find the corresponding first information in the dictionary obtained by the training of FIG. 9B. FIG. 9B is still used as an example. In this case, the UE may determine four first information. For example, the UE transmits identifiers of the four first information to the access network device. After receiving the identifiers of the four first information, the access network device may reconstruct the four first information based on the four dictionaries. Then, the access network device performs processing such as concatenation on the four first information to reconstruct the downlink channel information, i.e., to reconstruct the downlink channel matrix.

[0188] In FIG. 9A and FIG. 9B, an example is used in which the third downlink channel data is a pre-processing result of the original downlink channel data (or eigenvector). From the description of the embodiment shown in FIG. 5, it can be known that the third downlink channel data may alternatively be F consecutive data strings extracted from the pre-processing result. In this case, the dimension of the elements included in the dictionary may change. For example, if the i-th third downlink channel sub-data is one of M data obtained by dividing the F data strings extracted from the pre-processing result, the i-th third downlink channel sub-data is, for example, a matrix with a dimension of [16, F], and the UE may convert this matrix into a vector with a length of 16×F. In this case, the dimension of the elements included in the first dictionary is 16×F. F is generally less than the number of sub-bands. In this case, the storage space occupied by the dictionary can be reduced.

[0189] 9C is yet another diagram of the training process according to the present disclosure. The UE processes each training data in the original downlink channel data to obtain an eigenvector, and the dimension of the eigenvector is [N tx =32,N sbIt is assumed that the eigenvector is sparse coefficients of the eigenvector, and the sparse coefficients of the eigenvector corresponding to the multiple training data included in the original downlink channel data can be used as the third downlink channel data. Since real training is used for network training, the data input is divided into two parts, a real part and an imaginary part, and the dimension of the third downlink channel data is, for example, [E, 2, 32, 13]. Here, E is the amount of training data, and E is a positive integer. To obtain M third downlink channel sub-data, F consecutive data columns are extracted from the third downlink channel data, and the F consecutive data columns are divided and assigned to M data spaces. In FIG. 9C, M=4 is used as an example, and the four third downlink channel sub-data obtained by division are y1, y2, y3, and y4. The dimensions of y1, y2, y3, and y4 are [S, 16×F], respectively. q1 to q4 in Fig. 9C represent four dictionaries, that is, N=M is ​​used as an example in Fig. 9C. The UE trains the four dictionaries in a clustering method.

[0190] In the network inference process, for each data space, the UE may independently find the corresponding first information in the corresponding dictionary. Figure 9C is still used as an example. In this case, the UE may determine four first information based on the four dictionaries obtained by the training of Figure 9C. For example, the UE transmits identifiers of the four first information to the access network device. After receiving the identifiers of the four first information, the access network device may reconstruct the four first information based on the four dictionaries. Then, the access network device performs processing such as concatenation on the four first information to reconstruct downlink channel information, i.e., to reconstruct a downlink channel matrix.

[0191] 9D is yet another diagram of the training process according to the present disclosure. The UE processes each training data in the original downlink channel data to obtain an eigenvector, and the dimension of the eigenvector is [N tx =32,N sb It is assumed that the dimension of the third downlink channel data is [E, 2, 32, 13]. The UE pre-processes the eigenvector to obtain sparse coefficients of the eigenvector, and the sparse coefficients of the eigenvector corresponding to the multiple training data included in the original downlink channel data can be used as the third downlink channel data. Since real training is used for network training, the data input is divided into two parts, a real part and an imaginary part, and the dimension of the third downlink channel data is, for example, [E, 2, 32, 13].

[0192] To obtain M third downlink channel subdata, F consecutive data strings are extracted from the third downlink channel data, and the F consecutive data strings are divided and assigned to M data spaces. In FIG. 9D, M=4 is used as an example. These four third downlink channel subdata can be uniformly represented as y1, and the dimension of y1 is [4×S, 16×13]. In FIG. 9B, q1 represents a dictionary to which the M data spaces correspond uniformly, that is, in FIG. 9C, N=1 is used as an example. The UE trains the dictionary with a clustering method.

