Channel compensation processing method, communication device, and computer program product

Abstract

The application discloses a kind of channel compensation processing method, communication device and computer program product.It relates to network communication technical field, the method includes: obtaining the target moving speed of relative motion between first device and second device, multiple channel estimation results of channel between first device and second device;Based on multiple channel estimation results, the channel state information of channel is compensated using interpolation method, and target intermediate compensation result is obtained;With target moving speed and target intermediate compensation result as input, the channel state information of channel is compensated using channel state information prediction model, and target channel state information compensation result is obtained.The application solves the technical problems that in the related art, when channel compensation is carried out, the channel in static or slow moving environment is considered, in the case where terminal moving speed is relatively fast, channel has strong time-varying, channel reciprocity is poor, and channel compensation accuracy is low.

Description

Technical Field

[0001] This invention relates to the field of network communication technology, and more specifically, to a channel compensation processing method, communication equipment, and computer program product. Background Technology

[0002] In some communication scenarios, such as wireless transmission security in vehicle-to-everything (V2X) networks, transmission delays between the communicating parties cause time slot discrepancies in channel state information, necessitating compensation for these discrepancies. For instance, to enhance wireless transmission security in V2X networks, V2X wireless channel key generation is typically required. This generation leverages the randomness, time-varying nature, and transient reciprocity of the wireless channel between vehicle terminals to measure shared channel characteristics as a random source for key generation.

[0003] Research on wireless key generation systems in related technologies primarily considers channels in static or slowly moving environments. However, in scenarios where terminals move at high speeds, such as in vehicle-to-everything (V2X) environments, channels exhibit strong time-varying characteristics and poor channel reciprocity, leading to a high rate of key inconsistency between the initial keys of communicating parties. Furthermore, since wireless channels in mobile environments often do not follow linear changes, linear compensation methods for channel state information in related technologies often fail to meet key consistency requirements, resulting in poor channel reciprocity and low compensation accuracy, thereby reducing the performance and efficiency of encrypted communication.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a channel compensation processing method, communication device, and computer program product to at least solve the technical problems in related technologies where channel compensation is mainly considered in static or slow-moving environments. When the terminal moves at a high speed, the channel has strong time-varying characteristics, poor channel reciprocity, and low channel compensation accuracy.

[0006] According to one aspect of the present invention, a channel compensation processing method is provided, comprising: acquiring a target moving speed of relative motion between a first device and a second device, and multiple channel estimation results of the channel between the first device and the second device; compensating the channel state information of the channel by interpolation based on the multiple channel estimation results to obtain a target intermediate compensation result; and compensating the channel state information of the channel by a channel state information prediction model using the target moving speed and the target intermediate compensation result as input to obtain a target channel state information compensation result of the channel, wherein the channel state information prediction model is obtained by machine learning based on multiple moving speeds and the channel state information compensation results between the communicating parties obtained under the multiple moving speeds.

[0007] According to another aspect of the present invention, a communication device is also provided, comprising: a memory and a processor, the memory storing a computer program; the processor being configured to execute the computer program stored in the memory, wherein the computer program, when executed, causes the processor to perform any one of the channel compensation processing methods described above.

[0008] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of any one of the channel compensation processing methods.

[0009] In this embodiment of the invention, the target moving speed of the relative motion between the first device and the second device, and multiple channel estimation results of the channel between the first device and the second device are obtained. Based on the multiple channel estimation results, the channel state information of the channel is compensated by interpolation to obtain a target intermediate compensation result. Using the target moving speed and the target intermediate compensation result as input, a channel state information prediction model is used to compensate the channel state information of the channel to obtain a target channel state information compensation result. The channel state information prediction model is based on multiple moving speeds and communication data obtained at the multiple moving speeds. The channel state information compensation result between the two parties is obtained through machine learning. It takes into account the time-varying characteristics of the channel between the two parties, introduces the relative movement speed between the two parties into the input parameters of the channel state information prediction model, and uses the channel state information prediction model to accurately compensate the channel state based on the relative movement speed. This achieves the technical effect of improving the channel reciprocity and the accuracy of channel compensation between the two parties. It also solves the technical problem that related technologies mostly consider the channel in static or slow-moving environments when performing channel compensation. When the terminal moves at a fast speed, the channel has strong time-varying characteristics, poor channel reciprocity, and low channel compensation accuracy. Attached Figure Description

[0010] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0011] Figure 1 This is a flowchart of a channel compensation processing method according to an embodiment of the present invention;

[0012] Figure 2 This is a schematic diagram of an optional channel detection between two communicating parties according to an embodiment of the present invention;

[0013] Figure 3 This is a flowchart of an optional training phase according to an embodiment of the present invention;

[0014] Figure 4 This is a schematic diagram of an optional channel state information compensation process and result according to an embodiment of the present invention;

[0015] Figure 5 This is a schematic diagram of the model training process according to an embodiment of the present invention;

[0016] Figure 6 This is a flowchart of an optional prediction stage according to an embodiment of the present invention;

[0017] Figure 7 This is a schematic diagram of a channel compensation processing device according to an embodiment of the present invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0019] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] First, to facilitate understanding of the embodiments of the present invention, some terms or nouns involved in the present invention will be explained below:

[0021] A time slot, in the context of communication, refers to a time interval or period set to distinguish between channel probing and channel estimation operations within different time periods or cycles. Simply put, a time slot is a specific segment or window of time used to identify and execute a particular communication task.

[0022] According to an embodiment of the present invention, a method embodiment for channel compensation processing is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0023] Figure 1 This is a flowchart of a channel compensation processing method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0024] Step S102: Obtain the target moving speed relative to the first device and the second device, as well as multiple channel estimation results of the channel between the first device and the second device;

[0025] Optionally, the executing entity of steps S102 to S106 can be a first device, a second device, or other communication devices capable of data interaction with the first device and / or the second device. The first device and the second device can be two communicating parties in the vehicle network, that is, two entities participating in communication in the vehicle network. The first device and the second device can be, but are not limited to, two vehicles, a vehicle and a roadside device, a vehicle and a pedestrian (i.e., a terminal device carried by a pedestrian), etc. The roadside device can be, but is not limited to, a roadside unit (RSU).

[0026] Optionally, multiple channel estimation results between the first device and the second device can be obtained by performing multiple channel probes between the first device and the second device during their relative movement at the target moving speed. Specifically, multiple channel probe signals transmitted during the multiple channel probes between the first device and the second device are acquired, and channel estimation is performed on the channel between the first device and the second device based on the obtained multiple probe signals to obtain multiple channel estimation results between the first device and the second device. These multiple probe signals can be transmitted from the first device to the second device, or from the second device to the first device.

[0027] Step S104: Based on multiple channel estimation results, interpolation is used to compensate for the channel state information to obtain the target intermediate compensation result.

