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 apparatus

[0001] The present application claims priority to the Chinese patent application No. 202410341276.X, filed on March 22, 2024, and entitled "Channel estimation method and related apparatus", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

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

[0003] In a wireless communication system, when a signal is transmitted through a wireless channel, it will be affected by noise and interference, resulting in an error between the signal received at the receiving end and the signal sent at the transmitting end. In order to remove the influence of noise and interference, the communication device needs to determine the characteristics of the wireless channel, and the process of determining the characteristics of the wireless channel by the communication device can be referred to as channel estimation.

[0004] Exemplarily, a communication device can perform channel estimation through a channel estimation algorithm and a received signal. At present, the following channel estimation algorithms are proposed: a classical channel estimation algorithm based on a mathematical closed-form solution (for example, a least square (LS) method, a minimum mean squared error (MMSE) method), the calculation process of which is simple but it is difficult to obtain accurate channel estimation results in all scenarios.

[0005] Improving the accuracy of channel estimation results is a problem to be solved. SUMMARY

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

[0007] In a first aspect, the present application provides a channel estimation method, which can be applied to a first communication device. For example, the first communication device can be a terminal device, or it can also be a component (such as a chip, a chip system, etc.) configured in the terminal device, or it can also be a logic module or software capable of realizing all or part of the functions of the terminal device, and the present application does not limit this.

[0008] The method comprises: receiving a first signal; receiving first information, the first information being used for indicating a channel estimation parameter, the channel estimation parameter comprising at least one of a matrix norm of a first expression or an iteration step, at least one of the matrix norm or the iteration step being determined based on a precoding matrix; performing T times of channel estimations based on the first signal and the channel estimation parameter to obtain a channel estimation result, T being a positive integer, T being a number of iterations of the channel estimation.

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

[0010] Exemplarily, the first expression can be: wherein y represents the first signal (i.e., a signal received by the terminal device), A represents the precoding matrix, represents the kth channel estimation value.

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

[0012] Based on this technical solution, the terminal device performs channel estimation based on the obtained matrix norm of the first expression and the iteration step, and the received signal, the matrix norm and the iteration step being two parameters obtained based on the fixed point theory, so 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, i.e., the T times of 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, so the channel estimation method provided by the present application can improve the accuracy of channel estimation.

[0013] Optionally, the first information comprises the first channel estimation parameter. The first channel estimation parameter can comprise specific values of the matrix norm and / or the iteration step; or comprise a calculation formula of the matrix norm and / or the iteration step, the terminal device determining the matrix norm and / or the iteration step 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 comprises the first index, and the first index and the first channel estimation parameter satisfy a corresponding relationship.

[0015] In some implementations, the correspondence is predefined or indicated by the network side, and the correspondence includes a correspondence between each parameter group in the at least one parameter group and an index, the at least one parameter group including 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 correspondence is indicated by the network side, the method further includes: receiving third information from the network device, the third information being used for configuring the correspondence.

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

[0019] In some implementations, in the T times of channel estimation, the kth time of channel estimation satisfies: The is a fixed point operator designed based on fixed point theory. It can be understood that the matrix norm of the first expression and the iteration step length can be parameters in the .

[0020] wherein, denotes an output of the kth time of channel estimation, denotes an output of the (k-1)th time of channel estimation, denotes an output obtained by taking the first signal y and the as inputs and performing the kth time of channel estimation.

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

[0022] In some implementations, the includes a nonlinear estimator, and in the kth time of channel estimation: an input of the nonlinear estimator is an 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 T channel estimations, and thus the T channel estimations can be implemented by the nonlinear estimator.

[0024] With reference to the first aspect, in some implementations, the method further includes: obtaining parameters of the neural network model, the neural network model being used for channel estimation. The linear estimator and the nonlinear estimator are included, and 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

[0025] The input of the linear estimator is the same as the input of the kth channel estimation in the T channel estimations, and the output of the nonlinear estimator is the same as the output of the kth channel estimation in the T channel estimations, and thus the T channel estimations can be implemented by the linear estimator and the nonlinear estimator.

[0026] Optionally, the method further includes: obtaining the parameters of the neural network model. The nonlinear estimator included in the method is implemented by a neural network model.

[0027] With reference to the first aspect, in some implementations, the method further includes: obtaining parameters of the neural network model, the neural network model being used for channel estimation.

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

[0029] Optionally, the 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 can determine the parameters of the neural network model based on the parameters of the channel estimation.

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

[0032] With reference to the first aspect, in some implementations, the value of T is predefined or indicated by a network side.

[0033] Optionally, in the case where the value of T is indicated by the network side, the method further comprises: receiving fourth information, the fourth information indicating the value of T.

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

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

[0036] P:

[0037] Constraint condition:

[0038] wherein, is a fixed point equation, is a solution of the fixed point equation, representing a channel estimation value; y is the first signal, and h is a real value of the channel, represents taking an average of ; and P has a physical meaning of: obtaining a function that minimizes the channel estimation error

[0039] The channel estimation error is an error between the real value of the channel and the channel estimation value.

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

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

[0042] The first signal is a signal of the second signal after passing through a wireless channel, and the first signal and the first channel estimation parameter are used to perform T times of 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] The description of the first expression can refer to the related description of the first aspect, and will not be repeated here.

[0045] Based on the technical solution, the network device indicates the matrix norm of the first expression and the iteration step to the terminal device, so that the terminal device can perform channel estimation based on the obtained matrix norm and iteration step, and the received signal. The matrix norm and the iteration step are two parameters obtained based on the fixed point theory, so it can be said that the terminal device uses 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 times of channel estimation 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 by the present application can improve the accuracy of channel estimation.

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

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

[0048] For example, when the network device indirectly indicates the first channel estimation parameter to the terminal device through the first information, the first information can 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, refer to the related 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, the third information being used for configuring the corresponding relationship, the corresponding relationship including a corresponding relationship between each channel estimation parameter in the at least one channel estimation parameter and an index, the at least one channel estimation parameter including the first channel estimation parameter.

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

[0052] The description of the third information can refer to the related description of the first aspect, which will not be repeated here.

[0053] In some implementations of the second aspect, the method further includes: sending fourth information, the fourth information indicating a value of the number of channel estimations T.

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

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

[0056] wherein, denotes an output of the kth channel estimation, denotes an output of the (k-1)th channel estimation, denotes an output of the kth channel estimation performed on the first signal y and the as input.

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

[0058] Optionally, the The nonlinear estimator included in the nonlinear estimator is implemented by a neural network model.

