Channel estimation method and related apparatus

Abstract

The present application provides a channel estimation method and a related apparatus, improving the accuracy of channel estimation. The method comprises: a terminal device receiving a first signal, the first signal being a signal received by the terminal device after a second signal sent by a network device passes through a wireless channel; receiving first information, the first information being used for indicating a first channel estimation parameter, and the first channel estimation parameter including at least one of the following: a matrix norm of a first expression and an iteration step size, wherein the first expression is determined by a precoding matrix, the first signal, and a kth channel estimation value, at least one of the matrix norm and the iteration step size is determined on the basis of the precoding matrix, k is a positive integer less than or equal to T, T is a positive integer, and T is the number of iterations of channel estimation; and on the basis of the first signal and the first channel estimation parameter, performing channel estimation T times to obtain a channel estimation result.

Description

Channel estimation method and related device

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on March 22, 2024, with application number 202410341276.X and application name “Channel Estimation Method and Related Device”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of communication technology, and in particular to a channel estimation method and related devices. Background Art

[0003] In wireless communication systems, signals transmitted through wireless channels are subject to noise and interference, resulting in errors between the signals received by the receiver and those sent by the transmitter. To mitigate the effects of noise and interference, communication devices must determine the characteristics of the wireless channel. This process is called channel estimation.

[0004] For example, the communication device can perform channel estimation using a channel estimation algorithm and a received signal. Currently, the following channel estimation algorithms have been proposed: classical channel estimation algorithms based on mathematical closed-form solutions (e.g., the least squares (LS) method and the minimum mean squared error (MMSE) method). These algorithms have simple calculation processes but are difficult to obtain accurate channel estimation results in all scenarios.

[0005] Improving the accuracy of channel estimation results is an urgent problem to be solved. Summary of the Invention

[0006] The present application provides a channel estimation method and related devices to improve the accuracy of channel estimation.

[0007] In a first aspect, the present application provides a channel estimation method that can be applied to a first communication device. For example, the first communication device can be a terminal device, or a component configured in the terminal device (such as a chip, a chip system, etc.), or a logic module or software that can implement all or part of the terminal device functions, which is not limited by the present application.

[0008] The method includes: receiving a first signal; receiving first information, where the first information is used to indicate parameters for channel estimation, and the channel estimation parameters include at least one of the following: a matrix norm of a first expression, or an iteration step, and at least one of the matrix norm or the iteration step is determined based on a precoding matrix; performing T channel estimations based on the first signal and the channel estimation parameters to obtain a channel estimation result, where T is a positive integer and T is the number of iterations of the channel estimation.

[0009] The first expression is determined by the precoding matrix, the first signal and the k-th channel estimation value, where k is a positive integer less than or equal to T.

[0010] Exemplarily, the first expression may be: Wherein, y represents the first signal (i.e., the signal received by the terminal device), A represents the precoding matrix, represents the k-th channel estimation value.

[0011] It can be understood that the above-mentioned T-times channel estimation can be understood as performing T iterations, and the initial input of the T iterations (ie, the input of the first iteration) is the initial value of the first signal and the channel. The input of the kth iteration in T iterations is the output of the k-1th iteration. In this way, the channel estimation result obtained by the terminal device can be the output of the Tth iteration after the terminal device performs T iterations.

[0012] Based on this technical solution, the terminal device performs channel estimation based on the matrix norm and iteration step of the obtained first expression and the received signal. The matrix norm and iteration step are two parameters obtained based on the fixed point theory. Therefore, it can be said that the terminal device adopts the fixed point theory when performing channel estimation, and the application of the fixed point theory can theoretically ensure that the optimal solution converges to the fixed point, that is, the T channel estimations performed in this application can ensure that the channel estimation value converges to a fixed value, and the terminal device can determine the fixed value as the channel estimation result. Therefore, the channel estimation method provided in this application can improve the accuracy of channel estimation.

[0013] Optionally, the first information includes the first channel estimation parameter. The first channel estimation parameter may include a specific value of a matrix norm and / or an iteration step size; or include a calculation formula for the matrix norm and / or the iteration step size, and the terminal device determines the matrix norm and / or the iteration step size based on the calculation formula.

[0014] Optionally, the first information indicates the first channel estimation parameter by carrying a first index. That is, the first information includes the first index, and the first index and the first channel estimation parameter satisfy a corresponding relationship.

[0015] In combination with the first aspect, in some implementations, the correspondence is predefined or indicated by the network side, and the correspondence includes a correspondence between each parameter group in at least one parameter group and an index, and the at least one parameter group includes the first channel estimation parameter.

[0016] It can be understood that the at least one index includes the first index.

[0017] Optionally, in the case where the corresponding relationship is a network-side indication, the method further includes: receiving third information from a network device, where the third information is used to configure the corresponding relationship.

[0018] Exemplarily, the third information may be carried in non-access stratum (NAS) signaling or radio resource control (RRC); the first information may be carried in downlink control information (DCI) or a medium access control (MAC) control element (CE).

[0019] In conjunction with the first aspect, in some implementations, among the T channel estimates, the k-th channel estimate satisfies: described is a fixed point operator designed based on fixed point theory. It can be understood that the matrix norm and iteration step size of the first expression can be Parameters in .

[0020] in, represents the output of the k-th channel estimation, represents the output of the k-1th channel estimation, Indicates that the first signal y and the is input, and the output is obtained by performing the k-th channel estimation.

[0021] It is understood that when the value of k is not 1, the input of the kth channel estimation may not include the first signal y. That is, the first signal y may be input only once during the first channel estimation and remain unchanged during the subsequent T-1 channel estimations.

[0022] In combination with the first aspect, in some implementations, the Including a nonlinear estimator, in the k-th channel estimation: the input of the nonlinear estimator is The output of the nonlinear estimator is

[0023] The input and output of the nonlinear estimator are the same as the input and output of the kth channel estimation in the Tth channel estimation, so the above-mentioned Tth channel estimation can be implemented by the nonlinear estimator.

[0024] In combination with the first aspect, in some implementations, the It includes a linear estimator and a nonlinear estimator. In the k-th channel estimation, the input of the linear estimator is The output of the linear estimator is the input of the nonlinear estimator, and the output of the nonlinear estimator is

[0025] The input of the linear estimator is the same as the input of the k-th channel estimation in the T-th channel estimation, and the output of the nonlinear estimator is the same as the output of the k-th channel estimation in the T-th channel estimation. Therefore, the above-mentioned T-th channel estimation can be realized by the linear estimator and the nonlinear estimator.

[0026] Optionally, the The nonlinear estimator included in is implemented through a neural network model.

[0027] In combination with the first aspect, in some implementations, the method further includes: obtaining parameters of the neural network model, where the neural network model is used for channel estimation.

[0028] Optionally, obtaining the parameters of the neural network model includes: receiving second information, where the second information indicates the parameters of the neural network model.

[0029] Optionally, obtaining the parameters of the neural network model includes: training the neural network model to obtain the parameters of the neural network model.

[0030] Optionally, the terminal device may determine the parameters of the neural network model based on the parameters of the above channel estimation.

[0031] Exemplarily, the terminal device may train the neural network model based on at least one matrix norm and / or iteration step size included in the pre-acquired correspondence to obtain neural network model parameters corresponding to different parameter groups. In this way, upon receiving the first information, the terminal device may determine the neural network model parameters corresponding to the parameter group indicated by the first information from the pre-acquired neural network model parameters to perform channel estimation.

[0032] In combination with the first aspect, in some implementations, the value of T is predefined or indicated by the network side.

[0033] Optionally, when the value of T is indicated by the network side, the method further includes: receiving fourth information, where the fourth information indicates the value of T.

[0034] Optionally, the value of T may also be determined by the terminal design based on the channel estimation result. For example, if the terminal device performs 15 channel estimations and the channel estimation value obtained converges to a certain value, then T=15.

[0035] In conjunction with the first aspect, in some implementations, the T-time channel estimation is implemented by a channel estimator, and a mathematical model of the channel estimator is:

[0036] P:

[0037] Constraints:

[0038] in, is the fixed point equation, is the solution of the fixed point equation, representing the channel estimation value; y is the first signal, h is the true value of the channel, Express Find the mean; the physical meaning of P is: to obtain the function that minimizes the channel estimation error

[0039] The channel estimation error is the error between the true channel value and the channel estimation value.

[0040] In a second aspect, the present application provides a channel estimation method that can be applied to a second communication device. For example, the second communication device can be a network device, or a component configured in the network device (such as a chip, chip system, etc.), or a logic module or software that can implement all or part of the network device functions, which is not limited by the present application.