[0028] Optionally, the multiple channel estimation results can be obtained based on multiple channel probe signals received by the first device or multiple channel probe signals received by the second device. During the acquisition of the target intermediate compensation result, channel compensation can be performed using interpolation based on the multiple channel estimation results corresponding to the first and second devices respectively. In the above method, either of the communicating parties (such as the first device or the second device) can perform preliminary compensation of channel state information based on its own multiple channel estimation results. Taking Alice as the first device and Bob as the second device as an example, multiple channel probes between Alice and Bob can obtain several channel estimation results {H′} corresponding to Alice. a}, and we can also obtain several channel estimation results {H′} corresponding to the second device Bob. b}

[0029] Optionally, the intermediate compensation result can be in matrix form. For example, the intermediate compensation result can be a sensing matrix obtained by interpolation based on multiple channel estimation results. The intermediate compensation result is in time-domain matrix form, that is, the multiple channel state information included in the intermediate compensation result are arranged in chronological order. The rows of the matrix can represent the elements included in each channel state information, and the columns can represent the arrangement order of the multiple channel state information.

[0030] Optionally, multiple channel probes are performed between Alice and Bob to obtain several corresponding channel estimation results {H′}. a} and {H′ b After that; the first device Alice can be based on {H′ a The channel state information of the channel is compensated by interpolation to obtain the sensing matrix χ. Alice′As the corresponding target intermediate compensation result; the second device Bob can also be based on {H′ b The channel state information of the channel is compensated by interpolation to obtain the sensing matrix χ. Bob′ This serves as the intermediate compensation result for the corresponding target.

[0031] In one optional embodiment, when the target intermediate compensation result is in matrix form, the channel state information of the channel is compensated by interpolation based on multiple channel estimation results to obtain the target intermediate compensation result, including: determining the dimension of the target intermediate compensation result; and compensating the multiple channel estimation results by interpolation based on the dimension of the target intermediate compensation result to obtain the target intermediate compensation result.

[0032] Optionally, the target intermediate compensation result can be in matrix form. For example, it can be understood as a sensing matrix, where each row represents a channel state information entry, and the total number of columns represents the total number of channel state information entries included in the target intermediate compensation result. When multiple channel estimation results are obtained based on multiple channel probe signals received by the first device, the corresponding target intermediate compensation result can be represented as the sensing matrix χ corresponding to the first device. Alice′ When multiple channel estimation results are obtained based on multiple channel probe signals received by the second device, the corresponding intermediate compensation result for the target can be characterized as the sensing matrix χ corresponding to the second device. Bob′ .

[0033] It is understandable that when using interpolation to determine the target intermediate compensation result, the dimension of the target intermediate compensation result can be predetermined. This dimension can reflect the total number of channel state information entries included in the target intermediate compensation result, and can further determine the time slot range corresponding to the channel state information included in the target intermediate compensation result. Therefore, when the dimension of the target intermediate compensation result is known, it is possible to further determine at which specific time slots channel compensation needs to be performed, thereby obtaining the target intermediate compensation result.

[0034] In one optional embodiment, determining the dimension of the target intermediate compensation result includes: obtaining time interval information of multiple channel probes performed between the first device and the second device when they move relative to each other at the target moving speed; and determining the dimension of the target intermediate compensation result based on the time interval information.

[0035] Optionally, during channel probing between the two communicating parties (such as the first device and the second device), there may be a time delay, leading to inconsistencies in the time slots of the channel state information between the two parties. Therefore, when determining the corresponding dimension of the target channel state information, the time interval information of multiple channel probing operations between the first device and the second device can be taken into account. Based on the time interval information between the two, the dimension of the target intermediate compensation result can be determined in a targeted manner, thereby reducing the inconsistency in the time slots of the channel state information caused by the channel probing delay between the two communicating parties.

[0036] Optionally, the time interval information includes the delay information of channel detection between the two communicating parties, and the time interval between two adjacent channel detections in multiple channel detections. When the two communicating parties conduct channel detection, the number and time interval of the channel detection signals sent by each party to the other can be the same. For example, the first device sends multiple channel detection signals to the second device at time intervals, and the second device also sends multiple channel detection signals to the first device at time intervals. The delay information of channel detection between the two communicating parties can be understood as the time interval between the corresponding channel detection signals sent by the two communicating parties, for example, the time interval between the first channel detection signals sent by the first device and the second device respectively. The time interval between multiple channel detections can be understood as the time interval between any two adjacent channel detection signals among the multiple channel detection signals sent by either party (such as the first device or the second device). Figure 2 This is a schematic diagram illustrating an optional channel detection process between two communicating parties according to an embodiment of the present invention, such as... Figure 2 As shown, the two communicating parties include a first device Alice and a second device Bob. Alice and Bob perform three channel probes. Bob sends three channel probe signals H{a1}, H{a2}, and H{a3} to Alice, and Alice sends three channel probe signals H{b1}, H{b2}, and H{b3} to Bob. The time interval t1 between H{a1} and H{b1} can be used as the time delay information for channel probes between the two communicating parties, and the time interval t2 between H{a1} and H{a2} can be used as the time interval between two adjacent channel probes in the multiple channel probes.

[0037] It should be noted that the key to channel state compensation based on interpolation prediction lies in ensuring that the interpolated channel state information of both communicating parties has overlapping time slot intervals. Given t1, t2, and t3, the lead-lag relationship between the channel detection times of both communicating parties can be determined. Based on this relationship, the dimension of the target intermediate compensation result can be determined. Here, t3 is obtained based on t1 and t2 and represents the total time range covered by the interpolated channel state information (i.e., the channel state information compensation result). For example, to ensure that the channel state information of both communicating parties has overlapping time slot intervals, the sensing matrix χ corresponding to the first device can be set. Alice′ The dimension and the perception matrix χ corresponding to the second device Bob′ All dimensions are set as follows Figure 2 The dimensions corresponding to the t3 time period shown above are only examples of the dimensions of the intermediate compensation results of the target. The acquisition range of the dimensions can be set according to actual needs, provided that the channel state information of the two communicating parties can have overlapping time slot intervals.

[0038] Step S106: Using the target moving speed and the target intermediate compensation result as input, the channel state information is compensated by the channel state information prediction model to obtain the target channel state information compensation result. The channel state information prediction model is obtained through machine learning based on multiple moving speeds and the channel state information compensation results between the two communicating parties under multiple moving speeds.

[0039] Optionally, the channel state information prediction model pre-learns the relationship between the relative movement speeds of the two communicating parties and the channel state information compensation results. Given that the relative movement speeds (e.g., target movement speeds) between the first and second devices are known, the target intermediate compensation results between the first and second devices can be optimized based on this channel state information prediction model. The resulting target channel state information compensation result is a channel state information compensation result that matches the current relative movement speeds between the two parties.

[0040] It should be noted that during channel probing between the two communicating parties, there is a certain time delay between their channel estimations. Different relative movement speeds will lead to different trends in the differences between their channel state information. Introducing the relative movement speed between the two parties into the input parameters of the channel state information prediction model allows it to learn the relationship between corresponding elements in the target intermediate compensation result and the target channel state information compensation result at different movement speeds. Based on this, the channel state information compensation result takes into account the time-varying nature of the channel between the two communicating parties, thereby improving the reciprocity of the channel between the two parties when the terminal moves at a high speed, and thus improving the accuracy of the channel state information compensation between the two communicating parties.

[0041] Optionally, the target channel state information compensation result can be in matrix form. For example, the target channel state information compensation result can be used as a prediction result matrix, and the dimension of the target channel state information compensation result is the same as the dimension of the initial channel state information compensation result.