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

[0060] Optionally, the network device can obtain the 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-obtained correspondence, to obtain neural network model parameters corresponding to different parameter groups. In this way, when determining the first information, the network device can determine the neural network model parameters corresponding to the parameter group indicated by the first information from the pre-obtained neural network model parameters, and indicate the neural network model parameters to the terminal device.

[0062] In some implementations of the first and second aspects, the satisfies:

[0063] wherein I is an identity matrix, g is a regularization term, denotes a sub-gradient of g, A is a precoding matrix, and M is the matrix norm, σ is the iteration step size.

[0064] Exemplarily, in the case that and A is an identity matrix, the includes a nonlinear estimator and does not include a linear estimator, and the mathematical model of the nonlinear estimator can be

[0065] Exemplarily, in the case that and A is not an identity matrix, the includes a nonlinear estimator and a linear estimator, and the mathematical model of the linear estimator can be: the mathematical model of the nonlinear estimator can be

[0066] wherein, may be implemented by a neural network model.

[0067] In a third aspect, a communication apparatus is provided, which can be used in the first communication apparatus of the first aspect, and can be a terminal device, a device (for example, a chip, or a chip system, or a circuit) of the terminal device, or a device capable of being used in matching with the terminal device, and can also be a device capable of implementing all or part of the logic modules or software of the terminal device; or the communication apparatus can be used in the second communication apparatus of the second aspect, and can be a network device, a device (for example, a chip, or a chip system, or a circuit) of the network device, or a device capable of being used in matching with the network device, and can also be a device capable of implementing all or part of the logic modules or software of the network device.

[0068] In a possible implementation, the communication apparatus can include modules or units corresponding to the methods / operations / steps / actions described in the first aspect, which can be hardware circuits, software, or a combination of hardware circuits and software.

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

[0070] In a possible implementation, the communication apparatus can include a transceiver and a processing module. The transceiver can be configured to receive a first signal and receive first information. The processing module can be configured to perform T times of channel estimation based on the first signal and the first channel estimation parameter to obtain a channel estimation result. Alternatively, the transceiver can be configured to send a second signal. The processing module can be configured to determine the first information based on a precoding matrix. The transceiver can be further configured to send the first information.

[0071] The descriptions of the first information, the first signal and the second signal can refer to the descriptions of the first and second aspects, which will not be repeated here.

[0072] In a fourth aspect, the present application provides a communication apparatus, including a processor, which is configured to execute the method in any of the above aspects and any possible implementation manner of the above aspects.

[0073] The apparatus can further include a memory configured to store instructions and / or data. The memory is coupled to the processor, and the processor executes the instructions stored in the memory to implement the method described in the above aspects.

[0074] The apparatus can further include a communication interface configured to enable the apparatus to communicate with other devices. For example, the communication interface can 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, including at least one processor configured to support the functions involved in any of the above aspects and any possible implementation manner of the above aspects, such as receiving or processing the data and / or information involved in the above method.

[0076] In a possible design, the chip system can further include a memory configured to store program instructions and data. The memory can be located in the processor or outside the processor.

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

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

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

[0080] In an eighth aspect, the present application provides a communication system, comprising the terminal device and the network device as described above. The terminal device is configured to implement the method of the first aspect and any possible implementation of the first aspect. The network device is configured to implement the method of the second aspect and any possible implementation of the second aspect.

[0081] It should be understood that the third aspect to the eighth aspect of the present application correspond to the technical solutions of the first aspect or the second aspect of the present application, and the beneficial effects achieved by each aspect and the corresponding possible implementation manners are similar, which will not be described herein again. BRIEF DESCRIPTION OF DRAWINGS

[0082] FIG. 1 is a schematic diagram of an architecture of a communication system suitable for the method provided by the embodiments of the present application;

[0083] FIG. 2 is a schematic flowchart of a channel estimation method provided by the embodiments of the present application;

[0084] FIG. 3 is a schematic diagram of an architecture of a neural network model provided by the embodiments of the present application;

[0085] FIG. 4 is a schematic diagram of a connection manner of a linear estimator and a nonlinear estimator provided by the embodiments of the present application;

[0086] FIG. 5 is a comparative diagram of normalized mean square errors corresponding to various algorithms provided by the embodiments of the present application;

[0087] FIG. 6 is a schematic block diagram of an apparatus provided by the embodiments of the present application;

[0088] FIG. 7 is another schematic block diagram of an apparatus provided by the embodiments of the present application. DETAILED DESCRIPTION

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

[0090] In order to facilitate the understanding of the embodiments of the present application, the following points will be explained first:

[0091] First, in the embodiments of the present application, the use of prefixes such as "first", "second" and the like is only for the convenience of distinguishing and describing different things belonging to the same name category, and does not constrain the order, size or quantity of the things. For example, "first information" and "second information" are only different signals, and there is no time sequence relationship, size relationship or priority relationship between them.

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

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

[0094] It can be understood that the information can be processed as necessary, such as encoding and modulation, before being sent from the source to the destination. The destination can also perform corresponding processing, such as decoding and demodulation, after receiving the information from the source, so as to interpret the valid information from the source. Similar expressions in the present application can be similarly understood, and will not be repeated here.

[0095] Thirdly, in the embodiments of the present application, "at least one" means one or more, and "multiple" means two or more. "And / or" describes the association between the associated objects, which means that there can be three kinds of relationships, for example, A and / or B, which can represent the following three cases: A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after it, but does not rule out the case that the associated objects before and after it represent an "and" relationship. The specific meaning can be understood in combination with the context. "At least one of the following" or similar expressions means any combination of these items, including any combination of single item or multiple 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, the indication can include direct indication and indirect indication, and can also include explicit indication and implicit indication. The information indicated by certain information (indication information described below) is referred to as to-be-indicated information. In the implementation process, there are many ways to indicate the to-be-indicated information, for example, but not limited to, the to-be-indicated information can be directly indicated, such as the to-be-indicated information itself or an index of the to-be-indicated information. The to-be-indicated information can also be indirectly indicated by indicating other information, where the other information and the to-be-indicated information have an association relationship. The to-be-indicated information can also be indicated only by a part of the to-be-indicated information, and the other part of the to-be-indicated information is known or agreed in advance. For example, the indication of a specific information can be achieved by means of the arrangement order of each information agreed in advance (for example, protocol predefined), thereby reducing the indication overhead to a certain extent. The specific manner of indication is not limited in the present application.