[0041] The method includes: sending a second signal; determining first information based on a precoding matrix, wherein the first information is used to indicate a first channel estimation parameter, and the first channel estimation parameter includes at least one of the following: a matrix norm of a first expression, or an iteration step size, wherein the first expression is determined by the precoding matrix, the first signal, and the kth channel estimation value, where k is a positive integer less than or equal to T, T is a positive integer, and T is the number of iterations of the channel estimation; and sending the first information.

[0042] The first signal is a signal obtained by passing the second signal through a wireless channel, and the first signal and the first channel estimation parameter are used to perform T-times channel estimation.

[0043] In a possible implementation, the precoding matrix is ​​a downlink precoding matrix determined by the network device based on a precoding matrix indicator (PMI) and a rank indication (RI) indicated by the terminal device.

[0044] For the description of the first expression, please refer to the relevant description of the first aspect and will not be repeated here.

[0045] Based on this technical solution, the network device indicates the matrix norm and iteration step of the first expression to the terminal device, so that the terminal device can perform channel estimation based on the obtained matrix norm and iteration step, as well as the received signal. The matrix norm and iteration step are two parameters obtained based on the fixed point theory. Therefore, it can be said that the terminal device adopts the fixed point theory when performing channel estimation, and the application of the fixed point theory can theoretically ensure that the optimal solution converges to the fixed point, that is, the T channel estimations performed in this application can ensure that the channel estimation value converges to a fixed value, and the terminal device can determine the fixed value as the channel estimation result. Therefore, the channel estimation method provided in this application can improve the accuracy of channel estimation.

[0046] Optionally, the network device may directly or indirectly indicate the first channel estimation parameter to the terminal device through the first information.

[0047] Exemplarily, when the network device directly indicates the first channel estimation parameter to the terminal device through first information, the first information includes the first channel estimation parameter.

[0048] Exemplarily, when the network device indirectly indicates the first channel estimation parameter to the terminal device through first information, the first information may include a first index, and the first index and the first channel estimation parameter satisfy a corresponding relationship.

[0049] For a more detailed description of the first information, please refer to the relevant description of the first aspect, which will not be repeated here.

[0050] In combination with the first aspect, in some implementations, the method further includes: sending third information, wherein the third information is used to configure the correspondence, wherein the correspondence includes a correspondence between each channel estimation parameter and an index in at least one channel estimation parameter, and the at least one channel estimation parameter includes the first channel estimation parameter.

[0051] It can be understood that the at least one index includes the first index.

[0052] For the description of the third information, please refer to the relevant description of the first aspect and will not be repeated here.

[0053] In combination with the second aspect, in some implementations, the method further includes: sending fourth information, where the fourth information indicates a value of the number of channel estimation times T.

[0054] The value of T may be determined by the network device based on the number of iterations required for historical channel estimation.

[0055] In conjunction with the second aspect, in some implementations, the matrix norm of the first expression and / or the iteration step size are parameters for channel estimation, and the channel estimation satisfies: described It is a fixed point operator designed based on fixed point theory.

[0056] in, represents the output of the k-th channel estimation, represents the output of the k-1th channel estimation, Indicates that the first signal y and the is input, and the output is obtained by performing the k-th channel estimation.

[0057] Optionally, the including a nonlinear estimator; or, It includes a linear estimator and a nonlinear estimator, and the output of the linear estimator is the input of the nonlinear estimator.

[0058] Optionally, the The included nonlinear estimators are implemented via neural network models.

[0059] In combination with the second aspect, in some implementations, the method further includes: obtaining parameters of the neural network model, where the neural network model is used for channel estimation; and sending second information, where the second information indicates the parameters of the neural network model.

[0060] Optionally, the network device may obtain parameters of the neural network model by training the neural network model.

[0061] Similar to the first aspect, the network device can train the neural network model based on at least one matrix norm and / or iteration step size included in the pre-acquired correspondence to obtain neural network model parameters corresponding to different parameter groups. In this way, when the network device determines the first information, it can determine the neural network model parameters corresponding to the parameter group indicated by the first information from the pre-acquired neural network model parameters and indicate them to the terminal device.

[0062] In combination with the first and second aspects, in some implementations, the satisfy:

[0063] Where I is the identity matrix, g is the regularization term, Indicates the subgradient of g, A is the precoding matrix, M is the matrix norm, σ is the iteration step size.

[0064] For example, in And when A is the unit matrix, the Including nonlinear estimators, excluding linear estimators, and the mathematical model of the nonlinear estimator can be

[0065] For example, in And when A is not a unit matrix, the It includes nonlinear estimators and linear estimators, and the mathematical model of the linear estimator can be: The mathematical model of the nonlinear estimator can be

[0066] in, This can be achieved through a neural network model.

[0067] In the third aspect, the present application provides a communication device, which can be used for the first communication device of the first aspect. The communication device can be a terminal device, or a device of the terminal device (for example, a chip, or a chip system, or a circuit), or a device that can be used in combination with the terminal device, or a device that can implement all or part of the terminal device logic module or software; or, the communication device is used for the second communication device of the second aspect, which can be a network device, or a device of the network device (for example, a chip, or a chip system, or a circuit), or a device that can be used in combination with the network device, or a device that can implement all or part of the network device logic module or software.

[0068] In one possible implementation, the communication device may include a module or unit corresponding to the method / operation / step / action described in the first aspect. The module or unit may be a hardware circuit, software, or a combination of hardware circuit and software.

[0069] In one possible implementation, each module or unit may implement corresponding functions by executing a computer program.

[0070] In one possible implementation, the communication device may include a transceiver module and a processing module. The transceiver module is configured to receive a first signal and first information, and the processing module is configured to perform T channel estimations based on the first signal and the first channel estimation parameters to obtain a channel estimation result. Alternatively, the transceiver module is configured to transmit a second signal, and the processing module is configured to determine the first information based on a precoding matrix. The transceiver module is further configured to transmit the first information.

[0071] For the description of the first information, the first signal and the second signal, please refer to the description of the first and second aspects above, which will not be repeated here.

[0072] In a fourth aspect, the present application provides a communication device, comprising a processor, wherein the processor is configured to execute the method described in any of the above aspects and any possible implementation of any of the aspects.

[0073] The apparatus may further include a memory for storing instructions and / or data. The memory is coupled to the processor, and when the processor executes the instructions stored in the memory, the methods described in the above aspects may be implemented.

[0074] The apparatus may further include a communication interface, where the communication interface is used for the apparatus to communicate with other devices. Exemplarily, the communication interface may be a transceiver, a circuit, a bus, a module, or other types of communication interfaces.

[0075] In a fifth aspect, the present application provides a chip system comprising at least one processor for supporting the implementation of the functions involved in any of the above aspects and any possible implementation of any aspect, for example, receiving or processing the data and / or information involved in the above method.

[0076] In one possible design, the chip system further includes a memory, which is used to store program instructions and data, and the memory is located inside or outside the processor.

[0077] The chip system can be composed of chips, or can include chips and other discrete devices.

[0078] In a sixth aspect, the present application provides a computer-readable storage medium comprising a computer program, which, when executed on a computer, enables the computer to implement the method in any of the above aspects and any possible implementation of any of the aspects.

[0079] In the seventh aspect, the present application provides a computer program product, which includes: a computer program (also referred to as code, or instructions), which, when executed, enables a computer to execute the method in any of the above aspects and any possible implementation of any aspect.

[0080] In an eighth aspect, the present application provides a communication system comprising the aforementioned terminal device and network device. The terminal device is configured to indicate the method in the aforementioned first aspect and any possible implementation thereof; and the network device is configured to execute the method in the aforementioned second aspect and any possible implementation thereof.

[0081] It should be understood that the third to eighth aspects of the present application correspond to the technical solutions of the first or second aspect of the present application, and the beneficial effects achieved by each aspect and the corresponding feasible implementation methods are similar and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] FIG1 is a schematic diagram of the architecture of a communication system applicable to the method provided in an embodiment of the present application;

[0083] FIG2 is a schematic flow chart of a channel estimation method provided in an embodiment of the present application;

[0084] FIG3 is a schematic diagram of the architecture of a neural network model provided in an embodiment of the present application;

[0085] FIG4 is a schematic diagram of a connection method between a linear estimator and a nonlinear estimator provided in an embodiment of the present application;

[0086] FIG5 is a schematic diagram showing a comparison of normalized mean square errors corresponding to various algorithms provided in an embodiment of the present application;

[0087] FIG6 is a schematic block diagram of a device provided in an embodiment of the present application;

[0088] FIG7 is another schematic block diagram of the apparatus provided in an embodiment of the present application. DETAILED DESCRIPTION

[0089] The technical solution in this application will be described below with reference to the accompanying drawings.