[0042] In an optional embodiment, before obtaining the target channel state information compensation result of the channel by using the target moving speed and the target intermediate compensation result as input and employing the channel state information prediction model, the method further includes: obtaining channel estimation results corresponding to multiple moving speeds respectively; based on the channel estimation results corresponding to multiple moving speeds respectively, compensating the channel state information of the channel between the two communicating parties by interpolation, obtaining historical intermediate compensation results corresponding to multiple moving speeds, and historical channel state information compensation results corresponding to multiple moving speeds respectively; and training an initial neural network model based on multiple moving speeds, historical intermediate compensation results corresponding to multiple moving speeds, and historical channel state information compensation results corresponding to multiple moving speeds respectively, to obtain a channel state information prediction model.

[0043] Optionally, before executing step S104, a training phase is also included. This phase is used to train the channel state information prediction model. The initial neural network model can be trained based on multiple relative movement speeds of the two communicating parties, as well as the historical intermediate compensation results and historical channel state information compensation results corresponding to the two communicating parties at the multiple movement speeds. The resulting channel state information prediction model learns the correspondence between the movement speeds of the two communicating parties and the channel state information compensation results. This channel state information prediction model can predict the channel state information compensation results that match the movement speeds of the two communicating parties.

[0044] Optionally, the two communicating parties can be a first device and a second device. When the two communicating parties move relative to each other at any speed, the first device Alice and the second device Bob perform multiple channel probes and channel estimations in different time slots. During the multiple channel probes, each communicating party sends multiple probe signals to the other end and performs channel estimation based on the multiple probe signals received by each party, thus obtaining multiple channel estimation results corresponding to each party, namely, multiple channel estimation results corresponding to Alice and multiple channel estimation results corresponding to Bob. Based on the multiple channel estimation results corresponding to each party, channel compensation is performed by interpolation to obtain the historical channel state information compensation result corresponding to any speed.

[0045] Optionally, the purpose of channel state compensation is to ensure that the channel state information of the two communicating parties remains consistent in time slot. In the process of channel state information compensation based on the channel estimation results of each of the two communicating parties, the channel state information compensation between the two communicating parties can be unidirectional or bidirectional. That is, the channel compensation process can be performed by only one of the first device Alice and the second device Bob, or the channel compensation process can be performed by both the first device Alice and the second device Bob at the same time.

[0046] Optionally, when the channel state information compensation between the two communicating parties is unidirectional, the channel state information prediction model can be constructed for only one of the two communicating parties; when the channel state information compensation between the two communicating parties is bidirectional, the channel state information prediction model can be constructed for each of the two communicating parties, that is, the channel state information prediction model can be constructed for the first device and the second device respectively.

[0047] In an optional embodiment, when the channel estimation result includes a first channel estimation result and a second channel estimation result, based on the channel estimation results corresponding to multiple movement speeds, interpolation is used to compensate the channel state information of the channel between the two communicating parties to obtain historical intermediate compensation results corresponding to multiple movement speeds and historical channel state information compensation results corresponding to multiple movement speeds. This includes: based on the first channel estimation results corresponding to multiple movement speeds, interpolation is used to compensate the channel state information of the channel between the two communicating parties to obtain historical intermediate compensation results corresponding to multiple movement speeds, wherein the first channel estimation result is a channel estimation result obtained based on the channel probe signal received by one of the two communicating parties, and the historical intermediate compensation result is an intermediate compensation result of one party at the corresponding movement speed; based on the first channel estimation results corresponding to multiple movement speeds and the second channel estimation results corresponding to multiple movement speeds, interpolation is used to compensate the channel state information of the channel between the two communicating parties to obtain historical channel state information compensation results corresponding to multiple movement speeds, wherein the second channel estimation result is a channel estimation result obtained based on the channel probe signal received by the other party of the two communicating parties, and the historical channel state information compensation result is a channel state information compensation result of one party at the corresponding movement speed.

[0048] Optionally, any of the multiple movement speeds may correspond to both historical intermediate compensation results and historical channel state information compensation results. The historical intermediate compensation results serve as input to the initial neural network model training, while the historical channel state information compensation results serve as the output of the initial neural network model. The historical intermediate compensation results, as model input, can be obtained based on the channel estimation results of one of the communicating parties (such as the first device or the second device); the historical channel state information compensation results, as model output, can be obtained based on the channel estimation results of both communicating parties (i.e., the first device and the second device) respectively.

[0049] Optionally, the channel state information prediction model can be constructed for one or more of the first and second devices. The historical channel state information compensation results required during the training phase differ depending on the target device for the channel state information prediction model. Based on the channel estimation result of one of the communicating parties, the historical intermediate compensation result constructed using interpolation is the historical intermediate compensation result corresponding to that party, and the corresponding historical channel state information compensation result is also the historical channel state information compensation result corresponding to that party. For example, when constructing the channel state information prediction model for the first device in the communication process, the historical intermediate compensation result is obtained based on the channel estimation result corresponding to the first device at any moving speed; the historical channel state information compensation result is obtained based on the channel estimation results corresponding to both the first and second devices at that any moving speed. When constructing the channel state information prediction model for the second device in the communication process, the historical intermediate compensation result is obtained based on the channel estimation result corresponding to the second device at any moving speed; the historical channel state information compensation result is obtained by interpolation based on the channel estimation results corresponding to both the first and second devices at that any moving speed.

[0050] In one optional embodiment, the historical intermediate compensation results and the corresponding historical channel state information compensation results for the multiple moving speeds are all in matrix form, and the dimensions of the historical intermediate compensation results and the corresponding historical channel state information compensation results for the multiple moving speeds are the same, wherein the dimension includes the number of matrix rows and the number of matrix columns, the number of matrix rows represents the total number of subcarriers, and the number of matrix columns represents the total number of channel state information.

[0051] Optionally, the historical intermediate compensation result is obtained based on multiple first channel estimation results at the corresponding movement speed. Both the historical intermediate compensation result and the historical channel state information compensation result can be in matrix form. The historical intermediate compensation result can be understood as a perception matrix constructed using interpolation based on the channel estimation result of one of the communicating parties; the historical channel state information compensation result can be understood as an inference matrix constructed using interpolation based on the corresponding channel estimation results of each of the communicating parties. Furthermore, the historical intermediate compensation result and the historical channel state information compensation result have the same dimensions and interpolation methods. The difference lies in that the historical intermediate compensation result is obtained only based on the channel estimation result corresponding to one of the communicating parties, while the historical channel state information compensation result is obtained based on the channel estimation results corresponding to each of the communicating parties.

[0052] For example, when constructing a channel state information prediction model for the first device in a communication relationship, it can be based on multiple first channel estimation results {H} obtained by the first device at any moving speed. a The perceptual matrix χ is constructed based on interpolation.Alice As the historical intermediate compensation result corresponding to the first device; based on the multiple first channel estimation results {H} obtained by the first device at any moving speed. a}, and multiple first channel estimation results {H} obtained by the second device at any moving speed. b The inference matrix ψ is constructed using interpolation. Alice This serves as the compensation result for the historical channel state information corresponding to the first device. When constructing the channel state information prediction model for the second device in both communication parties, the multiple second channel estimation results {H} obtained by the second device at any moving speed are used. b The perceptual matrix χ is constructed based on interpolation. Bob As the historical intermediate compensation result corresponding to the second device; based on multiple first channel estimation results {H} obtained by the first device at any moving speed. a}, and multiple first channel estimation results {H} obtained by the second device at any moving speed. b The inference matrix ψ is constructed using interpolation. Bob This serves as the compensation result for the historical channel state information corresponding to the second device.