[0193] In the network inference process, for each data space, the UE may independently find the corresponding first information in the corresponding dictionary. As shown in FIG. 9D, when the M data spaces correspond to one dictionary uniformly, in the network inference process, for each data space, the UE may find the corresponding first information in the dictionary obtained by the training of FIG. 9D. FIG. 9D is still used as an example. In this case, the UE may determine four first information. For example, the UE transmits identifiers of the four first information to the access network device. After receiving the identifiers of the four first information, the access network device may reconstruct the four first information based on the four dictionaries. Then, the access network device performs processing such as concatenation on the four first information to reconstruct the downlink channel information, i.e., to reconstruct the downlink channel matrix.

[0194] The network training process described in the embodiment shown in Figure 8 is a process of obtaining a dictionary by training. The above also describes the information that an encoder network may be configured on the UE side, and a decoder network corresponding to the encoder network may be configured on the access network device side. In this case, another network training process is a process of training the encoder network, the decoder network, and the dictionary together. The following describes yet another communication method in the present disclosure. In this method, a joint training process is described. Figure 10 is a flowchart of this method.

[0195] S1001: A second node acquires fifth downlink channel data.

[0196] The fifth downlink channel data is, for example, the original downlink channel data. Alternatively, the fifth downlink channel data may be data obtained by pre-processing the original downlink channel data. Alternatively, the fifth downlink channel data may be data output by the neural network. The original downlink channel data may be considered as training data or may be called training samples. In the process of training the dictionary, the second node needs to train the training samples. The original downlink channel data may include one or more training data.

[0197] If the fifth downlink channel data is obtained by pre-processing the original downlink channel data, a pre-processing process is included.For the pre-processing process of the original downlink channel data, please refer to the description of the pre-processing process of the second downlink channel data in S501 of the embodiment shown in Figure 5.

[0198] In the present disclosure, the second node may be, for example, a UE or an access network device, or a third party device (e.g., an AI node). The training process may be an online training process or an offline training process. The second node and the first node in the embodiment shown in FIG. 8 may be the same node or different nodes.

[0199] The second node may use the fifth downlink channel data to perform joint training on the encoder network, the dictionary, and the decoder network. The following describes the training process by performing S1002 to S1006.

[0200] S1002: The second node inputs the fifth downlink channel data to the encoder network to obtain the sixth downlink channel data output by the encoder network.

[0201] The encoder network is an encoder network that needs to be trained. The second node inputs the fifth downlink channel data into the encoder network, and the encoder network may perform processing, such as compression, on the fifth downlink channel data. After processing, the encoder network outputs the sixth downlink channel data.

[0202] S1003: The second node obtains M sixth downlink channel subdata. Each sixth downlink channel subdata corresponds to one data space in the M data spaces. The M data spaces in the present disclosure and the M data spaces in the embodiment shown in FIG. 5 may have the same characteristics.

[0203] The M sixth downlink channel subdata are obtained based on the sixth downlink channel data. For example, the M sixth downlink channel subdata can be obtained by dividing the sixth downlink channel data and allocating it to M data spaces. For further content of S1001, please refer to S801 of the embodiment shown in FIG. 8.

[0204] S1004: The second node obtains M pieces of third information based on the M pieces of sixth downlink channel sub data and the N training object dictionaries.

[0205] For example, in the process of training the dictionary, the second node may train a training target dictionary based on the ith data space in the M data spaces, where i is an integer from 1 to M, and the second node may train M training target dictionaries. The second node trains a training target dictionary corresponding to a data space based on the ith data space in the M data spaces. For example, in the training method, for the ith sixth downlink channel subdata in the M sixth downlink channel subdata, if the second node obtains third information corresponding to the ith sixth downlink channel subdata based on the training target dictionary corresponding to the ith data space, the second node may obtain M third information in total. For example, the third information corresponding to the ith sixth downlink channel subdata is an element corresponding to the ith sixth downlink channel subdata in the training target dictionary corresponding to the ith data space.