[0090] To facilitate understanding of the embodiments of the present application, the following points are first explained:

[0091] First, in the embodiments of this application, prefixes such as "first" and "second" are used solely to distinguish between different items belonging to the same category and do not constrain the order, size, or quantity of the items. For example, "first information" and "second information" are simply different signals; there is no temporal, size, or priority relationship between them.

[0092] Second, the "sending" and "receiving" in the embodiments of the present application indicate the direction of signal transmission. For example, "sending first information to a terminal device" can be understood as the destination end of the information being the terminal device, which can include direct sending through the air interface, and also includes indirect sending through the air interface by other units or modules. "Receiving second information from a network device" can be understood as the source end of the second information being the network device, which can include direct receiving from the network device through the air interface, and also includes indirect receiving from the network device through the air interface from other units or modules. "Sending" can also be understood as the "output" of the chip interface, and "receiving" can also be understood as the "input" of the chip interface.

[0093] In other words, sending and receiving can be carried out between devices, for example, between terminal devices and network devices; it can also be carried out within a device, for example, sending or receiving between components, modules, chips, software modules or hardware modules within the device through a bus, wiring or interface.

[0094] It is understood that before information is sent from the source to the destination, it may undergo necessary processing, such as encoding and modulation. After receiving the information from the source, the destination may also perform corresponding processing, such as decoding and demodulation, to interpret the valid information from the source. Similar expressions in this application can be understood similarly and will not be repeated here.

[0095] Third, in the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: the existence of A alone, the existence of A and B at the same time, and the existence of B alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship, but does not exclude the situation where the previous and next associated objects are in an "and" relationship. The specific meaning can be understood in conjunction with the context. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c; a and b; a and c; b and c; or a and b and c. Where a, b, c can be single or multiple.

[0096] Fourth, in the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information (such as the indication information described below) is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, directly indicating the information to be indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated may also be indirectly indicated by indicating other information, wherein the other information has an association relationship with the information to be indicated; or only a part of the information to be indicated may be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information may be achieved by means of the arrangement order of each information agreed in advance (such as predefined by the protocol), thereby reducing the indication overhead to a certain extent. The present application does not limit the specific method of indication.

[0097] It can be understood that, for the sender of the indication information, the indication information can be used to indicate the information to be indicated, and for the receiver of the indication information, the indication information can be used to determine the information to be indicated.

[0098] Fifth, the tables in the embodiments of the present application are only examples. The values ​​of the information in each table are only examples and can be configured as other values, which are not limited by the present application. The tables do not limit the scope of protection of the present application. For example, appropriate deformation adjustments can be made based on the tables in the text, such as splitting, merging, etc. For another example, the parameter names shown in the titles of the tables can also use other names that can be understood by the communication device, and the values ​​or representations of the parameters can also use other values ​​or representations that can be understood by the communication device. For another example, when implementing each table, other data structures can also be used, such as arrays, queues, containers, stacks, linear lists, pointers, linked lists, trees, graphs, structures, classes, heaps, hash tables or hash tables.

[0099] Sixth, in the embodiments of the present application, descriptions such as "when...", "in the case of...", "if" and "if" all mean that the device (such as the first device or the second device) will perform corresponding processing under certain objective circumstances. It does not limit the time, nor does it require the device to perform a judgment action when implemented, nor does it mean that there are other limitations.

[0100] Seventh, the pre-defined in the embodiments of the present application can be understood as: definition, pre-definition, storage, pre-storage, pre-negotiation, pre-configuration, solidification, or pre-burning.

[0101] The technical solutions provided in this application can be applied to various communication systems, such as: long term evolution (LTE) system, LTE frequency division duplex (FDD) system, LTE time division duplex (TDD) system, sidelink (SL) communication system, fifth generation (5G) mobile communication system or new radio access technology (NR), satellite communication system, etc. Among them, the 5G mobile communication system can include non-standalone (NSA) and / or standalone (SA) networking.

[0102] The technical solution provided in this application can also be applied to communication systems that evolve after 5G, such as the sixth generation (6G) mobile communication system, etc. This application does not limit this.

[0103] In this application, a radio access network (RAN) device is a device with wireless transceiver capabilities. It can provide wireless communication services and connect terminals to a wireless network. It can be a node in a radio access network, referred to as a RAN node.

[0104] In one possible scenario, a RAN node can be a base station (BS), an evolved NodeB (eNodeB), a transmission reception point (TRP), a home evolved NodeB (HNB), a wireless fidelity (Wi-Fi) access point (AP), a mobile switching center, a next-generation NodeB (gNB) in a 5G mobile communication system, a next-generation NodeB in a 6G mobile communication system, or a base station in a future mobile communication system. A RAN node can also be a device that performs base station functions in device-to-device (D2D) communication systems, vehicle-to-everything (V2X) communication systems, machine-to-machine (M2M) communication systems, and Internet of Things (IoT) communication systems. A RAN node can also be a RAN node in a non-terrestrial network (NTN), meaning that the RAN node can be deployed on a high-altitude platform or satellite. A RAN node can be a macro base station, a micro base station, an indoor base station, a relay node, a donor node, or a radio controller in a cloud radio access network (CRAN) scenario, or a node in an open radio access network (O-RAN or ORAN) scenario. Alternatively, a RAN node can be a server, a wearable device, a vehicle, or an onboard device. For example, a RAN node in V2X technology can be a roadside unit (RSU). Of course, a RAN node can also be a node in the core network.

[0105] In another possible scenario, multiple RAN nodes collaborate to assist the terminal in achieving wireless access, and different RAN nodes respectively implement part of the functions of the base station. For example, the RAN node can be a centralized unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU). The CU and DU can be set separately, or they can be included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or radio frequency unit, such as a remote radio unit (RRU), an active antenna unit (AAU), or a remote radio head (RRH).

[0106] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meanings. For example, in the ORAN system, CU may be referred to as Open CU (O-CU), DU may be referred to as Open DU (O-DU), CU-CP may be referred to as Open CU-CP (O-CU-CP), CU-UP may be referred to as Open CU-UP (O-CU-UP), and RU may be referred to as Open RU (O-RU).

[0107] Among them, any unit among CU (or CU-CP, CU-UP), DU and RU can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module. That is, the wireless access network device in this application can be a virtualized device, for example, implemented by general hardware and instantiated virtualization functions, or by dedicated hardware and instantiated virtualization functions. Among them, the general hardware can be a server, such as a cloud server.

[0108] The terminal in this application may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal equipment, wireless communication equipment, user agent or user device.

[0109] A terminal can be a device that provides voice / data connectivity to a user, such as a handheld device or vehicle-mounted device with wireless connection function. At present, some examples of terminal devices can be: mobile phones, tablet computers, computers with wireless transceiver functions (such as laptops, PDAs, etc.), mobile internet devices (MIDs), virtual reality (VR) devices, augmented reality (AR) devices, smart point of sale (POS) machines, customer-premises equipment (CPE), wireless terminals in industrial control, wireless terminals in self-driving cars, drones, terminal devices in IoT systems, wireless terminals in remote medical care, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, cellular phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), etc. assistant, PDA), handheld devices with wireless communication capabilities, computing devices or other processing devices connected to a wireless modem, vehicle-mounted devices, wearable devices, terminal devices in 5G networks or terminal devices in future evolved public land mobile communication networks (PLMN), etc.

[0110] Wearable devices, also known as wearable smart devices, are a general term for wearable devices that are designed and developed using wearable technology to intelligently design everyday wearables, such as glasses, gloves, watches, rings, clothing, and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not just hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. In a broad sense, wearable smart devices include those that are fully functional, large in size, and can achieve full or partial functions without relying on smartphones, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0111] In addition, terminal devices can also include sensors such as smart printers, train detectors, and gas stations. Their main functions include collecting data (part of the terminal devices), receiving control information and downlink data from network devices, and sending electromagnetic waves to transmit uplink data to network devices.

[0112] The terminal in this application may be a virtualized device, for example, implemented by general-purpose hardware and instantiated virtualization functions, or by dedicated hardware and instantiated virtualization functions. The general-purpose hardware may be a server, for example, a cloud server.

[0113] It should be understood that the present application does not limit the specific forms of the wireless access network device and the terminal device.

[0114] Figure 1 is a schematic diagram of the architecture of a communication system 100 applicable to the method provided in an embodiment of the present application. As shown in Figure 1 , the communication system 100 includes a radio access network 10 and a core network 20. Optionally, the communication system 100 may also include the Internet 30. The radio access network 10 may include at least one radio access network device (such as 110a and 110b in Figure 1 ) and at least one terminal (such as 120a-120j in Figure 1 ).