[0053] Optionally, before obtaining the historical intermediate compensation results and historical channel state information compensation results corresponding to multiple movement speeds, the corresponding dimensions of the historical intermediate compensation results and historical channel state information compensation results at the corresponding movement speeds can be determined based on the time interval information of the multiple channel probes performed by the communicating parties at the corresponding movement speeds. Taking the example of the first device and the second device performing multiple channel probes at any movement speed, the first device Alice and the second device Bob (hereinafter referred to as Alice and Bob) transmit Physically Shared Channel (PSSCH) probe signals to each other in different time slots and receive PSSCH probe signals sent by the other party. Figure 4 This is a schematic diagram illustrating an optional channel state information compensation process and result according to an embodiment of the present invention, as shown below. Figure 4 As shown, where Figure 4 (a) and (e) represent the channel estimation results {H} of the PSSCH subframes received by Alice and Bob, respectively. a} and {H b In the PSSCH subframe, only the demodulation reference signal (DMRS) in slots 2, 5, 8, and 11 can be used for channel estimation to generate CSI. Figure 4 The CSIs in the table are arranged chronologically from left to right. It can be seen that the current {H}... a} and {H b There are no overlapping time slot intervals between them, and the first column of the PSSCH subframe received by Alice differs from the first column of the first column of the PSSCH subframe received by Bob by 16 time slots (i.e., the channel sounding delay information between Alice and Bob). The time interval between Alice and Bob receiving two adjacent CSIs is 3 time slot symbols. To ensure that the interpolated channel state information of the two communicating parties has overlapping time slot intervals, a prediction matrix ψ can be set. Alice and the prediction matrix ψ Bob Both have a dimension (or number of columns) of 24. Alice inserts 10 elements B1 to B10 after the 13th time slot symbol in the corresponding received PSSCH subframe, and Bob inserts 10 elements F1 to F10 before the 0th time slot symbol in the corresponding received PSSCH subframe. After interpolation, the elements at positions B6 and B9 on Alice's side are the same as the corresponding symbol time slots of CSIs at positions 2 and 5 on Bob's side, respectively. The elements at positions F9 and F6 on Bob's side are the same as the corresponding symbol time slots of CSIs at positions 8 and 11 on Alice's side. Therefore, the CSIs at positions 2 and 5 on Bob's side can be inserted into the corresponding positions at positions B6 and B9 on Alice's side, respectively, to obtain the inference matrix ψ. Alice Insert the CSIs at positions 8 and 11 on Alice's side into the F9 and F6 positions on Bob's side, respectively, to obtain the prediction matrix ψ. Alice .

[0054] Correspondingly, the perception matrix χ is also set. Alice and perception matrix χ Bob The dimensions (or column count) of each matrix are 24. Alice inserts 10 elements after the 13th time slot symbol in the corresponding received PSSCH subframe to obtain the perception matrix χ. Alice Bob inserts 10 elements before the 0th time slot symbol in the corresponding received PSSCH subframe to obtain the sensing matrix χ. Bob Each element corresponds to a time slot symbol.

[0055] Furthermore, it can be based on {H a} and {H b The channel state information compensation result is determined. This is done to satisfy the input-output relationship during model training and to ensure the channel state information compensation result (prediction matrix ψ) obtained after compensation. Alice and the prediction matrix ψ Bob There are overlapping time slot intervals. The dimensions of the historical intermediate compensation result and the corresponding historical channel state information compensation result for the same device are completely identical, i.e., the inference matrix ψ is set. Alice With the perception matrix χAlice The dimensions are exactly the same, and the inferred matrix ψ Bob With the perception matrix χ Bob The dimensions are exactly the same (e.g.) Figure 4 (As shown). The above method makes it easier for the channel state information prediction model to learn the relationship between the channel state information compensation results before and after optimization (i.e., the intermediate compensation result and the channel state information compensation result).

[0056] It should be noted that when determining the compensation results for historical channel state information corresponding to multiple movement speeds, in addition to the interpolation method described above, other methods can also be used. For example, the nonlinear interpolation coefficients corresponding to each movement speed can be determined based on the correspondence between movement speed and nonlinear interpolation coefficients; based on the channel estimation results {H} corresponding to Alice... a The channel state information of Alice at each moving speed is compensated using the nonlinear interpolation coefficients corresponding to each moving speed, resulting in the historical channel state information compensation results for Alice at each moving speed; based on the channel estimation results {H} corresponding to Bob... b The channel state information of the channel is compensated by nonlinear interpolation coefficients corresponding to each moving speed, and the historical channel state information compensation results of Bob at each moving speed are obtained, etc., without specific limitations here.

[0057] In one optional embodiment, an initial neural network model is trained based on multiple movement speeds and historical channel state information compensation results corresponding to the multiple movement speeds to obtain a channel state information prediction model. This includes: using multiple movement speeds and historical intermediate compensation results corresponding to the multiple movement speeds as inputs and historical channel state information compensation results corresponding to the multiple movement speeds as outputs to train the initial neural network model to obtain a channel state information prediction model.

[0058] Optionally, the historical intermediate compensation result is constructed based solely on the channel estimation result of one of the communicating parties at any given movement speed; this can be understood as the initially constructed channel state information compensation result. The historical channel state information compensation result is obtained by considering the channel estimation results of both communicating parties at any given movement speed; this can be understood as the optimized channel state information compensation result. Using the movement speed and the historical intermediate compensation result as model inputs, and the historical channel state information compensation result as model outputs, the initial neural network model is trained. The trained channel state information prediction model can learn the relationship between the corresponding elements of the initially constructed intermediate compensation result and the channel state information compensation result at different movement speeds.

[0059] Optionally, when constructing the channel state information prediction model for the first device in the communication process, the relationship between the moving speed, the historical intermediate compensation result, and the historical channel state information compensation result can be expressed in the following form:

[0060] M Alice (χ Alice ,V)=ψ Alice

[0061] Among them, M Alice This represents the channel state information prediction model constructed for the first device, Alice; V represents any one of multiple movement speeds; χ Alice This represents the channel estimation result {H} for the first device at any given moving speed. a The historical intermediate compensation result for the first device is obtained by interpolation; ψ Alice This represents the channel estimation results ({H) for the first and second devices respectively, based on any given moving speed. a} and {H b The historical channel state information compensation result corresponding to the first device is obtained by interpolation.

[0062] Optionally, when constructing the channel state information prediction model for the second device in the communication parties, the relationship between the moving speed, the historical intermediate compensation result, and the historical channel state information compensation result can be expressed in the following form:

[0063] M Bob (χ Bob ,V)=ψ Bob

[0064] Among them, M Bob This represents the channel state information prediction model built for the second device Bob; V represents any one of multiple movement speeds; χ Bob This represents the channel estimation result {H} for the second device at any given moving speed. b The historical intermediate compensation result for the first device is obtained by interpolation; ψ Bob This represents the channel estimation results ({H) for the first and second devices respectively, based on any given moving speed. a} and {H b The historical channel state information compensation result corresponding to the second device is obtained by interpolation.