[0206] Before training begins, an initial model may be set as a training target dictionary, and multiple trainings (here, a process of training using one training data may be considered as one training process) are performed on the initial model using original downlink channel data. After the training is completed, a dictionary used in the network inference stage may be obtained. Thus, the training target dictionary corresponding to the i-th data space may be the initial model, or may be an intermediate model obtained by performing at least one training on the initial model.

[0207] Optionally, in the process of training the dictionary, the second node may further train the trained dictionary based on the M data spaces, and the second node may obtain the same M dictionaries or one dictionary through training. The second node trains the trained dictionary based on the M data spaces. For example, in the training method, for the i-th sixth downlink channel subdata in the M sixth downlink channel subdata, if the second node obtains third information corresponding to the i-th sixth downlink channel subdata based on the training dictionary, the second node may obtain M pieces of third information in total. For example, the third information corresponding to the i-th sixth downlink channel subdata is an element corresponding to the i-th sixth downlink channel subdata in the training dictionary.

[0208] S1005: For the i-th third information among the M pieces of third information, the second node reconstructs the i-th fifth downlink channel subdata based on the training target dictionary corresponding to the i-th data space among the M pieces of data spaces. When i is an integer from 1 to M, the second node may obtain M pieces of fifth downlink channel subdata in total.

[0209] In an ideal state, the M fifth downlink channel sub-data acquired by the second node and the M sixth downlink channel sub-data acquired by the second node can be the same data. For example, the i sixth downlink channel sub-data and the i fifth downlink channel sub-data are the same data. In practical application, there may be a gap between the M sixth downlink channel sub-data and the M fifth downlink channel sub-data. For content, please refer to S504 in the embodiment shown in FIG. 5.

[0210] For further details of S1005, please refer to S504 in the embodiment shown in FIG.

[0211] S1006: The second node inputs the M fifth downlink channel subdata to the decoder network to obtain L reconstructed information output by the decoder network, where L is a positive integer. Alternatively, the second node concatenates the M fifth downlink channel subdata, and inputs the concatenated downlink channel subdata to the decoder network to obtain the first reconstructed information output by the decoder network.

[0212] The decoder network is the decoder network that needs to be trained and is also the decoder network that corresponds to the encoder network of S1002.

[0213] For example, the original downlink channel data may include a plurality of training data, one of the plurality of training data may include training sub-data and a label. The second node may input the training sub-data to the encoder network to obtain encoded data, and after the encoded data is processed by the decoder network, the decoder network may output an inference result (e.g., the L reconstructed information or the first reconstructed information described in this disclosure). The second node may calculate an error between the inference result and the label according to a loss function. Based on the error, the second node may optimize parameters of the encoder network and / or the decoder network according to a backpropagation optimization algorithm (or called a model optimization algorithm, etc.). The encoder network and the decoder network are trained using a large amount of training data, such that the neural network training is completed after the difference between the output of the decoder network and the label is less than a preset value.

[0214] It should be noted that in the above-mentioned training process of the encoder network and the decoder network, a supervised learning training method is used, that is, a loss function is used to implement the training of the encoder network and the decoder network based on the training data and the label. Alternatively, in the training process of the intelligent model, unsupervised learning may be used, and an algorithm is used to learn the internal pattern of the training data to accomplish the training of the intelligent model based on the training data. In the training process of the intelligent model, reinforcement learning may be used, and an excitation signal that is fed back by the environment is obtained by interacting with the environment to learn a problem-solving policy and optimize the model. In the present disclosure, the model training method, model type, etc. are not limited.