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

[0098] Fifth, the tables in the embodiments of the present application are only examples. The values of the information in the tables are only examples, and can be configured as other values. The present application is not limited. The tables do not limit the protection scope of the present application. For example, the tables can be appropriately deformed and adjusted, for example, split, merged, and the like. For another example, the parameter names shown in the titles of the tables can also use other names understandable by the communication device, and the values or representation methods of the parameters can also use other values or representation methods understandable by the communication device. For another example, in the implementation, the tables can also use other data structures, for example, arrays, queues, containers, stacks, linear tables, pointers, linked lists, trees, graphs, structures, classes, heaps, hash tables, or the like.

[0099] Sixth, in the embodiments of the present application, the descriptions such as “when”, “in the case of”, “if”, and the like all refer to that under certain objective circumstances, the device (such as the first device or the second device) will make corresponding processing, which is not limited by time, and does not require the device to have a judgment action when implemented, and also does not mean that there are other limitations.

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

[0101] The technical solutions provided in the present application can be applied to various communication systems, for example: a long term evolution (LTE) system, an LTE frequency division duplex (FDD) system, an LTE time division duplex (TDD), a sidelink (SL) communication system, a 5th generation (5G) mobile communication system or a new radio access technology (NR), a satellite communication system, and the like. The 5G mobile communication system can include non-standalone (NSA) and / or standalone (SA).

[0102] The technical solutions provided in the present application can also be applied to a communication system evolved after 5G, such as a 6th generation (6G) mobile communication system, and the like. The present application does not make a limitation in this regard.

[0103] The radio access network (RAN) device in the present application is a device with wireless transceiving function. The radio access network device can provide wireless communication function service and can access terminals to a wireless network. The radio access network device can be a node in the radio access network, referred to as a RAN node.

[0104] In a possible scenario, the RAN node can be a base station (BS), an evolved NodeB (eNodeB), a transmission reception point (TRP), a home evolved NodeB, or a home Node B (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, and the like. The RAN node can also be a device assuming the function of a base station in a device to device (D2D) communication system, a vehicle to everything (V2X) communication system, a machine to machine (M2M) communication system, and an internet to things (IoT) communication system, and the like. The RAN node can also be a RAN node in a non terrestrial network (NTN), that is, the RAN node can be deployed in a high altitude platform or a satellite. The RAN node can be a macro base station, or a micro base station or an indoor station, or a relay node or a donor node, and the like, or a radio controller in a cloud radio access network (CRAN) scenario, a node in an open radio access network (O-RAN or ORAN) scenario, and the like. Alternatively, the RAN node can also be a server, a wearable device, a vehicle or a vehicle-mounted device, and the like. For example, the RAN node in a V2X technology can be a roadside unit (RSU). Of course, the RAN node can also be a node in a core network.

[0105] In another possible scenario, multiple RAN nodes cooperate to assist a terminal to implement wireless access, and different RAN nodes respectively implement part of functions of a base station. For example, a RAN node can be a central unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU), etc. The CU and the DU can be separately configured, or can also be included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or a 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, the CU (or CU-CP and CU-UP), DU or RU can also have different names, but those skilled in the art can understand their meanings. For example, in an ORAN system, the CU can also be referred to as an open CU (O-CU), the DU can also be referred to as an open DU (O-DU), the CU-CP can also be referred to as an open CU-CP (O-CU-CP), the CU-UP can also be referred to as an open CU-UP (O-CU-UP), and the RU can also be referred to as an open RU (O-RU).

[0107] Any of the 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 radio access network device in this application can be a virtualized device, which can be implemented by general hardware and instantiated virtualized functions, or by special hardware and instantiated virtualized functions. The general hardware can be a server, such as a cloud server.

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

[0109] The terminal can be a device that provides voice / data connectivity to a user, for example, a handheld device with wireless connection function, a vehicle-mounted device, etc. Currently, some examples of terminal devices can be: a mobile phone, a pad, a computer (such as a notebook computer, a palm computer, etc.) with wireless transceiver function, a mobile internet device (MID), a virtual reality (VR) device, an augmented reality (AR) device, a smart point of sale (POS) machine, a customer-premises equipment (CPE), a wireless terminal in industrial control, a wireless terminal in self driving, a drone, a terminal device in an IoT system, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, a cellular phone, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA), a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a wearable device, a terminal device in a 5G network, or a terminal device in a future evolved public land mobile network (PLMN), etc.

[0110] Among them, the wearable device can also be called a wearable smart device, which is a general term for devices that can be designed and developed by applying wearable technology to daily wear, such as glasses, gloves, watches, rings, clothing, and shoes. The wearable device is a portable device that can be directly worn on the body or integrated into the user's clothes or accessories. The wearable device is not only a hardware device, but also a powerful function achieved through software support and data interaction, cloud interaction. The general wearable smart device includes a full function, large size, and can realize complete or partial functions without relying on a smart phone, such as a smart watch or smart glasses, and only focuses on a certain application function and needs to cooperate with other devices such as a smart phone, such as various smart wristbands and smart jewelry for monitoring vital signs.

[0111] In addition, the terminal device can also include a smart printer, a train detector, a gas station sensor, and the like, and the main functions include collecting data (for some terminal devices), receiving control information and downlink data of a network device, and transmitting electromagnetic waves to transmit uplink data to the network device.

[0112] The terminal in the present application can be a virtualized device, which can be implemented by using general hardware and instantiated virtualization functions, or special hardware and instantiated virtualization functions. The general hardware can be a server, such as 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] FIG. 1 is a schematic diagram of an architecture of a communication system 100 applicable to the method provided by the embodiments of the present application. As shown in FIG. 1, the communication system 100 includes a wireless access network 10 and a core network 20, and optionally, the communication system 100 can also include an Internet 30. The wireless access network 10 can include at least one wireless access network device (such as 110a and 110b in FIG. 1), and can also include at least one terminal (such as 120a-120j in FIG. 1).

[0115] The terminal can be connected to the wireless access network device in a wireless manner, and the wireless access network device can be connected to the core network in a wireless or wired manner. The core network device and the wireless access network device can be independent and different physical devices, or can be integrated into the same physical device, or can be a physical device integrated with part of the functions of the core network device and part of the functions of the wireless access network device. The terminals can be connected to each other in a wired or wireless manner, and the wireless access network devices can be connected to each other in a wired or wireless manner.