[0065] It should be noted that, due to the different channel estimation results {H} of the two communicating parties, a} and {H b There is a certain time delay between {H}, and different movement speeds V will result in {H} being affected.a} and {H b The differences between them show different trends. Introducing velocity V into the input parameters of the channel state information prediction model allows it to learn the relationship between the corresponding elements of the intermediate compensation result and the channel state information compensation result under different velocities V, thereby improving the accuracy of channel state information compensation. This compensation method is different from the channel state information compensation methods in related technologies. It not only completes the channel state information compensation for the differences in the fingerprints of the two communicating devices, but also completes the prediction of short-term channel changes. It can effectively compensate for the uplink and downlink delays of channel detection.

[0066] It should be noted that the dimensions and interpolation methods corresponding to the target intermediate compensation results of the channel between the first device and the second device obtained in step S104 are consistent with the historical intermediate compensation results, and the structures of the obtained historical intermediate compensation results are the same as those of the historical channel state information compensation results. Therefore, given that the target intermediate compensation results and the corresponding target moving speed are known, the target channel state information compensation results obtained based on this channel state information prediction model are the optimized channel state information compensation results between the first device and the second device at the target moving speed.

[0067] In one alternative embodiment, the channel estimation result corresponding to each of the plurality of movement speeds is obtained based on multiple acquisitions of the channel estimation results between the communicating parties when they move relative to each other at each movement speed.

[0068] Optionally, to avoid randomness in the process of establishing the optimal interpolation coefficients under different movement speeds, channel detection and channel estimation can be repeated multiple times in the experimental scenario with the same movement speed to obtain multiple channel estimation results for the first device Alice at the corresponding movement speed, and multiple channel estimation results for the second device Bob at the corresponding movement speed; based on the obtained multiple channel estimation results, channel compensation processing is performed between the two communicating parties.

[0069] In one optional embodiment, when compensating for the channel state information of one of the first and second devices, the target moving speed and the target intermediate compensation result are used as inputs, and a channel state information prediction model is used to compensate for the channel state information to obtain the target channel state information compensation result. This includes: obtaining a third channel state information compensation result based on the target moving speed and the target intermediate compensation result using the channel state information prediction model; acquiring multiple third channel estimation results obtained by the other device based on multiple received channel probe signals; determining the channel state information with the same time slot among the third channel state information compensation result and the multiple third channel estimation results; and obtaining the target channel state information compensation result based on the channel state information with the same time slot.

[0070] Optionally, when the channel state information compensation between the two communicating parties is unidirectional, the channel state information prediction model can be constructed only for one of the communicating parties. That is, a corresponding channel state information prediction model can be constructed for the first device and the second device respectively. Specifically, for one of the communicating parties (i.e., the first device and the second device), the target moving speed and the target intermediate compensation result corresponding to one party can be used as inputs. The channel state information prediction model corresponding to one party is used to obtain the third channel state information compensation result corresponding to that party. For the other device, channel estimation can be performed based on multiple received channel probe signals to obtain multiple third channel estimation results. Channel state information with the same time slot in the third channel state information compensation result and multiple third channel estimation results are selected. One or more pairs of channel state information with the same time slot are selected from the channel state information with the same time slot as the target channel state information compensation result.

[0071] It should be noted that, based on the physical characteristics of short-term reciprocity of wireless channels, the shorter the interval between uplink and downlink channels, the higher the reciprocity. Since there are several overlapping time slots between the historical channel state information compensation result (third channel state information compensation result) of one device and the channel estimation result (i.e., multiple third channel estimation results) of the other device, the first device and the second device can select the channel state information under the same time slot in the channel state information as the final channel state information result between the first device and the second device, which has higher reciprocity.

[0072] In one optional embodiment, when both the first device and the second device compensate for the channel state information of the channel, the channel state information is compensated using a channel state information prediction model based on the target moving speed and the target intermediate compensation result to obtain the target channel state information compensation result. This includes: obtaining a fourth channel state information compensation result based on the target moving speed and the target intermediate compensation result corresponding to the first device, using the channel state information prediction model corresponding to the first device; obtaining a fifth channel state information compensation result based on the target moving speed and the target intermediate compensation result corresponding to the second device, using the channel state information prediction model corresponding to the second device; determining the predicted channel state information with the same corresponding time slot based on the fourth and fifth channel state information compensation results; and obtaining the target channel state information compensation result based on the predicted channel state information with the same time slot.

[0073] Optionally, when the channel state information compensation between the two communicating parties is bidirectional, channel state information prediction models can be constructed separately for each party, i.e., separate channel state information prediction models can be constructed for the first device and the second device. During channel state information compensation, each party bases its channel estimation results at the target moving speed (i.e., the channel estimation result {H′ of the first device at the target moving speed) on its own channel estimation results. a}, and the channel estimation results {H′} of the second device at the target moving speed. b The channel compensation process is performed using interpolation to obtain the target intermediate compensation results for each of the two communicating parties. The target intermediate compensation results and target movement speed for each of the two communicating parties are then input into the corresponding channel state information prediction model to obtain the optimized channel state information compensation results for each of the two communicating parties, namely the third channel state information compensation result and the fifth channel state information compensation result.

[0074] Optionally, when performing channel state information compensation based on the first device, the correspondence between the target moving speed, the initial channel state information, and the compensation result of the fourth channel state information can be expressed in the following form:

[0075] ψ Alice′ =M Alice (χ Alice′ ,V′)

[0076] Where, ψ Alice′ Indicates the compensation result of the fourth channel state information; M Alice χ represents the channel state information prediction model corresponding to the first device; Alice′ V' represents the intermediate compensation result of the target obtained by the first device based on the corresponding multiple channel estimation results; V' represents the target moving speed.

[0077] Optionally, when performing channel state information compensation based on the second device, the correspondence between the target moving speed, the initial channel state information, and the compensation result of the fifth channel state information can be expressed in the following form:

[0078] ψ Bob′ =M Bob (χ Bob′ ,V′)

[0079] Where, ψ Bob′ Indicates the compensation result of the fifth channel state information; M Bob χ represents the channel state information prediction model corresponding to the second device. Bob′ V' represents the intermediate compensation result of the target obtained by the second device based on the corresponding multiple channel estimation results; V′ represents the target moving speed.

[0080] It should be noted that, based on the physical characteristics of short-term reciprocity in wireless channels, the shorter the interval between uplink and downlink channels, the higher the reciprocity; the channel state information compensation result ψ Alice′ and ψ Bob′ It contains {H′ a} and {H′ b} Predicted channel state information between corresponding time slots; ψ Alice′ and ψ Bob′ There are several overlapping time slots between them. The first device and the second device select the predicted channel state information under the same time slot in the predicted channel state information as the final channel state information result between the first device and the second device, which has higher reciprocity.

[0081] Through the above steps S102 to S106, the time-varying characteristics of the channel between the two communicating parties can be considered. The relative movement speed between the two communicating parties can be introduced into the input parameters of the channel state information prediction model. Based on the relative movement speed, the channel state information prediction model is used to accurately compensate for the channel state. This achieves the technical effect of improving the channel reciprocity and channel compensation accuracy between the two communicating parties. It also solves the technical problem in related technologies that channel compensation is mostly considered in static or slow-moving environments. When the terminal moves at a fast speed, the channel has strong time-varying characteristics, poor channel reciprocity, and low channel compensation accuracy.