[0215] From the above description, it can be known that when training the encoder network and the decoder network, the second node may perform training according to a loss function. Optionally, the same loss function may be set for the M data spaces. In other words, for all data spaces in the M data spaces, the second node may perform joint training according to this loss function. For example, when the decoder network outputs L reconstructed information, the mean square error (MSE) between the fifth downlink channel data and the data obtained by concatenating the L reconstructed information reconstructed by the decoder network may be used as the loss function, or the correlation between the third downlink channel data and the data obtained by concatenating the L reconstructed information reconstructed by the decoder network may be used as the loss function. Alternatively, when the decoder network outputs the first reconstructed information, the MSE between the first reconstructed information and the fifth downlink channel data may be used as the loss function, or the correlation between the first reconstructed information and the third downlink channel data may be used as the loss function, etc.

[0216] Alternatively, different loss functions may be set for different data spaces. For example, when the decoder network outputs L pieces of reconstructed information, the MSE between the reconstructed information reconstructed by the decoder network and the data input to the encoder network may be used as the loss function corresponding to the data space. The reconstructed information reconstructed by the decoder network corresponding to the loss function is the reconstructed information reconstructed by the decoder network corresponding to the data space. The data input to the encoder network corresponding to the loss function is the data in the fifth downlink channel data input to the encoder network corresponding to the data space.

[0217] In the above process, the second node obtains N dictionaries and performs joint training on the encoder network, the decoder network, and the dictionary to obtain an encoder network and a corresponding decoder network. In this case, the UE can use the N dictionaries and the encoder network in the network inference process in the embodiment shown in FIG. 5, and the access network device can also use the N dictionaries and the decoder network in the process of reconstructing downlink channel information. The data space and the N dictionaries are divided, so that the environmental information corresponding to the downlink channel can be reflected, which helps the access network device to reconstruct more accurate downlink channel information. If the encoder network and the decoder network are not used in the embodiment shown in FIG. 5, the network training method provided in the embodiment shown in FIG. 8 can be used to train the dictionary separately. If the encoder network and the decoder network need to be used in the embodiment shown in FIG. 5, the network training method provided in the embodiment shown in FIG. 10 can be used to obtain the encoder network, the decoder network, and the dictionary by joint training.

[0218] For example, FIG. 11 is a diagram of a training process and a network inference process according to the present disclosure. In the training process, for example, an encoder network, a decoder network, and a dictionary are obtained by joint training, and the encoder network, the decoder network, and the dictionary can be used in the network inference process. The process from the third downlink channel data to q1 to q4, that is, the process before the information is transmitted to the access network device, can be considered as a training process. The entire process in FIG. 11 can also be considered as a network inference process, and the network inference process can also be considered as a processing process of training data. Of course, the data is not actually the training data used for training, but the processing process of the data is consistent with the processing process of the training data.

[0219] In the training process, it is assumed that the original downlink channel data may include multiple training data. The UE processes each training data in the original downlink channel data to obtain an eigenvector, and the dimension of the eigenvector is [N tx =32,N sb = 13]. The UE pre-processes the eigenvector to obtain sparse coefficients of the eigenvector. The UE compresses the sparse coefficients of the eigenvector using an encoder network to obtain compressed information. The compressed information corresponding to the plurality of training data may be used as the third downlink channel data.

[0220] The UE divides the third downlink channel data and allocates it to four data spaces to obtain four third downlink channel subdata, and the four third downlink channel subdata are y1, y2, y3, and y4. The dimensions of y1, y2, y3, and y4 are [S, 16×13], respectively, where S represents the amount of training data corresponding to one third downlink channel subdata, and 16×13 is, for example, the dimension of the training target dictionary. q1 to q4 in FIG. 11 represent four training target dictionaries, that is, N=M is ​​used as an example in FIG. 11. The UE trains the four dictionaries in a clustering method.

[0221] In the inference process, for example, the UE may obtain four pieces of first information based on four training object dictionaries and four pieces of first downlink channel subdata, where one piece of first information is an element corresponding to one piece of first downlink channel subdata in a corresponding dictionary.