[0116] The wireless access network device and the terminal, the wireless access network device and the wireless access network device, and the terminal and the terminal can communicate through a licensed spectrum, or through an unlicensed spectrum, or through both the licensed spectrum and the unlicensed spectrum; can communicate through a spectrum below 6 gigahertz (GHz), or through a spectrum above 6 GHz, or through both the spectrum below 6 GHz and the spectrum above 6 GHz. The embodiments of the present application do not limit the spectrum resources used for wireless communication.

[0117] The wireless access network device can 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 the drone 120i in FIG. 1; or a terminal deployed on the ground, such as the mobile phone 120a, 120e, 120f, and 120j, the vehicle 120b, the computer 120g, the printer 120h, and the like in FIG. 1.

[0119] The wireless access network device and the terminal can be fixed in position or movable. For example, the wireless access network device and the terminal can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; can be deployed on water; or can be deployed on an airplane, a balloon, or a man-made satellite in the air.

[0120] The roles of the wireless access network device and the terminal can be relative. For example, the helicopter or the drone 120i in FIG. 1 can be configured as a mobile base station, and for those 120j accessing the wireless access network 10 through 120i, 120i is a base station; but for 110a, 120i is a terminal, that is, 110a and 120i communicate with each other through a wireless air interface protocol. Of course, 110a and 120i can also communicate with each other through an interface protocol between wireless access network devices, and in this case, 120i is also a base station relative to 110a. Therefore, the wireless access network device and the terminal can be collectively referred to as a communication device, and 110a, 110b, and 120a-120j in FIG. 1 can be referred to as communication devices with their respective corresponding functions, such as a communication device with a base station function or a communication device with a terminal function.

[0121] It should be understood that FIG. 1 is only a schematic diagram, and the communication system can further include other devices, such as wireless relay devices and wireless backhaul devices, which are not shown in FIG. 1.

[0122] In a wireless communication system, when a signal is transmitted through a wireless channel, it will be affected by noise and interference. Therefore, the signal received by the receiving party will have errors compared with the transmitted signal. In order to remove the influence of noise and interference, it is necessary to determine the characteristics of the wireless channel. The process of determining the characteristics of the wireless channel is called channel estimation.

[0123] Channel estimation mainly includes the following steps: first, the channel needs to be mathematically modeled; second, the transmitting end transmits a signal known to both the transmitting end and the receiving end (for example, a reference signal or a pilot signal) to the receiving end; and finally, the receiving end compares the received signal containing 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-based channel estimation algorithms; and neural network-based channel estimation algorithms.

[0125] The following section describes the process of channel estimation by communication equipment based on the three channel estimation algorithms mentioned above, assuming the transmitted signal is X, the received signal is Y, and the channel is H.

[0126] 1. The channel estimation algorithm based on the least squares method yields the channel estimate. satisfy:

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

[0128] 2. The channel estimation algorithm based on the minimum mean square error method yields the channel estimate. satisfy:

[0129] in, For matrix H and matrix The cross-correlation matrix between them For matrix The autocorrelation matrix.

[0130] The channel estimation algorithm based on the least squares method described above is simple, but it is sensitive to noise. As the signal-to-noise ratio decreases, the performance of the least squares-based channel estimation algorithm deteriorates significantly. Compared with the least squares-based channel estimation algorithm, the channel estimation algorithm based on the minimum mean square error is more effective in combating noise, but it has high implementation complexity and is difficult to implement in hardware because it requires calculating the matrix inverse.

[0131] The aforementioned channel estimation algorithms based on least squares and minimum mean square error are both channel estimation methods based on mathematical closed-form solutions. Although these methods have relatively low computational complexity, they may result in inaccurate channel estimates. Therefore, to overcome the shortcomings of mathematical closed-form solution algorithms, an iterative channel estimation algorithm is proposed. This algorithm converges the channel estimate to a certain value through iteration, and then determines this value as the final channel estimation result.

[0132] According to the principle of iteration, it can be divided into two types: one is to increase the regularization term after the least square expression, and combine the optimization method such as proximal gradient descent method or alternating direction multiplier method to design the iterative algorithm. Two, use the probability model and combine the channel prior information to minimize the mean-square error (MSE) iterative algorithm. For example, approximate message passing (AMP), orthogonal approximate message passing (OAMP), and the expansion algorithm based on the two methods. In the iterative-based channel estimation algorithm, the terminal device may need to iterate multiple times, and the architecture used in each iteration can be summarized as: a linear estimator (LE) and a non-linear estimator (NLE) in series. Among them, the design difficulty of LE is relatively low, which can be designed based on an explicit linear mathematical expression; but the design difficulty of NLE is relatively high, and enough accurate channel prior information is needed when designing NLE, but the channel prior information is difficult to obtain.

[0133] Therefore, in order to overcome the design difficulty of NLE in the iterative algorithm, a neural network is used to simplify the design complexity of NLE. As follows shows a channel estimation algorithm based on deep unfolding network (DUN). The channel estimation algorithm based on DUN can regard each iteration in the iterative algorithm as a layer of neural network. That is, 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 replaced by a neural network with parameters Θ t .

[0134] Exemplarily, the channel estimation value obtained by the above-mentioned channel estimation method based on DUN satisfies:

[0135] P1:

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

[0137] The above neural network-based channel estimation algorithm can solve the design complexity of the NLE, but the algorithm is a design that truncates the traditional iteration into P-layer iteration, and therefore to some extent, the convergence of the traditional iteration is destroyed, and thus it is difficult to guarantee the accuracy of the channel estimation result.

[0138] Therefore, embodiments of the present application provide a channel estimation method and related apparatus, which utilize the characteristics of the fixed point theory as a constraint condition for designing a channel estimator, so that the designed channel estimator can theoretically guarantee that the optimal solution converges to the fixed point, and the accuracy of the channel estimation is improved.

[0139] The channel estimation method provided by the embodiments of the present application will be described in detail below in combination with FIG. 2. The method provided by the embodiments of the present application can be applied to the network architecture shown in FIG. 1, but the embodiments of the present application are not limited thereto. The channel estimation method can be applied to a first communication apparatus and a second communication apparatus.

[0140] FIG. 2 is a schematic flowchart of a channel estimation method 200 provided by an embodiment of the present application. In the flowchart shown in FIG. 2, the method is shown from the perspective of the interaction between a first communication apparatus as a terminal device and a second communication apparatus as a network device, but the present application does not limit the execution subject of the method. For example, the terminal device in FIG. 2 can be replaced by a chip, a chip system, or a processor supporting the terminal device to implement the method, and can also be a logic module or software capable of implementing all or part of the functions of the terminal device; the network device in FIG. 2 can be replaced by a chip, a chip system, or a processor supporting the network device to implement the method, and can also be a logic module or software capable of implementing all or part of the functions of the network device.