[0082] It should be noted that this embodiment is better suited for mobile scenarios, enabling the prediction of channel state information under short-term channel changes, thereby achieving reciprocity compensation of channel detection results between the communicating parties. It is suitable for vehicle-to-everything (V2X) environments where terminals move at high speeds. It can be used to generate wireless channel keys and improve the key generation rate and key generation consistency rate, thereby enhancing the performance and efficiency of encrypted communication.

[0083] Based on the above embodiments and optional embodiments, the present invention proposes an implementation method for an optional channel compensation processing method, which includes a training phase and a prediction phase, wherein...

[0084] Figure 3 This is a flowchart of an optional training phase according to an embodiment of the present invention, such as... Figure 3 As shown, the method includes the following steps:

[0085] S1, the two communicating parties, namely the first device Alice and the second device Bob (hereinafter referred to as Alice and Bob), transmit Physically Shared Channel (PSSCH) channel probe signals to each other in different time slots, and receive PSSCH channel probe signals sent by each other. Then, they perform channel estimation on the received PSSCH channel probe signals to obtain several channel estimation results {H}. a} and {H b}

[0086] In this embodiment, both Alice and Bob use a Universal Software Radio Peripheral (USRP) to synchronize with each other using the initial PSBCH signal transmitted by the USRP. This enables software-level control of rapid switching between transmit and receive modes in a Time Division Duplexing (TDD) communication system. By continuously transmitting and receiving channel sounding signals, the channel state information (CSI) required for secure communication between Alice and Bob is obtained. However, in actual implementation, this switching has a certain time delay; after multiple measurements, this delay in this embodiment is approximately 0.2 milliseconds, corresponding to a gap of about 3 symbols in the PSSCH subframe. This will affect the selection of interpolation positions and other information in the channel state information compensation results in subsequent steps.

[0087] S2, Alice according to {H a The perceptual matrix χ is constructed based on interpolation. Alice This serves as the historical intermediate compensation result corresponding to Alice; Bob, based on {H b The perceptual matrix χ is constructed based on interpolation. Bob This serves as the historical intermediate compensation result corresponding to Bob.

[0088] Figure 4 This is a schematic diagram illustrating an optional channel state information compensation process and result according to an embodiment of the present invention, as shown below. Figure 4 As shown, where Figure 4(a) and (e) represent the channel estimation results {H} of the PSSCH subframes received by Alice and Bob, respectively. a} and {H b In the PSSCH subframe, only the demodulation reference signals (DMRS) in slots 2, 5, 8, and 11 can be used for channel estimation to generate CSI.

[0089] In this embodiment, as Figure 4 As shown in (b), Alice is based on {H a The 2nd, 5th, 8th, and 11th symbols in the matrix constitute the CSI perception matrix χ. Alice ;χ Alice The values ​​in columns 2, 5, 8, and 11 are taken from {H} a The elements in columns 2, 5, 8, and 11 of the array are calculated using an interpolation function; the remaining elements are calculated using an interpolation function.

[0090] Correspondingly, such as Figure 4 As shown in (f), Bob is based on {H b The 2nd, 5th, 8th, and 11th symbols in the matrix constitute the CSI perception matrix χ. Bob ;χ Bob The values ​​in columns 2, 5, 8, and 11 are taken from {H} b The elements in columns 2, 5, 8, and 11 of the array are obtained by interpolation. The remaining elements are calculated using an interpolation function.

[0091] S3, Alice according to {H a} and {H b The inference matrix ψ is constructed based on interpolation. Alice This serves as the compensation result for Alice's corresponding historical channel state information; Bob, based on {H a} and {H b The inference matrix ψ is constructed based on interpolation. Bob This serves as the compensation result for Bob's corresponding historical channel state information.

[0092] In this embodiment, as Figure 4 As shown in (c), Alice is based on {H a The symbols 2, 5, 8, and 11 in} and {H b The second and fifth symbols in the} are used to interpolate and construct the CSI inference matrix ψ. Alice The prefix "B" indicates backward interpolation, and the specific interpolation method is as follows:

[0093] In the symbol time slot, ψ Alice Columns 0 to 13 correspond to {H a} columns 0 to 13, ψ AliceColumns B4 to B10 correspond to {H b} in columns 0 to 6, ψ Alice Columns B1 to B3 correspond to the three symbol slots that Alice and Bob actually exchange during the PSSCH subframe transmission and reception process; ψ Alice The values ​​in columns 2, 5, 8, and 11 are taken from {H} a In the 2nd, 5th, 8th, and 11th columns of}, ψ Alice The values ​​in columns B6 and B9 are taken from {H b} in columns 2 and 5, ψ Alice The remaining elements are calculated using an interpolation function, which may be, but is not limited to, a linear interpolation function. This linear interpolation function can estimate unknown channel state information based on known channel state information using a linear relationship, thereby filling in the gaps between data.

[0094] Correspondingly, such as Figure 4 As shown in (g), Bob is based on {H b The symbols 2, 5, 8, and 11 in} and {H a The second and fifth symbols in the} are used to interpolate and construct the CSI inference matrix ψ. Bob The prefix "F" indicates forward, and the specific interpolation method is as follows:

[0095] In the symbol time slot, ψ Bob Columns 0 to 13 correspond to {H b} columns 0 to 13, ψ Bob Columns F10 to F4 correspond to {H a} Columns 7-13, ψ Bob Columns F3 to F1 correspond to the three symbol slots that Alice and Bob actually exchange during the transmission and reception of PSSCH subframes; ψ Bob The values ​​in columns 2, 5, 8, and 11 are taken from {H} b In the 2nd, 5th, 8th, and 11th columns of}, ψ Bob The values ​​in columns F9 and F6 are taken from {H a} in columns 8 and 11, ψ Bob The remaining elements are calculated using an interpolation function.

[0096] In this embodiment, the perception matrix has the same dimension as the inference matrix. According to step S3 of this embodiment, the inference matrix has 24 columns; therefore, the perception matrix χ... Alice We need to interpolate columns B1 to B10 backwards, χ Bob Columns F10 to F1 need to be interpolated forward. At this time, the dimensions of the perception matrix and the inference matrix are the same, both with 24 columns; their element values ​​are calculated by the interpolation function.

[0097] S4, Alice constructs a CSI prediction network convolutional neural network as the initial neural network model, and uses χ Alice And the moving speed V as input, ψ Alice As output, repeat steps S11 to S13, and after multiple training iterations, obtain the trained CSI prediction convolutional neural network model M. Alice This serves as Alice's channel state information prediction model; similarly, on Bob's side, a CSI prediction network convolutional neural network is constructed as the initial neural network model, and χ... Bob And the moving speed V as input, ψ Bob As output, repeat steps S11 to S13, and after multiple training iterations, obtain the trained CSI prediction convolutional neural network model M. Bob This serves as the channel state information prediction model for Bob.

[0098] Figure 5 This is a schematic diagram of the model training process according to an embodiment of the present invention, such as... Figure 5 As shown in (a), during the model training phase, Alice and Bob respectively set the perceptual matrix χ of dimension (128, 24) to 128, 24, and 24, respectively. Alice , χ Bob And the movement speed V is used as input, and the prediction matrix ψ of dimension (128, 24) is used as input. Alice and ψ Bob As output, it is used to train the CSI prediction network convolutional neural network, where the matrix dimension (128, 24) indicates that the matrix contains interpolated CSI under 24 time slots, and each CSI element contains 128 subcarrier frequency points.