[0222] For example, the UE may send four first information identifiers to the access network device, and each first information identifier may occupy X bits. After receiving the four first information identifiers, the access network device may reconstruct the four compressed sub-information based on the four dictionaries. The access network device performs processing such as concatenation on the four compressed sub-information to obtain the reconstructed information output by the decoder network, and then inputs the processing result into the decoder network. After obtaining the reconstructed information output by the decoder network, the access network device may reconstruct the downlink channel information based on the reconstructed information. The loss function in the embodiment shown in FIG. 10 may be applied in the training process, which makes the performance of the encoder / decoder network obtained by training better.

[0223] The communication device provided in the present disclosure is described based on the above method embodiments.

[0224] It can be understood that in order to implement the functions in the above-mentioned manner, the access network device and the UE, etc., include corresponding hardware structures and / or software modules for performing the functions. Those skilled in the art should easily recognize that the present disclosure can be implemented by hardware or a combination of hardware and computer software with reference to the units and method steps in the examples described in the present disclosure. Whether the functions are performed by hardware or by hardware driven by computer software depends on the specific application scenario and design constraints of the technical solution.

[0225] The present disclosure provides a communication device. The communication device includes, for example, a processing unit and a transceiver unit (or referred to as a communication unit). The processing unit may be configured to implement the processing functions of the UE in the embodiment shown in FIG. 5, the embodiment shown in FIG. 8, or the embodiment shown in FIG. 10, and the transceiver unit may be configured to implement all or a part of the transceiver functions of the UE in the embodiment shown in FIG. 5, the embodiment shown in FIG. 8, or the embodiment shown in FIG. 10. Alternatively, the processing unit may be configured to implement the processing functions implemented by the access network device in the embodiment shown in FIG. 5, the embodiment shown in FIG. 8, or the embodiment shown in FIG. 10, and the transceiver unit may be configured to implement all or a part of the transceiver functions of the access network device in the embodiment shown in FIG. 5, the embodiment shown in FIG. 8, or the embodiment shown in FIG. 10.

[0226] Optionally, the processing unit and / or the transceiver unit may be implemented using a virtual module. For example, the processing unit may be implemented using a software functional unit or a virtual device, and the transceiver unit may be implemented using a software functional unit or a virtual device. Alternatively, the processing unit and / or the transceiver unit may be implemented 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 using a physical device.

[0227] FIG. 12 is a diagram of the structure of a communication device according to the present disclosure. The communication device 1200 may be a UE, a circuit system of a UE, or a circuit system that can be used in a UE in the embodiment shown in FIG. 5, the embodiment shown in FIG. 8, or the embodiment shown in FIG. 10, etc., configured to implement a method corresponding to the UE in the above-mentioned method embodiment. Alternatively, the communication device 1200 may be an access network device, a circuit system of an access network device, or a circuit system that can be used in an access network device in the embodiment shown in FIG. 5, the embodiment shown in FIG. 8, or the embodiment shown in FIG. 10, etc., configured to implement a method corresponding to the access network device in the above-mentioned method embodiment. For specific functions, please refer to the description of the above-mentioned method embodiment. For example, the circuit system is a chip system.

[0228] The communication device 1200 includes one or more processors 1201. The processor 1201 may perform certain control functions. The processor 1201 may be a general-purpose processor or a dedicated processor, etc. For example, the processor 1201 includes a baseband processor and 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 1200, execute software programs, and / or process data. The different processors may be independent components or may be arranged in one or more processing circuits, for example integrated in one or more application specific integrated circuits.