[0141] As shown in FIG. 2, the method 200 can include S201 to S203. The steps in the method 200 will be described in detail below.

[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 a wireless channel. It can be understood that the first signal can be used to obtain the channel information of the wireless channel through which the second signal passes.

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

[0145] At S202, the network device determines, based on the precoding matrix, first information used for indicating a first channel estimation parameter.

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

[0147] The precoding matrix can be a downlink precoding matrix determined by the network device according to the PMI and the RI from the terminal device. That is, the method can further include that the terminal device determines the PMI and the RI, and sends the PMI and the RI to the network device.

[0148] The process of the terminal device determining the PMI and the RI can refer to the related description in 3GPP standard technical specification (TS) 36.213, and the manner of the network device determining the downlink precoding matrix based on the PMI and the RI can refer to the related description in 3GPP standard TS 38.214, which are not described herein again.

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

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

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

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

[0153] Exemplarily, in the case that the first expression is The matrix norm of the first expression can be represented as:

[0154] Among them, || || M Let M denote the M-norm, where 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 parameters to obtain the channel estimation result.

[0157] Here, performing T channel estimations can be understood as performing T iterations. The initial input for these T iterations (i.e., the input for the first iteration) is the first signal received by the terminal device and the initial value of the channel. The input to the k-th iteration in these T iterations is the output of the (k-1)-th iteration. Thus, the channel estimation result obtained by the terminal device can be the output of the T-th iteration after the terminal device has performed 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 The initial channel value can be a channel estimate 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 channel value.

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

[0161] For example, a network device can directly or indirectly indicate a first channel estimation parameter to a terminal device through the first information.

[0162] In the first possible implementation (direct indication), the first information mentioned above includes the first channel estimation parameters.

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

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

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

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

[0167] Wherein, A is a precoding matrix, A T represents the transpose of A, represents the trace of the matrix , and represents the conjugate transpose of the matrix A, and n represents the number of columns of the matrix A.

[0168] In the second possible implementation (indirect indication), the above-mentioned first information includes a first index, and the first index and the first channel estimation parameter satisfy a corresponding relationship.

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

[0170] Optionally, the corresponding relationship includes a corresponding relationship between each parameter group in the at least one channel estimation parameter and an index, and the at least one channel estimation parameter includes the 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 can be specific values of the matrix norm and / or the iteration step length, such as M=1 and σ=0.5; or can be a calculation formula for determining the matrix norm and / or the iteration step length, such as M=(AA T ) -1 and

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

[0174] Optionally, the correspondence can be predefined or indicated by the network side.

[0175] In the case that the correspondence is indicated by the network side, the method 200 can further include: the network device sending third information to the terminal device, the third information being used for configuring the correspondence.

[0176] Exemplarily, the network device can configure at least one parameter group for the terminal device through layer 3 (L3) signaling. For example, the network device configures the correspondence between the parameter group and the index as shown in Table 1 for the terminal device through L3 signaling. In this way, when sending the first information, the network device can dynamically indicate the first channel estimation parameter to the terminal device through 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 DCI; if the network device indicates the first channel estimation parameter to the terminal device through L2 (wireless network layer) signaling, the above-mentioned first information can be carried in MAC CE.

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

[0179] Table 1 shows a correspondence.

[0180] Table 1

[0181] As shown in Table 1, each parameter group includes the matrix norm M and the iteration step size σ The description of other parameters in Table 1 can refer to the related description in the foregoing, which will not be described herein.

[0182] It can be understood that each parameter group shown in Table 1 can also include only the matrix norm M or only the iteration step size σ , which is not limited in the present application.

[0183] Optionally, the parameter of the channel estimation can be understood as a parameter used for channel estimation, and the k-th channel estimation in the T times of channel estimation satisfies: The is a fixed point operator designed based on the fixed point theory.

[0184] The fixed point theorem means that there is at least one fixed point x for the function f() under certain conditions, that is, there is at least one point x such that f(x) = x. Since is a fixed point operator designed based on the fixed point theory, the function at least one point such that the function

[0185] wherein, denotes the output of the kth channel estimation, denotes the output of the (k-1)th channel estimation, denotes the output of the kth channel estimation with the first signal y and the output of the (k-1)th channel estimation as inputs.

[0186] It can be understood that the output of the kth channel estimation is the input of the (k+1)th channel estimation. That is, the output of the (k-1)th channel estimation is the input of the kth channel estimation. It should be noted that when k=1, Actually, the input of the first channel estimation is the initial input.

[0187] It is mentioned above that the channel estimation result obtained by the terminal device is the output value of the Tth iteration after the terminal device performs T iterations. In the 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-1)th channel estimation is the same as the output of the kth channel estimation), the outputs of the (k+1)th to Tth channel estimations are the same as the output of the kth channel estimation.

[0188] In a possible implementation, in the case that the outputs of the (k+1)th to Tth channel estimations are the same as the output of the kth channel estimation, the terminal device can also determine the output of any one of the (k-1)th to Tth channel estimations as the channel estimation result.

[0189] In a 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+1)th to Tth channel estimations. This implementation can avoid resource waste. It should be noted that the input of the kth channel estimation being the same as the output of the kth channel estimation mentioned above can only exist in theory, and in actual channel estimation, there can be some errors between the input of the kth channel estimation and the output of the kth channel estimation, which are errors that can be allowed in channel estimation, or the channel estimation value obtained within the error range can be considered as the real 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 a 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] In a possible implementation, the above includes a 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 that the nonlinear estimator is implemented by a neural network model, the method 200 further includes: the terminal device acquires parameters of the neural network model, which is used for channel estimation.

[0193] Exemplarily, the terminal device can acquire the parameters of the neural network model by training the neural network model; or the terminal device can acquire the parameters of the neural network model through the second information indicated by the network side; or the terminal device can further determine the parameters of the neural network model through the acquired parameters of the channel estimation.

[0194] Optionally, in the case that the terminal device acquires the parameters of the neural network model through the second information indicated by the network side, the method 200 further 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 can be understood that the training of the neural network model is completed before T times of channel estimation (for example, initial access). The training process can be completed in an offline state.

[0196] Exemplarily, the network device can train the neural network model to determine the parameters of the neural network model corresponding to different parameter values of the channel estimation, in the case that the parameter values of the channel estimation determined according to the precoding matrix change.