[0115] Terminals can connect to radio access network equipment wirelessly, and radio access network equipment can connect to the core network wirelessly or via wired connections. Core network equipment and radio access network equipment can be independent, distinct physical devices, or they can integrate the core network equipment's functions and the radio access network equipment's logical functions into the same physical device. Alternatively, a single physical device can integrate some core network equipment functions and some radio access network equipment functions. Terminals and radio access network equipment can connect to each other via wired or wireless connections.

[0116] Wireless access network devices and terminals, wireless access network devices, and terminals can communicate through authorized spectrum, unauthorized spectrum, or both. They can communicate through spectrum below 6 gigahertz (GHz), spectrum above 6 GHz, or both. The embodiments of this application do not limit the spectrum resources used for wireless communications.

[0117] The wireless access network device may be a base station deployed in the air, such as a satellite base station 110a; or a base station deployed indoors, such as a micro base station or an indoor station 110b.

[0118] The terminal can be a terminal deployed in the air, such as the helicopter or drone 120i in Figure 1; it can also be a terminal deployed on the ground, such as the mobile phones 120a, 120e, 120f and 120j, vehicle 120b, computer 120g, printer 120h, etc. in Figure 1.

[0119] Wireless access network equipment and terminals can be fixed or mobile. For example, they can be deployed on land, indoors or outdoors, handheld or vehicle-mounted; on water; or in the air on aircraft, balloons, and satellites.

[0120] The roles of radio access network devices and terminals can be relative. For example, the helicopter or drone 120i in Figure 1 can be configured as a mobile base station. For devices 120j accessing the radio access network 10 via 120i, 120i is a base station; however, for 110a, 120i is a terminal. That is, communication between 110a and 120i occurs via a wireless air interface protocol. Of course, communication between 110a and 120i can also occur via an interface protocol between radio access network devices. In this case, 120i is also a base station relative to 110a. Therefore, radio access network devices and terminals can be collectively referred to as communication devices. 110a, 110b, and 120a-120j in Figure 1 can be referred to as communication devices having their respective functions, such as base station functions or terminal functions.

[0121] It should be understood that FIG1 is only a schematic diagram, and the communication system may further include other devices, such as wireless relay devices and wireless backhaul devices, which are not shown in FIG1 .

[0122] In wireless communication systems, signals transmitted through wireless channels are subject to noise and interference. Consequently, the received signal may contain errors compared to the transmitted signal. To mitigate the effects of noise and interference, it is necessary to characterize the wireless channel. This process is called channel estimation.

[0123] Channel estimation involves several steps: first, mathematical modeling of the channel is performed; second, the transmitter transmits a known signal (e.g., a reference signal or pilot signal) to the receiver; and finally, the receiver compares the received signal, which contains noise and interference, with the known signal to determine the characteristics of the wireless channel.

[0124] Exemplarily, some possible channel estimation algorithms include the following: classical algorithms based on mathematical closed-form solutions, including least squares method and minimum mean square error method; iterative channel estimation algorithms; and neural network-based channel estimation algorithms.

[0125] In the following, let the transmitted signal be X, the received signal be Y, and the channel be H. The channel estimation process performed by the communication device based on the above three channel estimation algorithms will be described respectively.

[0126] 1. Channel estimation algorithm based on least squares method, the channel estimation value obtained satisfy:

[0127] in, represents the conjugate transpose of X.

[0128] 2. Channel estimation algorithm based on minimum mean square error method, the channel estimation value obtained satisfy:

[0129] in, The matrix H and the matrix The cross-correlation matrix between is a matrix The autocorrelation matrix of .

[0130] The aforementioned least-squares-based channel estimation algorithm is simple but sensitive to noise. Its performance deteriorates significantly as the signal-to-noise ratio decreases. Compared to least-squares-based channel estimation algorithms, minimum mean square error-based channel estimation algorithms are more effective against noise. However, because they require matrix inversion, they are more complex to implement and are difficult to implement in hardware.

[0131] The aforementioned least squares and minimum mean square error (MMSE) channel estimation algorithms rely on closed-form mathematical solutions. While these methods offer relatively low computational complexity, they can produce inaccurate channel estimates. Therefore, to overcome the limitations of closed-form solutions, an iterative channel estimation algorithm has been proposed. This algorithm iteratively converges the channel estimate to a specific value, which is then used as the final channel estimation result.

[0132] Based on the principle of iteration, iterative algorithms can be divided into the following two types: 1. Adding a regularization term after the least squares expression and combining it with optimization methods such as proximal gradient descent or the alternating direction multiplier method to design an iterative algorithm. 2. Iterative algorithms that utilize probabilistic models and incorporate prior information about the channel to minimize the mean-squared error (MSE). Examples include the approximate message passing (AMP) algorithm, the orthogonal approximate message passing (OAMP) algorithm, and their extensions. In iterative channel estimation algorithms, the terminal device may need to perform multiple iterations, and the architecture used in each iteration can be summarized as a series connection of a linear estimator (LE) and a nonlinear estimator (NLE). The LE is relatively easy to design and can be based on a clear linear mathematical expression. However, the NLE is more challenging to design and requires sufficient and accurate prior information about the channel, which is difficult to obtain.

[0133] Therefore, in order to overcome the design difficulties of NLE in iterative algorithms, it is proposed to simplify the design complexity of NLE through neural networks. A channel estimation algorithm based on deep unfolding network (DUN) is shown below. The channel estimation algorithm based on DUN regards each iteration in the iterative algorithm as a layer of the neural network. In other words, the channel estimation algorithm based on DUN can be understood as truncating the traditional iterative algorithm into a neural network with a fixed number of layers. Among them, the NLE in the tth iteration of the traditional iterative algorithm can be calculated with parameter Θ t neural network instead.

[0134] For example, the channel estimation method based on DUN can obtain the channel estimation value satisfy:

[0135] P1:

[0136] Among them, h is the true value of the channel, R Θ represents a neural network with parameters Θ, Indicates that the parameter is Θ t The t-th layer of the neural network, represents a composite operation, t = 1, 2, ..., P, where P is an integer greater than 1, and P represents the number of layers of the neural network in DUN, which can also be understood as the number of iterations of channel estimation. The physical meaning of P1 is: the receiver (for example, the terminal device) inputs the received signal y and the initial value h0 of the channel into the neural network with parameter Θ, and the channel estimation value obtained is The average error between the channel true value h is the smallest.

[0137] Although the above-mentioned neural network-based channel estimation algorithm can solve the design complexity of NLE, the algorithm is a design that truncates the traditional iteration into P-layer iterations, which to a certain extent destroys the convergence of the traditional iteration, making it difficult to ensure the accuracy of the channel estimation results.

[0138] In view of this, an embodiment of the present application proposes a channel estimation method and related devices. In this method, the characteristics of the fixed point theory are used as constraints for designing a channel estimator, so that the designed channel estimator can theoretically ensure that the optimal solution converges to the fixed point, thereby improving the accuracy of channel estimation.

[0139] The channel estimation method provided in the embodiment of the present application is described in detail below in conjunction with Figure 2. The method provided in the embodiment of the present application can be applied to the network architecture shown in Figure 1, but the embodiment of the present application is not limited thereto. The channel estimation method can be applied to a first communication device and a second communication device.

[0140] Figure 2 is a schematic flow chart of a channel estimation method 200 provided in an embodiment of the present application. In the flow chart shown in Figure 2, the method is illustrated from the perspective of the interaction between the first communication device as a terminal device and the second communication device as a network device, but the present application does not limit the execution subject of the method. For example, the terminal device in Figure 2 can be replaced by a chip, a chip system, or a processor that supports the terminal device to implement the method, or a logic module or software that can implement all or part of the terminal device functions. The network device in Figure 2 can be replaced by a chip, a chip system, or a processor that supports the network device to implement the method, or a logic module or software that can implement all or part of the network device functions.

[0141] As shown in Figure 2, the method 200 may include steps S201 to S203. The following describes each step in the method 200 in detail.

[0142] S201: The network device sends a second signal, and the terminal device receives a first signal from the network device.

[0143] The first signal is a signal received by the terminal device after the second signal sent by the network device passes through the wireless channel. It can be understood that the first signal can be used to obtain channel information of the wireless channel passed by the second signal.

[0144] The first signal or the second signal in this application can be a reference signal, a signal for transmitting data, or other types of signals. This application does not limit this. However, it should be understood that the first signal and the second signal are of the same type.