[0099] In this embodiment, the CSI prediction network convolutional neural network was trained multiple times in groups under different movements, so that M Alice and M Bob To obtain short-term prediction capability for CSI at different movement speeds.

[0100] Figure 6 This is a flowchart of an optional prediction stage according to an embodiment of the present invention, such as... Figure 6 As shown, it includes the following steps:

[0101] S21, the two communicating parties perform channel probing multiple times again, and obtain several channel estimation results {H′} respectively. a} and {H′ b}

[0102] S2, based on the obtained channel estimation results {H′ a} and {H′ b Using the same processing method as in step S12, Alice obtains the perception matrix χ. Alice′This serves as the intermediate compensation result for Alice's target; Bob obtains the perception matrix χ. Bob′ This serves as the intermediate compensation result for Bob's target.

[0103] S23, Alice senses the movement speed and will χ Alice′ Input M along with movement speed V′ Alice The output yields the prediction result matrix ψ. Alice′ This serves as the compensation result for Alice's fourth channel state information; similarly, Bob, after sensing the movement speed, will use χ... Bob′ Input M along with movement speed V′ Bob The output yields the prediction result matrix ψ. Bob′ This serves as the compensation result for the fifth channel state information corresponding to Bob.

[0104] In this embodiment, as Figure 5 As shown in (b), the prediction result matrix has the same dimension as the inference matrix in the training phase. Its matrix dimension (128, 24) indicates that the matrix contains the predicted CSI under 24 time slots, and each CSI element contains 128 subcarrier frequency points.

[0105] S24, Alice and Bob respectively from ψ Alice′ and ψ Bob′ One or more pairs of predicted CSIs are selected for key generation.

[0106] In this embodiment, as Figure 4 As shown in (d) and (h), ψ Alice′ Columns B1 to B3 and ψ Bob′ Columns F3 to F1 are located in the same symbol time slot; according to the physical characteristics of short-time reciprocity of wireless channels, the shorter the interval between uplink and downlink channels, the higher the reciprocity; in this embodiment, Alice selects ψ Alice′ CSI element ψ in column B2 Alice′ (B2), Bob chooses ψ Bob′ CSI element ψ in column F2 Bob′ (F2), as a prediction compensation CSI for high reciprocity, is used for symmetric key generation; similarly, ψ Alice′ (B1), ψ Bob′ (F3) and ψ Alice′ (B3), ψ Bob′ (F1) can also be used as a prediction compensation CSI for high reciprocity for symmetric key generation.

[0107] It should be noted that this embodiment is better suited for mobile scenarios, enabling the prediction of channel state information under short-term channel changes, thereby achieving reciprocity compensation of channel detection results between the communicating parties. It is suitable for vehicle-to-everything (V2X) environments where terminals move at high speeds. It can be used to generate wireless channel keys and improve the key generation rate and key generation consistency rate, thereby enhancing the performance and efficiency of encrypted communication.

[0108] This embodiment also provides a channel compensation processing apparatus for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the terms "module" and "apparatus" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0109] According to embodiments of the present invention, an apparatus embodiment for implementing the above-described channel compensation processing method is also provided. Figure 7 This is a schematic diagram of the structure of a channel compensation processing device according to an embodiment of the present invention, as shown below. Figure 7 As shown, the aforementioned channel compensation processing device includes: an acquisition module 700, a first compensation module 702, and a second compensation module 704, wherein:

[0110] The acquisition module 700 is used to acquire the target moving speed relative to the first device and the second device, as well as multiple channel estimation results of the channel between the first device and the second device;

[0111] The first compensation module 702, connected to the acquisition module 700, is used to compensate the channel state information of the channel by interpolation based on multiple channel estimation results, so as to obtain the target intermediate compensation result.

[0112] The second compensation module 704, connected to the first compensation module 702, is used to compensate the channel state information of the channel using the target moving speed and the target intermediate compensation result as input, and to obtain the target channel state information compensation result. The channel state information prediction model is obtained through machine learning based on multiple moving speeds and the channel state information compensation results between the two communicating parties obtained under multiple moving speeds.

[0113] In this embodiment of the invention, an acquisition module 700 is configured to acquire the target moving speed of the relative motion between the first device and the second device, as well as multiple channel estimation results of the channel between the first device and the second device; a first compensation module 702, connected to the acquisition module 700, is configured to compensate the channel state information of the channel based on the multiple channel estimation results using interpolation, to obtain a target intermediate compensation result; a second compensation module 704, connected to the first compensation module 702, is configured to use the target moving speed and the target intermediate compensation result as input, and use a channel state information prediction model to compensate the channel state information of the channel, to obtain a target channel state information compensation result, wherein the channel state information prediction model is based on multiple... The channel state information compensation results between the communicating parties at multiple movement speeds are obtained through machine learning. This achieves the goal of considering the time-varying characteristics of the channel between the communicating parties, incorporating the relative movement speed between the communicating parties into the input parameters of the channel state information prediction model, and using the channel state information prediction model to accurately compensate the channel state based on the relative movement speed. This achieves the technical effect of improving the channel reciprocity and channel compensation accuracy between the communicating parties. It also solves the technical problem in related technologies that channel compensation is mostly considered in static or slow-moving environments. When the terminal moves at a high speed, the channel has strong time-varying characteristics, poor channel reciprocity, and low channel compensation accuracy.

[0114] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0115] It should be noted that the acquisition module 700, the first compensation module 702, and the second compensation module 704 mentioned above correspond to steps S102 to S106 in the embodiments. The instances and application scenarios implemented by the above modules and their corresponding steps are the same, but they are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in a computer terminal.

[0116] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0117] The aforementioned channel compensation processing device may further include a processor and a memory. The aforementioned acquisition module 700, first compensation module 702, second compensation module 704, etc., are all stored in the memory as program modules, and the processor executes the aforementioned program modules stored in the memory to realize the corresponding functions.

[0118] The processor contains a core that retrieves the corresponding program modules from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0119] According to an embodiment of this application, a communication device is also provided, including: a memory and a processor, wherein the memory stores a computer program; the processor is configured to execute the computer program stored in the memory, wherein when the computer program is executed, the processor performs any of the aforementioned channel compensation processing methods.

[0120] According to an embodiment of this application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program runs, it controls the device where the non-volatile storage medium is located to execute any of the aforementioned channel compensation processing methods.

[0121] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals, and the non-volatile storage medium includes stored programs.

[0122] Optionally, during program execution, the device containing the non-volatile storage medium performs the following functions: acquiring the target moving speed of the relative motion between the first device and the second device, and multiple channel estimation results of the channel between the first device and the second device; based on the multiple channel estimation results, compensating the channel state information of the channel by interpolation to obtain the target intermediate compensation result; using the target moving speed and the target intermediate compensation result as input, compensating the channel state information of the channel by a channel state information prediction model to obtain the target channel state information compensation result, wherein the channel state information prediction model is obtained through machine learning based on multiple moving speeds and the channel state information compensation results between the two communicating parties obtained at multiple moving speeds.

[0123] According to an embodiment of this application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any of the channel compensation processing methods described above.