[0197] Alternatively, the terminal device can train the neural network model based on the channel estimation parameters included in one or more parameter groups included in the pre-acquired corresponding relationship, to obtain the parameters of the neural network model corresponding to each parameter group. In this way, when the terminal device performs channel estimation, it can determine the parameters of the neural network model corresponding to the channel estimation parameters in the case of acquiring the channel estimation parameters.

[0198] The process of training the neural network model in the present 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 values obtained by the adjusted parameter values and the true channel values is minimized, and the parameter value corresponding to the minimum error is determined as the parameter of the neural network model, and the training of the neural network model is completed. That is, the error between the channel estimation value obtained by 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] Exemplarily, the network device or the terminal device can optimize the parameters of the neural network model by designing a loss function, so that the terminal device obtains a more accurate channel estimation value when performing channel estimation using the optimized parameters. The loss function can be a function for calculating the difference or mean square error (MSE) between the channel estimation value obtained by the neural network model and the true channel value.

[0200] When the loss function is designed by calculating the mean square error, the parameters θ of the neural network model satisfy:

[0201] wherein, represents the loss function the value of the variable when the minimum value is reached, the function is a function for calculating the mean square error of the channel estimation value and the true channel value h, and m is the number of training samples, which are the received signal and the true value of the channel. The training samples can be pre-obtained channel prior information, historical data, or data generated by a channel generator (for example, a quasi deterministic radio channel generator (QuaDRiGa)) conforming to the 3GPP standard.

[0202] FIG. 3 shows an architecture of a neural network model. As shown in FIG. 3, the neural network model can include two convolution (conv) layers, two residential blocks (RBs), and an activation function. The input of the first convolution layer 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 the first convolution layer is defined as output 1, the output 1 is the input of the RBs 1, the output of the RBs 1 is the input of the RBs 2, the output of the RBs 2 is the input of the second convolution layer, and the output of the second convolution layer is the input of the activation function.

[0203] In another possible implementation, the above The linear estimator and the nonlinear estimator are included.

[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, in the kth channel estimation, the input of the nonlinear estimator is In the case where the linear estimator and the nonlinear estimator are included, 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, in the kth channel estimation, the input of the nonlinear estimator is In the case where the linear estimator and the nonlinear estimator are included, 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 how the terminal device obtains the parameters of the neural network model can refer to the foregoing related description, which will not be described here.

[0207] The neural network model used in the above DUN-based channel estimation method includes P layers, each layer needs to be independently trained as a separate subnetwork, the network structure is complex, and the training difficulty is large. In comparison, the neural network model used to implement the nonlinear estimator in the embodiments of the present application includes simple convolution layers and residual network blocks, the structure is simple, the training complexity is low, the trained neural network can be repeatedly used in each iteration of channel estimation, the generalization is high, and the scalability is good.

[0208] FIG. 4 shows a connection diagram of a linear estimator and a nonlinear estimator. As shown in FIG. 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 times of channel estimation are needed, in the kth time of channel estimation, the input of the kth time of the linear estimator is the output of the (k-1)th time of the nonlinear estimation.

[0209] It can be understood that the input of the nonlinear estimator in the channel estimator shown in FIG. 4 can also be the input of the channel estimator, the output of the nonlinear estimator is the input of the linear estimator, and the output of the linear estimator is the output of the channel estimator.

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

[0211] The above-mentioned A possible expression is that the above-mentioned satisfies:

[0212] where I is an identity matrix, g is a regularization term, denotes the sub-gradient of g, A is a precoding matrix, and M is a matrix norm. σ is an iteration step size.

[0213] Exemplarily, in the case that and A is an identity matrix, the above-mentioned includes the nonlinear estimator and does not include the linear estimator, and the mathematical model of the nonlinear estimator can be

[0214] Exemplarily, in the case that and A is not an identity matrix, the above-mentioned includes the nonlinear estimator and the linear estimator, and the mathematical model of the linear estimator can be: The mathematical model of the nonlinear estimator can be

[0215] wherein, can be implemented through a neural network model R θ .

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

[0217] Optionally, in the case that the value of T is indicated by the network side, the method 200 further comprises: the network device sends 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] Optionally, the value of T can also be determined by the terminal device according to the channel estimation result. For example, the terminal device performs 15 times of channel estimation, and the obtained channel estimation values converge to a certain value, and then T=15.

[0219] Optionally, the above-mentioned T times of channel estimation can be implemented by a channel estimator. The channel estimator can include a nonlinear estimator, or the channel estimator includes a linear estimator and a nonlinear estimator.

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

[0221] P2:

[0222] Constraint condition:

[0223] wherein, is a fixed point equation, f is a fixed point operator, is a solution of the fixed point equation, represents a channel estimation value; y is the downlink reference signal, h is a real value of the channel, represents the average of ; the physical meaning of P2 is to obtain a function that minimizes the channel estimation error The channel estimation error refers to the error between the real value of the channel and the channel estimation value.

[0224] The present application defines the normalized MSE (NMSE) between the channel estimation value obtained based on the channel estimation method shown in the method 200 and the channel estimation value obtained by the existing channel estimation method and the real value of the channel, respectively, and the NMSE satisfies:

[0225] wherein, h is a channel simulation result (which can be understood as the real value of the channel) generated by a channel generator (for example, QuaDRiGa) conforming to the 3GPP standard, is a channel estimation value.

[0226] The existing channel estimation algorithms include a channel estimation algorithm based on a least square method, a channel estimation algorithm based on a 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] FIG. 5 shows a comparison diagram of normalized mean square errors corresponding to various algorithms. As shown in FIG. 5, the horizontal coordinate represents a signal-noise ratio (SNR) in decibels (dB), and the vertical coordinate represents an NMSE in dB. The NMSE corresponding to the channel estimation values obtained based on various channel estimation algorithms decreases as the SNR increases, and as the SNR gradually increases, the decreasing rate of the NMSE gradually decreases.

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

[0229] The channel estimation algorithm provided by the embodiment of the present application 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 by the embodiments of the present application can be described as follows: input M, and σ performing linear estimator from k=0 to obtain the output of the linear estimator and taking as the input of the nonlinear estimator implemented by the neural network R θ to obtain the output of the first iteration taking the output of the first iteration as the input of the next iteration, and so on until k=T-1, ending the loop and outputting

[0235] The method provided by the embodiments of the present application is described in detail above in combination with FIGS. 1 to 5, and the apparatus provided by the embodiments of the present application is described in detail below in combination with FIGS. 6 and 7.