[0145] S202: The network device determines first information based on a precoding matrix, where the first information is used to indicate a first channel estimation parameter.

[0146] The first channel estimation parameter includes at least one of the following: a matrix norm of the first expression, or an iteration step size.

[0147] The precoding matrix may be a downlink precoding matrix determined by the network device according to the PMI and RI from the terminal device. That is, the method may further include: the terminal device determining the PMI and RI, and sending the PMI and RI to the network device.

[0148] Regarding the process of the terminal device determining PMI and RI, please refer to the relevant description in 3GPP standard technical specification (TS) 36.213. Regarding the method of the network device determining the downlink precoding matrix based on PMI and RI, please refer to the relevant description in 3GPP standard TS 38.214. No further details will be given here.

[0149] Since the first information is determined based on the precoding matrix, and the first information is used to indicate the first channel estimation parameter, it can be replaced by: the channel estimation parameter is determined by the precoding matrix, or replaced by: at least one of the matrix norm or iteration step size of the first expression is determined based on the precoding matrix.

[0150] The first expression described above can be determined by the precoding matrix, the first signal, and the kth channel estimation value, where k is a positive integer less than or equal to T, T is a positive integer, and T is the number of channel estimation iterations. It will be understood that the value of k can also be replaced by: k is an integer greater than or equal to 0 and less than T; or replaced by the value of k being all integers from 1 to T (or 0 to T-1).

[0151] One possible example of this first expression is:

[0152] Wherein, y represents the first signal, A represents the precoding matrix, represents the k-th channel estimation value.

[0153] For example, in the first expression In the case of , the matrix norm of the first expression can be expressed as:

[0154] Among them, || || M represents the M-norm, and M represents the matrix norm.

[0155] S203: The network device sends the first information. Correspondingly, the terminal device receives the first information from the network device.

[0156] S204: The terminal device performs T channel estimations based on the first signal and the first channel estimation parameter to obtain a channel estimation result.

[0157] Among them, performing T channel estimations can be understood as performing T iterations. The initial input of the T iterations (i.e., the input of the first iteration) is the first signal received by the terminal device and the initial value of the channel The input of the kth iteration in the T iterations is the output of the k-1th iteration. In this way, the channel estimation result obtained by the terminal device can be the output of the Tth iteration after the terminal device performs T iterations.

[0158] The first channel estimation parameter is used to ensure the convergence of the channel estimation algorithm.

[0159] For example, the initial value of the channel It can be a channel estimation value obtained by the terminal device based on the LS channel estimation algorithm or the MMSE channel estimation algorithm described above; or it can be a channel value randomly obtained from the prior information of the channel. This application does not limit the method of obtaining the initial value of the channel.

[0160] In an embodiment of the present application, the terminal device performs channel estimation based on the matrix norm and iteration step of the obtained first expression and the received signal. The matrix norm and iteration step are two parameters obtained based on the fixed point theory. Therefore, it can be said that the terminal device adopts the fixed point theory when performing channel estimation, and the application of the fixed point theory can theoretically ensure that the optimal solution converges to the fixed point, that is, the T channel estimations performed in the present application can ensure that the channel estimation value converges to a fixed value, and the terminal device can determine the fixed value as the channel estimation result. Therefore, the channel estimation method provided in the present application can improve the accuracy of channel estimation.

[0161] Exemplarily, the network device may directly or indirectly indicate the first channel estimation parameter to the terminal device through the first information.

[0162] In a first possible implementation manner (direct indication), the first information includes a first channel estimation parameter.

[0163] It can be understood that in the first possible implementation manner, the terminal device can directly obtain the matrix norm and / or iteration step size based on the first channel estimation parameter included in the first information.

[0164] In this direct indication manner, the network device may carry specific values ​​of the matrix norm and / or iteration step in the first information, or carry a calculation formula for determining the matrix norm and / or iteration step.

[0165] It can be understood that when the first information carries a calculation formula for determining the matrix norm and / or the iteration step size, the first information also indicates the values ​​of each parameter in the calculation formula.

[0166] For example, the first information includes M=1 and σ=0.5. For another example, the first information includes M=(AA T ) -1 and At this time, the first information may also include matrix A.

[0167] Among them, A is the precoding matrix, A T represents the transpose of A, Representation matrix traces, represents the conjugate transpose of matrix A, and n represents the number of columns in matrix A.

[0168] In a second possible implementation manner (indirect indication), the first information includes a first index, and the first index corresponds to the first channel estimation parameter.

[0169] It can be understood that in the second possible implementation manner, the terminal device determines the first channel estimation parameter based on the first index indicated by the first information and the corresponding relationship, and then obtains the matrix norm and / or iteration step.

[0170] Optionally, the above correspondence relationship includes a correspondence relationship between each parameter group and an index in at least one channel estimation parameter, and the at least one channel estimation parameter includes a first channel estimation parameter.

[0171] It can be understood that the at least one index includes the above-mentioned first index.

[0172] Similar to the first possible implementation, the channel estimation parameters included in the corresponding relationship may be specific values ​​of the matrix norm and / or the iteration step, such as M=1 and σ=0.5; or, may be a calculation formula for determining the matrix norm and / or the iteration step, such as M=(AA T ) -1 and

[0173] It can be understood that when the channel estimation parameters included in the corresponding relationship are calculation formulas for determining matrix norms and / or iteration steps, the network device also indicates the values ​​of the parameters in the calculation formula, such as the value of the precoding matrix A.

[0174] Optionally, the above correspondence relationship may be predefined or indicated by the network side.

[0175] In the case where the corresponding relationship is indicated by the network side, the method 200 may further include: the network device sends third information to the terminal device, where the third information is used to configure the corresponding relationship.

[0176] Illustratively, the network device may configure at least one parameter group for the terminal device via Layer 3 (L3) signaling. For example, the network device may configure the correspondence between parameter groups and indexes shown in Table 1 for the terminal device via L3 signaling. In this way, when the network device sends the first information, it may dynamically indicate the first channel estimation parameter to the terminal device via Layer 2 (L2) or Layer 1 (L1) signaling.

[0177] It can be understood that if the network device indicates the first channel estimation parameter to the terminal device through L1 (physical layer) signaling, the above-mentioned first information can be carried in the DCI; if the network device indicates the first channel estimation parameter to the terminal device through L2 (radio network layer) signaling, the above-mentioned first information can be carried in the MAC CE.

[0178] Since L3 includes the NAS layer and the RRC layer, the first information can be carried in NAS signaling or RRC signaling.

[0179] Table 1 shows a corresponding relationship.

[0180] Table 1

[0181] As shown in Table 1, each parameter group includes the matrix norm M and the iteration step size σ For the description of other parameters in Table 1, please refer to the previous description, which will not be repeated here.

[0182] It is understandable that each parameter group shown in Table 1 may also include only the matrix norm M, or only the iteration step size σ , this application does not limit this.

[0183] Optionally, the above channel estimation parameters may be understood as parameters used for channel estimation. In the above T channel estimations, the kth channel estimation satisfies: Should It is a fixed point operator designed based on fixed point theory.

[0184] The fixed point theorem states that under certain circumstances, a function f() has at least one fixed point, that is, at least one point x such that function f(x) = x. is a fixed point operator designed based on fixed point theory, so the function At least one point Make the function

[0185] in, represents the output of the k-th channel estimation, represents the output of the k-1th channel estimation, Indicates that the first signal y and the As input, the output is obtained by performing the k-th channel estimation.

[0186] Understandably, the It means the output of the k-1th channel estimation and the input of the kth channel estimation. It should be noted that when k=1, It actually represents the input of the first channel estimation, that is, the initial input.

[0187] As mentioned earlier, the channel estimation result obtained by the terminal device is the output value of the Tth iteration after the terminal device performs T iterations. During T channel estimations, if the input of the kth channel estimation is the same as the output of the kth channel estimation (or, the output of the k-1th channel estimation is the same as the output of the kth channel estimation), then the outputs of the k+1th to Tth channel estimations are the same as the output of the kth channel estimation.

[0188] In one possible implementation, when the output of the k+1th to Tth channel estimation is the same as the output of the kth channel estimation, the terminal device may also determine the output of any channel estimation from the outputs of the k-1th to Tth channel estimation as the channel estimation result.