[0229] Optionally, the communication device 1200 includes one or more memories 1202 for storing instructions 1204. The instructions 1204 may be operated on the processor so that the communication device 1200 executes the method described in the method embodiment above. Optionally, the memory 1202 may further store data. The processor and the memory may be separately located or integrated together. The memory may be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), or a volatile memory, such as a random access memory (RAM). The memory is any other medium that can be accessed by a computer that can hold or store appropriate program code in the form of instructions or data structures, but is not limited thereto. Alternatively, the memory of the present disclosure may be a circuit or any other device that can perform a storage function and is configured to store program instructions and / or data.

[0230] Optionally, the communication device 1200 may store instructions 1203 (which may also be referred to as code or programs). The instructions 1203 may be run on a processor such that the communication device 1200 executes the methods described in the above embodiments. The processor 1201 may store data.

[0231] For example, the processing unit is implemented using one or more processors 1201, or the processing unit is implemented using one or more processors 1201 and one or more memories 1202, or the processing unit is implemented using one or more processors 1201, one or more memories 1202, and instructions 1203.

[0232] Optionally, the communications device 1200 may further include a transceiver 1205 and an antenna 1206. The transceiver 1205 may be referred to as a transceiver unit, a transceiver, a transceiver circuit, a transceiver machine, or an input / output interface, etc., and is configured to implement a transceiver function of the communications device 1200 via the antenna 1206. For example, the transceiver unit is implemented using the transceiver 1205, or the transceiver unit is implemented using the transceiver 1205 and the antenna 1206.

[0233] Optionally, the communication device 1200 may further include 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, or a display, etc. It may be understood that in some embodiments, the communication device 1200 may include more or fewer components, or integration of some of the components, or division from some of the components. These components may be implemented by hardware, software, or a combination of software and hardware.

[0234] The processor 1201 and the transceiver 1205 described in this disclosure may be implemented in an integrated circuit (IC), an analog IC, a radio frequency identification (RFID), a mixed signal IC, an application specific integrated circuit (ASIC), a printed circuit board (PCB), or an electronic device, etc. The communication device described herein may be an independent device (e.g., an independent integrated circuit or a mobile phone), or may be a part of a larger device (e.g., a module that can be incorporated into another device). For details, please refer to the description of the UE and the access network device in the preceding embodiment. Details will not be described again here.

[0235] The present disclosure provides a terminal device, which may 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, the embodiment shown in Figure 8, or the embodiment shown in Figure 10. For example, the terminal device includes a transceiver module (or referred to as a transceiver unit) configured to support the terminal device in implementing transceiver functions, and a processing module (or referred to as a processing unit) configured to support the terminal device in processing signals.

[0236] 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, the embodiment shown in Figure 8, or the embodiment shown in Figure 10. For example, the access network device includes a transceiver module (or referred to as a transceiver unit) configured to support the access network device in implementing transceiver functions, and a processing module (or referred to as a processing unit) configured to support the access network device in processing signals.

[0237] All or part of the technical solutions provided in the present disclosure may be implemented using software, hardware, firmware, or any combination thereof. When software is used to implement the embodiments, 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 and executed on a computer, 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 one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired (e.g., coaxial cable, optical fiber, or digital subscriber line (DSL)) or wireless (e.g., infrared, radio, or microwave) manner. The computer-readable storage medium may be any available medium accessible by a computer, or a data storage device, such as a server or data center, incorporating one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk drive, or a magnetic tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium, etc.

[0238] The above description is merely a specific embodiment of the present disclosure, and is not intended to limit the scope of protection of the present disclosure. Any variation or replacement that is easily conceived by a person skilled in the art within the technical scope disclosed in the present disclosure shall be 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]

[0239] 10. Communication Systems 20 Access Network Devices 30 Communication Equipment 1200 Communication Equipment 1201 Processor 1202 Memory 1203 Instructions 1204 Instructions 1205 Transceiver 1206 Antenna