[0124] According to an embodiment of this application, an embodiment of a computer program product is also provided, which, when executed on a data processing device, is adapted to execute a program that initializes the channel compensation processing method steps described above.

[0125] Optionally, when the aforementioned computer program product is executed on a data processing device, it is suitable to execute a program with the following initialization steps: obtaining the target moving speed of the relative motion between the first device and the second device, and multiple channel estimation results of the channel between the first device and the second device; based on the multiple channel estimation results, compensating the channel state information of the channel by interpolation to obtain the target intermediate compensation result; using the target moving speed and the target intermediate compensation result as input, compensating the channel state information of the channel by a channel state information prediction model to obtain the target channel state information compensation result of the channel, wherein the channel state information prediction model is obtained through machine learning based on multiple moving speeds and the channel state information compensation results between the two communicating parties obtained at multiple moving speeds.

[0126] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: acquiring the target moving speed of the relative motion between a first device and a second device, and multiple channel estimation results of the channel between the first device and the second device; based on the multiple channel estimation results, compensating the channel state information of the channel by interpolation to obtain a target intermediate compensation result; using the target moving speed and the target intermediate compensation result as input, compensating the channel state information of the channel by a channel state information prediction model to obtain a target channel state information compensation result, wherein the channel state information prediction model is obtained through machine learning based on multiple moving speeds and the channel state information compensation results between the communicating parties obtained at multiple moving speeds.

[0127] The order of the above embodiments of the present invention is merely for description and does not represent the superiority or inferiority of the embodiments.

[0128] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0129] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of modules described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between modules, and may be electrical or other forms.

[0130] The modules described above as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0131] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0132] If the aforementioned integrated modules are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0133] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A channel compensation processing method, characterized in that, include: The target moving speed relative to the first device and the second device is obtained, as well as multiple channel estimation results of the channel between the first device and the second device; Based on the multiple channel estimation results, the channel state information of the channels is compensated by interpolation to obtain the target intermediate compensation result; Using the target moving speed and the target intermediate compensation result as input, a channel state information prediction model is used to compensate the channel state information of the channel to obtain the target channel state information compensation result. The channel state information prediction model is obtained through machine learning based on multiple moving speeds and the channel state information compensation results between the two communicating parties under the multiple moving speeds.

2. The method according to claim 1, characterized in that, Before obtaining the target channel state information compensation result of the channel by using the target moving speed and the target intermediate compensation result as input and employing a channel state information prediction model, the method further includes: Obtain the channel estimation results corresponding to the multiple movement speeds respectively; Based on the channel estimation results corresponding to the multiple movement speeds, the channel state information of the channel between the two communicating parties is compensated by interpolation to obtain the historical intermediate compensation results corresponding to the multiple movement speeds, and the historical channel state information compensation results corresponding to the multiple movement speeds. Based on the multiple movement speeds, the historical intermediate compensation results corresponding to the multiple movement speeds, and the historical channel state information compensation results corresponding to the multiple movement speeds, the initial neural network model is trained to obtain the channel state information prediction model.

3. The method according to claim 2, characterized in that, When the channel estimation result includes a first channel estimation result and a second channel estimation result, the method of compensating the channel state information of the channel between the communicating parties by interpolation based on the channel estimation results corresponding to the plurality of movement speeds, to obtain historical intermediate compensation results corresponding to the plurality of movement speeds and historical channel state information compensation results corresponding to the plurality of movement speeds, includes: Based on the first channel estimation results corresponding to the multiple movement speeds, the channel state information of the channel between the two communicating parties is compensated by interpolation to obtain the historical intermediate compensation results corresponding to the multiple movement speeds. The first channel estimation result is a channel estimation result obtained based on the channel probe signal received by one of the two communicating parties. Based on the first channel estimation results corresponding to the multiple movement speeds and the second channel estimation results corresponding to the multiple movement speeds, the channel state information of the channel between the two communicating parties is compensated by interpolation to obtain the historical channel state information compensation results corresponding to the multiple movement speeds. The second channel estimation result is a channel estimation result obtained based on the channel probe signal received by the other party in the communication.

4. The method according to claim 3, characterized in that, The initial neural network model is trained based on the multiple movement speeds, the historical intermediate compensation results corresponding to the multiple movement speeds, and the historical channel state information compensation results corresponding to the multiple movement speeds to obtain the channel state information prediction model, including: Using the multiple movement speeds and their corresponding historical intermediate compensation results as inputs, and the corresponding historical channel state information compensation results as outputs, the initial neural network model is trained to obtain the channel state information prediction model.

5. The method according to claim 3, characterized in that, The historical intermediate compensation results and the corresponding historical channel state information compensation results for the multiple moving speeds are all in matrix form, and the dimensions of the historical intermediate compensation results and the corresponding historical channel state information compensation results for the multiple moving speeds are the same. The dimension includes the number of matrix rows and the number of matrix columns. The number of matrix rows represents the total number of subcarriers, and the number of matrix columns represents the total number of channel state information.

6. The method according to claim 1, characterized in that, When the target intermediate compensation result is in matrix form, the step of compensating the channel state information of the channel using interpolation based on the multiple channel estimation results to obtain the target intermediate compensation result includes: Determine the dimensions of the target intermediate compensation result; Based on the dimension of the target intermediate compensation result, interpolation is used to compensate the multiple channel estimation results to obtain the target intermediate compensation result.

7. The method according to claim 6, characterized in that, The dimensions for determining the intermediate compensation result of the target include: Information on the time intervals between multiple channel probes performed between the first device and the second device when they move relative to each other at the target moving speed is obtained. Based on the time interval information, the dimension of the target intermediate compensation result is determined, wherein the time interval information includes at least: the delay information of channel detection between the two communicating parties, and the time interval between two adjacent channel detections in the multiple channel detections.

8. The method according to any one of claims 1 to 7, characterized in that, When compensating for the channel state information of the channel using either the first device or the second device, the process involves using the target moving speed and the target intermediate compensation result as inputs, and employing a channel state information prediction model to compensate for the channel state information to obtain the target channel state information compensation result, including: Based on the target's moving speed and the target's intermediate compensation result, the channel state information prediction model is used to obtain the third channel state information compensation result; Obtain multiple third channel estimation results obtained by the first device and another device of the second device based on multiple received channel probe signals; Determine the channel state information with the same time slot among the third channel state information compensation result and the multiple third channel estimation results; Based on the channel state information with the same time slot, the target channel state information compensation result is obtained.

9. The method according to any one of claims 1 to 7, characterized in that, When both the first device and the second device compensate for the channel state information of the channel, the step of using the target moving speed and the target intermediate compensation result as input, and employing a channel state information prediction model to compensate for the channel state information to obtain the target channel state information compensation result includes: Based on the target moving speed and the target intermediate compensation result corresponding to the first device, the channel state information prediction model corresponding to the first device is used to obtain the fourth channel state information compensation result. Based on the target moving speed and the target intermediate compensation result corresponding to the second device, the channel state information prediction model corresponding to the second device is used to obtain the fifth channel state information compensation result. Based on the fourth channel state information compensation result and the fifth channel state information compensation result, predictive channel state information with the same corresponding time slot is determined; Based on the predicted channel state information with the same time slot, the target channel state information compensation result is obtained.

10. A communication device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the channel compensation processing method according to any one of claims 1 to 9.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the channel compensation processing method according to any one of claims 1 to 9.

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