[0189] In one possible implementation, if the input of the kth channel estimation is the same as the output of the kth channel estimation, the terminal device can determine the output of the kth channel estimation as the signal estimation result, and can no longer continue to perform the k+1th to Tth channel estimation. This implementation can avoid resource waste. It should be noted that the above-mentioned input of the kth channel estimation and the output of the kth channel estimation are the same may only exist in theory. In actual channel estimation, there may be some errors between the input of the kth channel estimation and the output of the kth channel estimation. This error is the error that the network can allow in the channel estimation, or the channel estimation value obtained within this error range can be considered to be the true value of the channel. For example, when the difference between the input of the kth channel estimation and the output of the kth channel estimation is less than the first preset value, it can be considered that the input of the kth channel estimation is the same as the output of the kth channel estimation.

[0190] One possible implementation of the above Including nonlinear estimator. In the kth channel estimation: the input of the nonlinear estimator is The output of the nonlinear estimator is

[0191] Exemplarily, the nonlinear estimator can be implemented by a neural network model.

[0192] Optionally, in the case where the nonlinear estimator is implemented by a neural network model, the method 200 further includes: the terminal device obtains parameters of the neural network model, and the neural network model is used for channel estimation.

[0193] Exemplarily, the terminal device may obtain the parameters of the neural network model by training the neural network model; or, the terminal device may obtain the parameters of the neural network model through second information indicated by the network side; or, the terminal device may also determine the parameters of the neural network model through the obtained channel estimation parameters.

[0194] Optionally, when the terminal device obtains the parameters of the neural network model through the second information indicated by the network side, the method 200 also includes: the network device trains the neural network model to obtain the parameters of the neural network model; and sends the second information to the terminal device.

[0195] It is understood that the training of the neural network model is completed before performing T channel estimations (eg, initial access). The training process may be completed in an offline state.

[0196] Exemplarily, the network device may train the neural network model when the parameter value of the channel estimation determined according to the precoding matrix changes, so as to determine the parameters of the neural network model corresponding to different parameter values ​​of the channel estimation.

[0197] Alternatively, the terminal device may train the neural network model based on the channel estimation parameters included in one or more parameter groups included in the pre-acquired correspondence to obtain the parameters of the neural network model corresponding to each parameter group. In this way, when performing channel estimation, the terminal device may determine the parameters of the neural network model corresponding to the channel estimation parameters after obtaining the channel estimation parameters.

[0198] The process of training the neural network model in this application is a process of continuously adjusting and optimizing the parameters of the neural network model. The process is as follows: in the process of training the neural network model, the initial parameters are first given, and the initial parameter values ​​are continuously adjusted so that the error between the channel value obtained by the adjusted parameter value and the true channel value is minimized, and the parameter value corresponding to the minimum error is determined as the parameter of the neural network model to complete the training of the neural network model. That is, the error between the channel estimation value obtained using the trained neural network model and the true channel value should be minimized. In the training of the neural network model, the input of the model is the received signal and the true channel value of the wireless channel experienced by the signal, and the output is the channel estimation value.

[0199] For example, a network device or terminal device can optimize the parameters of a neural network model by designing a loss function, thereby enabling the terminal device to obtain a relatively accurate channel estimation value when performing channel estimation using the optimized parameters. The loss function can be a function for calculating the difference between the channel estimation value obtained by the neural network model and the true channel value, or a mean-square error (MSE).

[0200] When designing the loss function by calculating the mean square error, the parameter θ of the neural network model satisfies:

[0201] in, Represents the loss function The value of the variable when it reaches the minimum value, function To obtain the channel estimate and the true channel value h, where m is the number of training samples. The training samples are the true values ​​of the received signal and the channel. The training samples can be pre-acquired channel prior information, historical data, or data generated by a channel generator that complies with the 3GPP standard (for example, a quasi deterministic radio channel generator (QuaDRiGa)).

[0202] Figure 3 shows the architecture of a neural network model. As shown in Figure 3, the neural network model can include two convolution (conv) layers, two residual network blocks (RBs), and an activation function. The input of convolution layer 1 is the input of the channel estimator, and the output of the activation function is the output of the channel estimator. If the output of convolution layer 1 is defined as output 1, then output 1 is the input of RBs1, the output of RBs1 is the input of RBs2, the output of RBs2 is the input of convolution layer 2, and the output of convolution layer 2 is the input of the activation function.

[0203] Another possible implementation of the above Includes linear estimators and nonlinear estimators.

[0204] Optionally, in the kth channel estimation: the input of the linear estimator is The output of the linear estimator is the input of the nonlinear estimator, and the output of the nonlinear estimator is That is to say, in In the case of a linear estimator and a nonlinear estimator, the output of the linear estimator is the input of the nonlinear estimator.

[0205] Optionally, in the kth channel estimation: the input of the nonlinear estimator is The output of the nonlinear estimator is the input of the linear estimator, and the output of the linear estimator is That is to say, in In the case of a linear estimator and a nonlinear estimator, the output of the nonlinear estimator is the input of the linear estimator.

[0206] Similarly, the nonlinear estimator can be implemented by a neural network model. The description of the neural network model and the description of the terminal device obtaining the parameters of the neural network model can be referred to the relevant description above and will not be repeated here.

[0207] The neural network model used in the above-mentioned DUN-based channel estimation method includes P layers, each of which needs to be trained independently as a separate sub-network, resulting in a complex network structure and high training difficulty. In contrast, the neural network model used to implement the nonlinear estimator in the embodiment of the present application includes a simple convolutional layer and a residual network block structure with a simple structure and low training complexity. The trained neural network can be repeatedly used in each iteration of channel estimation, with high generalization and good scalability.

[0208] Figure 4 shows a schematic diagram of the connection between a linear estimator and a nonlinear estimator. As shown in Figure 4, the channel estimator includes a linear estimator and a nonlinear estimator. The input of the linear estimator is the input of the channel estimator, the output of the linear estimator is the input of the nonlinear estimator, and the output of the nonlinear estimator is the output of the channel estimator. If T channel estimates are required, then in the kth channel estimate, the kth input of the linear estimator is the k-1th output of the nonlinear estimator.

[0209] It can be understood that the input of the nonlinear estimator in the channel estimator shown in FIG4 can also be the input of the channel estimator, the output of the nonlinear estimator can be the input of the linear estimator, and the output of the linear estimator can be the output of the channel estimator.

[0210] Optionally, the above It can be derived through convex optimization and monotone operator theory.

[0211] The following shows the One possible expression is satisfy:

[0212] Where I is the identity matrix, g is the regularization term, Indicates the sub-gradient of g, A is the precoding matrix, M is the matrix norm, σ is the iteration step length.

[0213] For example, in And when A is the unit matrix, the Including nonlinear estimators, excluding linear estimators, and the mathematical model of the nonlinear estimator can be

[0214] For example, in And when A is not a unit matrix, the It includes nonlinear estimators and linear estimators, and the mathematical model of the linear estimator can be: The mathematical model of the nonlinear estimator can be

[0215] in, The neural network model R θ accomplish.

[0216] Optionally, the value of the channel estimation times T may be predefined or indicated by the network side.

[0217] Optionally, when the value of T is indicated by the network side, the method 200 further includes: the network device sending fourth information to the terminal device, the fourth information indicating the value of T. Correspondingly, the terminal device receives the fourth information from the network device and determines the value of T based on the fourth information.

[0218] Alternatively, the value of T may be determined by the terminal design based on the channel estimation result. For example, if the terminal device performs 15 channel estimations and the channel estimation value obtained converges to a certain value, then T can be set to 15.

[0219] Optionally, the T-time channel estimation may be implemented by a channel estimator. The channel estimator may include a nonlinear estimator; or the channel estimator may include a linear estimator and a nonlinear estimator.

[0220] Exemplarily, the mathematical model of the channel estimator can be expressed as:

[0221] P2:

[0222] Constraints:

[0223] in, is the fixed point equation, f is the fixed point operator, is the solution of the fixed point equation, representing the channel estimation value; y is the downlink reference signal, h is the true channel value, Express Find the mean; the physical meaning of P2 is: to obtain the function that minimizes the channel estimation error The channel estimation error refers to the error between the true channel value and the channel estimation value.

[0224] This application defines the normalized mean square error (NMSE) between the channel estimation value obtained by the channel estimation algorithm shown in method 200 and the channel estimation value obtained by the existing channel estimation algorithm and the true channel value. The NMSE satisfies:

[0225] Wherein, h is the channel simulation result generated by a channel generator (e.g., QuaDRiGa) that complies with the 3GPP standard (which can be understood as the true value of the channel), is the channel estimation value.

[0226] The above-mentioned existing channel estimation algorithms include: a channel estimation algorithm based on the least squares method, a channel estimation algorithm based on the mean square error method, an iterative OAMP algorithm, a fast iterative shrinkage-thresholding algorithm (FISTA), an ISTA-Net+ based on a neural network, and a fixed point network (FPN)-OAMP algorithm based on a neural network.