Claims

1. acquiring M first downlink channel subdata, each of the first downlink channel subdata corresponding to a data space among the M data spaces, where M is an integer greater than 1; for an i-th first downlink channel subdata among the M first downlink channel subdata, determining first information corresponding to the i-th first downlink channel subdata based on a first dictionary corresponding to an i-th data space among the M data spaces, a total of M pieces of first information are determined, i is an integer from 1 to M, the i-th first downlink channel subdata corresponds to the i-th data space, the first dictionary includes a plurality of elements, and the first information corresponding to the i-th first downlink channel subdata corresponds to P elements among the plurality of elements, where P is a positive integer; A step of transmitting first indication information, the first indication information indicating the M pieces of first information; A communication method including:

2. The first indication information indicates identifiers of the M pieces of first information, and the step of transmitting the first indication information includes: transmitting the identifiers of the M pieces of first information in a first order, the first order being an arrangement order of the M pieces of data space; 2. The method of claim 1, comprising:

3. the first order is a predefined order, or a second indication is received, the second indication indicating the first order; or the first order is determined, and third instruction information is transmitted, the third instruction information indicating the first order; The method of claim 2.

4. The M first downlink channel subdata are obtained based on the first downlink channel data; The first downlink channel data is a pre-processed result, The first downlink channel data includes F consecutive data streams in the pre-processing result; or The first downlink channel data is compressed information obtained by compressing a pre-processing result; The pre-processing result is obtained by pre-processing second downlink channel data.

4. The method according to any one of claims 1 to 3.

5. The division method of the M data spaces is predefined; A fourth instruction is received, the fourth instruction indicating how to divide the M data spaces; or A division method of the M data spaces is determined, and fifth instruction information is transmitted, the fifth instruction information indicating the division method of the M data spaces.

5. The method according to any one of claims 1 to 4.

6. receiving first indication information, the first indication information indicating M pieces of first information, where M is an integer greater than 1; reconstructing an i-th second downlink channel subdata based on a first dictionary corresponding to an i-th data space among the M data spaces for an i-th first information among the M first information, where a total of M pieces of the second downlink channel subdata are obtained, the i-th first information corresponds to the i-th data space, where i is an integer from 1 to M, the first dictionary includes a plurality of elements, and the first information corresponding to the i-th second downlink channel subdata corresponds to P elements among the plurality of elements; reconstructing downlink channel information based on the M second downlink channel subdata; A communication method including:

7. The step of receiving first indication information includes: receiving the M first information identifiers in a first order, the first order being an arrangement order of the M data spaces; 7. The method of claim 6, comprising:

8. the first order is a predefined order, or A second indication is sent, the second indication indicating the first order; or Third indication information is received, the third indication information indicating the first order. The method of claim 7.

9. 9. The method of claim 6, wherein the M data spaces correspond to M dictionaries, each of the data spaces corresponding to one of the dictionaries, or the M data spaces all correspond to the same dictionary.

10. The step of reconstructing downlink channel information based on the M second downlink channel subdata includes: obtaining compression information based on the M second downlink channel sub-data; obtaining the downlink channel information based on the compressed information; 10. The method according to any one of claims 6 to 9, comprising:

11. The division method of the M data spaces is predefined; A fourth instruction is sent, the fourth instruction indicating how to divide the M data spaces; or A fifth instruction is received, the fifth instruction indicating a method of dividing the M data spaces.

11. The method according to any one of claims 6 to 10.

12. A communications device configured to implement the method according to any one of claims 1 to 5 or configured to implement the method according to any one of claims 6 to 11.

13. A communications device comprising a processor and a memory, the memory coupled to the processor, the processor configured to perform a method according to any one of claims 1 to 5 or to perform a method according to any one of claims 6 to 11.

14. A computer readable storage medium, the computer readable storage medium configured to store a computer program, the computer program being enabled, when run on a computer, to perform the method according to any one of claims 1 to 5, or the computer being enabled to perform the method according to any one of claims 6 to 11.

15. A communication system comprising an apparatus configured to implement the method according to any one of claims 1 to 5 and / or an apparatus configured to implement the method according to any one of claims 6 to 11.

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