[0236] To implement the functions in the method provided by the embodiments of the present application, the first communication apparatus and the second communication apparatus can each include a hardware structure and / or a software module, and the functions are implemented in the form of the hardware structure, the software module, or the hardware structure plus the software module. Whether a certain function is implemented in the form of the hardware structure, the software module, or the hardware structure plus the software module depends on the specific application and design constraints of the technical solutions.

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

[0238] FIG. 6 is a schematic block diagram of an apparatus 600 provided by the embodiments of the present application. The apparatus 600 can be a terminal or a network device, or an apparatus in a terminal device or a network device, or an apparatus that can be used in matching with a terminal device or a network device. In one possible implementation, the apparatus 600 can include modules or units corresponding to the methods / operations / steps / actions performed by the first communication apparatus or the second communication apparatus in the method embodiments described above, which can be hardware circuits, software, or a combination of hardware circuits and software. For example, as shown in FIG. 6, the apparatus 600 can include a transceiver module 610 and a processing module 620.

[0239] In one possible design, the apparatus 600 is configured to implement the functions of the terminal device in the method embodiments shown in FIG. 2.

[0240] Exemplarily, the transceiver 610 is configured 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 comprising at least one of a matrix norm of a first expression or an iteration step size, the first expression being determined by a precoding matrix, the first signal and a k-th channel estimation value, the matrix norm and the iteration step size being determined based on the precoding matrix, k being a positive integer less than or equal to T, T being a positive integer; and the processing module 620 is configured to perform T times of channel estimation based on the first signal and the channel estimation parameter to obtain a channel estimation result.

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

[0242] Optionally, the transceiver 610 is further configured to receive second information, the second information indicating the parameters of the neural network model.

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

[0244] For more detailed description of the transceiver 610 and the processing module 620, refer to the description of the embodiment shown in FIG. 2.

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

[0246] Exemplarily, the transceiver 610 is configured to transmit a first signal, and the processing module 620 is configured to determine first information based on a precoding matrix, the first information being used to indicate a first channel estimation parameter, the first channel estimation parameter comprising at least one of a matrix norm of a first expression or an iteration step size, the first expression being determined by the precoding matrix, the first signal and a k-th channel estimation value, k being a positive integer less than or equal to T, T being a positive integer; and the transceiver 610 is further configured to transmit the first information.

[0247] Optionally, the transceiver 610 is further configured to transmit third information, the third information being used to configure the correspondence, the correspondence comprising at least one channel estimation parameter, the at least one channel estimation parameter comprising the first channel estimation parameter.

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

[0249] Optionally, the transceiver module 610 is further configured to send fourth information, the fourth information indicating the value of the T.

[0250] More detailed description of the transceiver module 610 and the processing module 620 can be directly obtained by referring to the related description in the embodiment shown in FIG. 2, which will not be repeated here.

[0251] It should be noted that the apparatus 600 can include a sending module but not a receiving module. Alternatively, the apparatus 600 can include a receiving module but not a sending module. Specifically, whether the apparatus 600 includes a sending action and a receiving action in the above-mentioned schemes can be determined. It can be understood that, since the apparatus 600 has a communication function, it can also be referred to as a communication apparatus.

[0252] FIG. 7 is another schematic block diagram of an apparatus provided by an embodiment of the present application. As shown in FIG. 7, the apparatus 700 includes one or more processors 710. The processor 710 can be a general-purpose processor or a special-purpose processor, etc. For example, it can be a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the apparatus (e.g., a terminal device, a network device or a chip, etc.), execute software programs, and process data of the software programs.

[0253] Optionally, in one design, the processor 710 can include a program (which can also be referred to as code or instructions) that can be run on the processor 710, so that the apparatus 700 performs the method performed by the terminal device or the network device in the above method embodiments. In another possible design, the apparatus 700 includes a circuit (not shown in FIG. 7) for implementing the functions of the terminal device or the network device in the above method embodiments.

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

[0255] Optionally, the apparatus 700 can include one or more memories 720 having programs (which can also be referred to as code or instructions) stored thereon, the programs being run on the processor 710, so that the apparatus 700 performs the method performed by the terminal device or the network device in the above embodiments.

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

[0257] Optionally, the processor 710 and / or the memory 720 can also store data. The processor and the memory can be separately arranged or integrated together.

[0258] Optionally, the apparatus 700 can further include a communication interface 730. The processor 710 can 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 can also be referred to as a transceiver, a transceiving unit, a transceiving circuit, or a transceiver, which is configured to implement the transceiving 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 can be understood that the communication interface 730 can be a transceiver or an input / output interface.

[0260] It can be understood that, since the apparatus 700 has a communication function, it can also be referred to as a communication apparatus.

[0261] When the apparatus 700 is used to implement the method of FIG. 2, the processor 710 is configured to perform the functions of the processing unit, and the communication interface 730 is configured to perform the functions of the transceiving module. Whether the communication interface 730 is configured to transmit or receive can depend on whether the apparatus 700 is configured to perform a transmitting action or a receiving action in the scheme.

[0262] When the apparatus 700 is a chip applied to a terminal device, the chip implements the functions of the terminal device in the method embodiments. The chip of the terminal device receives a signal from other modules (such as a radio frequency module or an antenna) in the terminal device. The signal can be transmitted by a network device to the terminal device. Alternatively, the chip of the terminal device transmits a signal to other modules (such as a radio frequency module or an antenna) in the terminal device. The signal can be transmitted by the terminal device to a network device.

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

[0264] It can be understood that when the apparatus 700 is a terminal device or a network device, the communication interface 730 can be a transceiver, and specifically can include a transmitter and a receiver. The transmitter is configured to send a signal, and the receiver is configured to receive a signal. When the apparatus 700 is a chip applied to a terminal device or a network device, the communication interface 730 can be an input / output circuit. The input circuit can be configured to receive, and the output interface can be configured to send.

[0265] It should be noted that the method embodiments described above can be applied to a processor or implemented by the processor. The processor can be an integrated circuit chip having a signal processing capability. In the implementation process, each step of the method embodiments described above can be completed by integrated logic circuits or instructions in the form of software in the processor.

[0266] The processor described above can be a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or any combination thereof. The general processor can be a microprocessor, or any conventional processor.

[0267] The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as hardware code processing executed by a processor, or executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium in the art, such as a random access memory, a flash memory, a read only memory, a programmable read only memory or an electrically erasable programmable memory, a register, or the like. The storage medium is located in the memory, and the processor reads the information in the memory, and combines the hardware to complete the steps of the above method.