[0227] Figure 5 shows a comparison of the normalized mean square error (NMSE) for various algorithms. As shown in Figure 5, the horizontal axis represents the signal-to-noise ratio (SNR) in decibels (dB), and the vertical axis represents the NMSE in dB. The NMSE for channel estimates obtained by various channel estimation algorithms decreases as the SNR increases, and the rate of NMSE decreases as the SNR increases.

[0228] As can be seen from Figure 5, the channel estimation algorithms corresponding to NMSE from large to small are: a channel estimation algorithm based on least squares, a channel estimation algorithm based on FISTA, an iterative OAMP channel estimation algorithm, a channel estimation algorithm based on MMSE, an ISTA-Net+ algorithm based on a neural network, an FPN-OAMP algorithm based on a neural network, and a channel estimation algorithm provided in accordance with an embodiment of the present application (the channel estimation algorithm provided in accordance with an embodiment of the present application is represented by a fixed point deep balanced network (FPDE-net) in Figure 5). For NMSE, the smaller the NMSE, the smaller the channel estimation error and the more accurate the channel estimation result. Therefore, as shown in Figure 5, the channel estimation result obtained based on the least squares method has the lowest accuracy; the channel estimation result obtained by the channel estimation algorithm in accordance with an embodiment of the present application has the highest accuracy.

[0229] The channel estimation algorithm provided in this embodiment is as follows:

[0230] input: M, σ;

[0231] for k=0,1,2,…,T-1 do

[0232] end for

[0233] output:

[0234] The channel estimation algorithm provided in the embodiment of the present application can be described as follows: M, and σ ; Execute the linear estimator starting from k=0 Get the output of the linear estimator and will As a neural network R θ Input to the implemented nonlinear estimator to get the output of the first iteration The output of the first iteration is used as the input of the next iteration, and so on, until k = T-1, the loop ends and outputs

[0235] The method provided by the embodiment of the present application is described in detail above in conjunction with Figures 1 to 5 , and the device provided by the embodiment of the present application is described in detail below in conjunction with Figures 6 and 7 .

[0236] To implement the various functions of the methods provided in the embodiments of the present application, the first communication device and the second communication device may each include hardware structures and / or software modules, and implement the aforementioned functions in the form of hardware structures, software modules, or a combination of hardware structures and software modules. Whether a particular one of the aforementioned functions is implemented in the form of hardware structures, software modules, or a combination of hardware structures and software modules depends on the specific application and design constraints of the technical solution.

[0237] Figures 6 and 7 are schematic diagrams of possible devices provided by embodiments of the present application. These devices can be used to implement the functions of the terminal device or network device in the above method embodiments, and thus can also achieve the beneficial effects of the above method embodiments.

[0238] Figure 6 is a schematic block diagram of an apparatus 600 provided in an embodiment of the present application. The apparatus 600 may be a terminal or a network device, or a device in a terminal device or a network device, or a device that can be used in conjunction with a terminal device or a network device. In one possible implementation, the apparatus 600 may include a module or unit that corresponds one-to-one to the method / operation / step / action performed by the first communication device or the second communication device in the above-mentioned method embodiment. The unit may be a hardware circuit, or software, or a combination of a hardware circuit and software. For example, as shown in Figure 6, the apparatus 600 may include a transceiver module 610 and a processing module 620.

[0239] One possible design is that the apparatus 600 is used to implement the functions of the terminal device in the method embodiment shown in FIG. 2 .

[0240] Exemplarily, the transceiver module 610 is used to: receive a first signal; and receive first information, the first information being used to indicate a first channel estimation parameter, the first channel estimation parameter including at least one of the following: a matrix norm of a first expression, or an iteration step, the first expression being determined by a precoding matrix, the first signal, and the kth channel estimation value, the matrix norm and the iteration step being determined based on the precoding matrix, k being a positive integer less than or equal to T, and T being a positive integer; the processing module 620 is used to: perform T channel estimations based on the first signal and the channel estimation parameter to obtain a channel estimation result.

[0241] Optionally, the processing module 620 is further used to: obtain parameters of the neural network model, where the neural network model is used for channel estimation.

[0242] Optionally, the transceiver module 610 is further used to: receive second information, where the second information indicates parameters of the neural network model.

[0243] Optionally, the processing module 620 is further used to: train the neural network model to obtain parameters of the neural network model.

[0244] A more detailed description of the transceiver module 610 and the processing module 620 can be directly obtained by referring to the relevant description in the embodiment shown in FIG2 , and will not be repeated here.

[0245] Another possible design is that the apparatus 600 is used to implement the functions of the network device in the method embodiment shown in FIG. 2 .

[0246] Exemplarily, the transceiver module 610 is used to: send a first signal; the processing module 620 is used to: determine first information based on a precoding matrix, where the first information is used to indicate a first channel estimation parameter, where the first channel estimation parameter includes at least one of the following: a matrix norm of a first expression, or an iteration step size, where the first expression is determined by the precoding matrix, the first signal, and the kth channel estimation value, where k is a positive integer less than or equal to T, and T is a positive integer; the transceiver module 610 is also used to: send the first information.

[0247] Optionally, the transceiver module 610 is further used to: send third information, where the third information is used to configure the corresponding relationship, the corresponding relationship includes at least one channel estimation parameter, and the at least one channel estimation parameter includes the first channel estimation parameter.

[0248] Optionally, the processing module 620 is also used to: train the neural network model to obtain parameters of the neural network model, and the neural network model is used for channel estimation; the transceiver module 610 is also used to: send second information, and the second information indicates the parameters of the neural network model.

[0249] Optionally, the transceiver module 610 is further used to: send fourth information, where the fourth information indicates the value of T.

[0250] A more detailed description of the transceiver module 610 and the processing module 620 can be directly obtained by referring to the relevant description in the embodiment shown in FIG2 , and will not be repeated here.

[0251] It should be noted that device 600 may include a sending module but not a receiving module. Alternatively, device 600 may include a receiving module but not a sending module. This may depend on whether the above-mentioned solution executed by device 600 includes both sending and receiving actions. It is understood that because device 600 has communication functionality, it can also be referred to as a communication device.

[0252] FIG7 is another schematic block diagram of an apparatus provided in an embodiment of the present application. As shown in FIG7 , apparatus 700 includes one or more processors 710. Processor 710 may be a general-purpose processor or a dedicated processor. For example, it may be a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, and the central processing unit may be used to control an apparatus (e.g., a terminal device, a network device, or a chip), execute software programs, and process data from the software programs.

[0253] Optionally, in one design, the processor 710 may include a program (also referred to as code or instructions), which may be executed on the processor 710 to cause the apparatus 700 to perform the method performed by the terminal device or network device in the above method embodiment. In another possible design, the apparatus 700 includes a circuit (not shown in FIG. 7 ) configured to implement the functions of the terminal device or network device in the above method embodiment.

[0254] Exemplarily, the processor 710 may be configured to execute computer programs or instructions in the memory to implement the steps performed by the terminal device or network device in the method embodiment shown in any one of the embodiments shown in FIG. 2 .

[0255] Optionally, the device 700 may include one or more memories 720 on which programs (sometimes also referred to as codes or instructions) are stored. The programs can be run on the processor 710 so that the device 700 executes the method executed by the terminal device or network device in the above embodiment.

[0256] Optionally, the processor 710 and / or the memory 720 may include an artificial intelligence (AI) module, which is used to implement AI-related functions. The AI ​​module may be implemented through software, hardware, or a combination of software and hardware. For example, the AI ​​module may include a wireless intelligent controller (RIC) module. For example, the AI ​​module may be a near real-time RIC or a non-real-time RIC.

[0257] Optionally, data may be stored in the processor 710 and / or the memory 720. The processor and memory may be provided separately or integrated together.

[0258] Optionally, the apparatus 700 may further include a communication interface 730. The processor 710 may also be referred to as a processing unit, which controls the apparatus (e.g., a terminal device or a network device). The communication interface 730 may also be referred to as a transceiver unit, a transceiver, a transceiver circuit, or a transceiver, etc., which implements the transceiver function of the apparatus.

[0259] Optionally, the apparatus 700 further includes a communication interface 730. The processor 710 and the communication interface 730 are coupled to each other. It is understood that the communication interface 730 may be a transceiver or an input / output interface.

[0260] It is understandable that, since the device 700 has a communication function, it can also be called a communication device.

[0261] When apparatus 700 is used to implement the method of FIG2 , processor 710 is used to perform the functions of the processing unit described above, and communication interface 730 is used to perform the functions of the transceiver module described above. Whether communication interface 730 is used for sending or receiving can be determined by whether it is used to perform a sending action or a receiving action in the solution implemented by apparatus 700.

[0262] When the apparatus 700 is a chip implemented in a terminal device, the chip implements the functions of the terminal device in the method embodiment described above. The chip of the terminal device receives signals from other modules in the terminal device (such as a radio frequency module or antenna), which may be signals sent by a network device to the terminal device; or the chip of the terminal device sends signals to other modules in the terminal device (such as a radio frequency module or antenna), which may be signals sent by the terminal device to a network device.

[0263] When the apparatus 700 is a chip used in a network device, the chip implements the functions of the network device in the above method embodiment. The chip of the network device receives a signal from another module in the network device (such as a radio frequency module or antenna), and the signal may be sent by the terminal device to the network device; or the chip of the network device sends a signal to another module in the network device (such as a radio frequency module or antenna), and the signal may be sent by the network device to the terminal device.

[0264] It is understood that when the apparatus 700 is a terminal device or a network device, the communication interface 730 may be a transceiver, specifically including a transmitter and a receiver, where the transmitter is used to transmit signals and the receiver is used to receive signals. When the apparatus 700 is a chip used in a terminal device or a network device, the communication interface 730 may be an input / output circuit, where the input circuit can be used for receiving and the output interface can be used for transmitting.

[0265] It should be noted that the above method embodiments can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiments can be completed by hardware integrated logic circuits in the processor or by software instructions.

[0266] The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0267] The steps of the method disclosed in the embodiments of this application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0268] The memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0269] The methods provided in the above embodiments can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product may include one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic disk), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state disk (SSD)).

[0270] The embodiment of the present application further provides a computer-readable medium on which a computer program is stored. When the computer program is executed by a computer, the functions of the above-mentioned method embodiment are realized.

[0271] The embodiment of the present application also provides a computer program product containing instructions, which implements the functions of the above method embodiment when executed by a computer.

[0272] An embodiment of the present application also provides a communication system, which includes a terminal device and a network device.

[0273] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0274] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0275] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0276] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0277] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0278] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk, or an optical disk.

[0279] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A channel estimation method, characterized in that: include: receiving a first signal; receiving first information indicating a first channel estimation parameter, the first channel estimation parameter including at least one of the following: a matrix norm or an iteration step of a first expression, the first expression being determined by a precoding matrix, the first signal, and a k-th channel estimation value, at least one of the matrix norm or the iteration step being determined based on the precoding matrix, k being a positive integer less than or equal to T, T being a positive integer, and T being the number of iterations of the channel estimation; Perform T channel estimations based on the first signal and the first channel estimation parameter to obtain a channel estimation result.

2. The method according to claim 1, characterized in that The first information includes a first index, and the first index corresponds to the first channel estimation parameter; or the first information includes the first channel estimation parameter.

3. The method according to claim 2, characterized in that The corresponding relationship is predefined or indicated by the network side, and the corresponding relationship includes a corresponding relationship between each channel estimation parameter in at least one channel estimation parameter and an index, and the at least one channel estimation parameter includes the first channel estimation parameter.

4. The method according to any one of claims 1 to 3, characterized in that In the T channel estimations, the k-th channel estimation satisfies: described It is a fixed point operator designed based on fixed point theory; in, represents the output of the k-th channel estimation, represents the output of the k-1th channel estimation, Indicates that the first signal y and the is input, and the output is obtained by performing the k-th channel estimation.

5. The method according to claim 4, characterized in that described Including a nonlinear estimator, in the k-th channel estimation: the input of the nonlinear estimator is The output of the nonlinear estimator is 6. The method according to claim 4, characterized in that described It includes a linear estimator and a nonlinear estimator. In the k-th channel estimation, the input of the linear estimator is The output of the linear estimator is the input of the nonlinear estimator, and the output of the nonlinear estimator is 7. The method according to claim 5 or 6, characterized in that The nonlinear estimator is implemented through a neural network model.

8. The method according to claim 7, characterized in that The method further comprises: Parameters of the neural network model are obtained, where the neural network model is used for channel estimation.

9. The method according to claim 8, characterized in that The obtaining of the parameters of the neural network model includes: receiving second information, wherein the second information indicates parameters of the neural network model; or The neural network model is trained to obtain parameters of the neural network model.

10. The method according to any one of claims 6 to 9, characterized in that described satisfy: Where I is the identity matrix, g is the regularization term, represents the subgradient of g, A is the precoding matrix, M is the matrix norm, and σ is the iteration step size.

11. The method according to claim 10, characterized in that The mathematical model of the linear estimator is: The mathematical model of the nonlinear estimator is:

12. The method according to any one of claims 1 to 11, characterized in that The value of T is predefined or indicated by the network device.

13. The method according to any one of claims 1 to 12, characterized in that The T-time channel estimation is achieved by a channel estimator, and the mathematical model of the channel estimator is: Constraints: in, is the fixed point equation, is the solution of the fixed point equation, representing the channel estimation value; y is the first signal, h is the true value of the channel, Express Find the mean; the physical meaning of P is: to obtain the function that minimizes the channel estimation error 14. A channel estimation method, characterized in that: include: sending a second signal; Determining first information based on a precoding matrix, where the first information is used to indicate a first channel estimation parameter, where the first channel estimation parameter includes at least one of the following: a matrix norm of a first expression or an iteration step size, where the first expression is determined by the precoding matrix, the first signal, and a k-th channel estimation value, where k is a positive integer less than or equal to T, T is a positive integer, and T is the number of iterations of the channel estimation; sending the first information; The first signal is a signal obtained by passing the second signal through a wireless channel, and the first signal and the first channel estimation parameter are used to perform T-times channel estimation.

15. The method according to claim 14, characterized in that The first information includes a first index, and the first index corresponds to the first channel estimation parameter; or the first information includes the first channel estimation parameter.

16. The method according to claim 15, characterized in that The method further comprises: Send third information, where the third information is used to configure the corresponding relationship, where the corresponding relationship includes a corresponding relationship between each channel estimation parameter in at least one channel estimation parameter and an index, and the at least one channel estimation parameter group includes the first channel estimation parameter.

17. The method according to any one of claims 14 to 16, characterized in that The matrix norm of the first expression and / or the iteration step size are parameters for channel estimation, and the channel estimation satisfies: described It is a fixed point operator designed based on fixed point theory; in, represents the output of the k-th channel estimation, represents the output of the k-1th channel estimation, Indicates that the first signal y and the is input, and the output is obtained by performing the k-th channel estimation.

18. The method according to claim 17, characterized in that described Includes nonlinear estimators.

19. The method according to claim 17, wherein described It includes a linear estimator and a nonlinear estimator, and the output of the linear estimator is the input of the nonlinear estimator.

20. The method according to claim 18 or 19, characterized in that The nonlinear estimator is implemented through a neural network model.

21. The method according to claim 20, characterized in that The method further comprises: Training the neural network model to obtain parameters of the neural network model, wherein the neural network model is used for channel estimation; Second information is sent, where the second information indicates parameters of the neural network model.

22. The method according to any one of claims 19 to 21, characterized in that described satisfy: Where I is the identity matrix, g is the regularization term, represents the subgradient of g, A is the precoding matrix, M is the matrix norm, and σ is the iteration step size.

23. The method according to claim 22, characterized in that The mathematical model of the linear estimator is: The mathematical model of the nonlinear estimator is:

24. The method according to any one of claims 14 to 23, characterized in that The method further comprises: Send fourth information, where the fourth information indicates the value of T.

25. A communication device, characterized in that: The method comprises a module for implementing the method according to any one of claims 1 to 13; or the method comprises a module for implementing the method according to any one of claims 14 to 24.

26. A communication device, characterized in that: The device comprises a processor configured to cause the communication device to implement the method according to any one of claims 1 to 13 by executing a computer program and / or a logic circuit, or to cause the communication device to implement the method according to any one of claims 14 to 24.

27. The device according to claim 26, characterized in that The system further comprises a memory for storing a computer program and / or a configuration file of the logic circuit.

28. The device according to claim 26 or 27, characterized in that A communication interface is also included for inputting and / or outputting signals.

29. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 13 is executed, or the method according to any one of claims 14 to 24 is executed.

30. A computer program product, characterized in that The invention comprises a computer program, and when the computer program is run, the method according to any one of claims 1 to 13 is executed, or the method according to any one of claims 14 to 24 is executed.

31. A communication system, characterized in that: The method comprises a terminal device and a network device, wherein the terminal device is used to implement the method according to any one of claims 1 to 13, and the network device is used to implement the method according to any one of claims 14 to 24.

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