[0268] The memory in the embodiments of the present application can be a volatile memory or a nonvolatile memory, or can include both volatile and nonvolatile memory. Among them, the nonvolatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM) used as an external cache. By way of example, and not limitation, many forms of RAM can be used, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM). It is noted that the memory of the systems and methods described herein is intended to include, without being limited to, these and any other suitable types of memory.

[0269] The method provided by the above embodiments can be implemented by software, hardware, firmware, or any combination thereof, in whole or in part. When implemented by software, the method can be implemented in whole or in part in the form of a computer program product. The computer program product can include one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can 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 can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired (for example, coaxial cable, optical fiber, digital subscriber (DSL)) or wireless (for example, infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic disk), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)), etc.

[0270] The embodiments of the present application also provide a computer readable medium having a computer program stored thereon, which, when executed by a computer, implements the functions of the above method embodiments.

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

[0272] The embodiments of the present application also provide a communication system, which includes a terminal device and a network device.

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

[0274] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0275] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic. The division of the units is merely logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0276] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0277] In addition, each functional unit in the various embodiments of the present application can be integrated into a processing unit, or each unit can exist physically, or two or more units can be integrated into one unit.

[0278] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, and various media that can store program codes.

[0279] The above is merely specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method of channel estimation, characterized by, The method comprises: receiving a first signal; receiving first information, the first information being used to indicate a first channel estimation parameter, the first channel estimation parameter comprising at least one of a matrix norm of a first expression or an iteration step, the first expression being determined by a precoding matrix, the first signal, and a kth 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, T being a number of iterations of channel estimation; performing T times of channel estimation based on the first signal and the first channel estimation parameter to obtain a channel estimation result.

2. The method of claim 1, wherein, The first information comprises a first index, and a correspondence relationship is established between the first index and the first channel estimation parameter; or the first information comprises the first channel estimation parameter.

3. The method of claim 2, wherein, The correspondence relationship is predefined or indicated by a network side, and the correspondence relationship comprises a correspondence relationship between each of at least one channel estimation parameter and an index, the at least one channel estimation parameter comprising 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 kth channel estimation satisfies: The The fixed point operator designed based on the fixed point theory; wherein an output representing the kth channel estimate, an output representing the k-1th channel estimate, representing the first signal y and the second signal x The output obtained by performing the kth channel estimation is input.

5. The method of claim 4, wherein, The including a non-linear estimator, in the kth channel estimate: the input of the non-linear estimator is the output of the non-linear estimator is 6. The method of claim 4, wherein, The comprising a linear estimator and a nonlinear estimator, the kth channel estimate is obtained by: 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 by using a neural network model.

8. The method of claim 7, wherein, The method further comprises: obtaining parameters of the neural network model used for channel estimation.

9. The method of claim 8, wherein, The obtaining of the parameters of the neural network model comprises: receiving second information, the second information indicating the parameters of the neural network model; or training the neural network model to obtain the parameters of the neural network model.

10. The method according to any one of claims 6 to 9, characterized in that, The satisfies: where I is an identity matrix, g is a regularization term, denotes a subgradient of g, A is the precoding matrix, M is the matrix norm, and σ is the iteration step.

11. The method of claim 10, wherein, 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 a network device.

13. The method according to any one of claims 1 to 12, characterized in that, The T times channel estimations are realized by a channel estimator, and a mathematical model of the channel estimator is: Constraints: wherein, For the fixed point equation, for the fixed point equation, representing a channel estimate value; y is the first signal, h is a channel true value, represents a bond to averaging; P has the physical meaning of obtaining a function that minimizes the channel estimation error 14. A method of channel estimation, characterized by, The method comprises: sending a second signal; determining first information based on a precoding matrix, the first information being used to indicate a first channel estimation parameter, the first channel estimation parameter comprising at least one of a matrix norm of a first expression or an iteration step, the first expression being determined by the precoding matrix, the first signal, and a kth channel estimation value, k being a positive integer less than or equal to T, T being a positive integer, T being a number of iterations of channel estimation; sending the first information; The first signal is a signal obtained after the second signal passes through a wireless channel, and the first signal and the first channel estimation parameter are used to perform T times of channel estimation.

15. The method of claim 14, wherein, The first information comprises a first index, and a correspondence relationship is established between the first index and the first channel estimation parameter; or the first information comprises the first channel estimation parameter.

16. The method of claim 15, wherein, The method further comprises: sending third information, the third information being used to configure the correspondence relationship, the correspondence relationship comprising a correspondence relationship between each of at least one channel estimation parameter and an index, the at least one channel estimation parameter comprising the first channel estimation parameter.

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

18. The method of claim 17, wherein, The includes a non-linear estimator.

19. The method of claim 17, wherein, The comprises a linear estimator and a nonlinear estimator, an output of the linear estimator being an input of the nonlinear estimator.

20. The method of claim 18 or 19, wherein, The nonlinear estimator is implemented by using a neural network model.

21. The method of claim 20, wherein, The method further comprises: training the neural network model to obtain parameters of the neural network model, the neural network model being used for channel estimation; sending second information, the second information indicating the parameters of the neural network model.

22. The method of any one of claims 19-21, wherein, The Satisfies: where I is an identity matrix, g is a regularization term, denotes the subgradient of g, A is the precoding matrix, M is the matrix norm, and σ is the iteration step size.

23. The method of claim 22, wherein, The mathematical model of the linear estimator is: The mathematical model of the nonlinear estimator is 24. The method of any one of claims 14-23, wherein, The method further includes: sending fourth information, the fourth information indicating the value of T.

25. A communications device, characterized by comprising modules for implementing the method according to any one of claims 1 to 13; or comprising modules for implementing the method according to any one of claims 14 to 24.

26. A communications device, characterized by comprising a processor for causing the communication device to implement the method according to any one of claims 1 to 13, or to implement the method according to any one of claims 14 to 24, by executing a computer program and / or by a logic circuit.

27. The apparatus of claim 26, wherein, further comprising a memory for storing the computer program and / or a configuration file of the logic circuit.

28. The apparatus of claim 26 or 27, wherein, further comprising a communication interface for inputting and / or outputting signals.

29. A computer-readable storage medium having stored thereon a computer program, wherein The computer program is executed by the processor, and 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, characterised in that, The computer program is executed by the processor, and 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 by comprising a terminal device for implementing the method according to any one of claims 1 to 13, and a network device for implementing the method according to any one of claims 14 to 24.

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