Channel estimation methods, apparatus, equipment and storage media

Abstract

This application proposes a channel estimation method, apparatus, device, and storage medium, relating to the field of communication technology, for achieving channel estimation of a single-relay cooperative communication cascaded channel in a low-complexity manner. The method includes: the channel estimation apparatus acquiring multiple pilot signals transmitted by a target device to a relay device; and generating a compressed pilot signal for each of the multiple pilot signals based on a preset compressed sensing algorithm and the pilot matrix of the multiple pilot signals, thus obtaining multiple compressed pilot signals. Further, the channel estimation apparatus estimates the channel state between the target device and the relay device based on the multiple pilot signals and the multiple compressed pilot signals.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a channel estimation method, apparatus, device and storage medium. Background Technology

[0002] In long-distance communication scenarios, signal attenuation is severe. Due to the high installation and maintenance costs of deploying fixed base stations, and constrained by cost, the most promising technology for achieving wireless connections between users or between users and base stations is the deployment of relay equipment. This is commonly referred to as a long-distance communication scenario. Relay equipment can improve communication quality at the cell edge without increasing the number of base stations.

[0003] Currently, for channel estimation methods of single-relay cooperative communication cascaded channels, when the time-varying characteristics of the cascaded channel are not obvious, the least squares method is generally used to estimate based on the training sequence signal on the receiving end side. However, the least squares method requires a large amount of prior knowledge of the cascaded channel. The amount of prior knowledge determines the accuracy of the model, which leads to the need to send a large number of training sequences for modeling before building the model, increasing the complexity of channel estimation. Summary of the Invention

[0004] This application proposes a channel estimation method, apparatus, device, and storage medium for achieving channel estimation of a single-relay cooperative communication cascaded channel with low complexity.

[0005] Firstly, a channel estimation method is provided, comprising: a channel estimation device acquiring multiple pilot signals transmitted by a target device to a relay device; and generating a compressed pilot signal for each of the multiple pilot signals according to a preset compressed sensing algorithm and the pilot matrix of the multiple pilot signals, thereby obtaining multiple compressed pilot signals. Further, the channel estimation device estimates the channel state between the target device and the relay device based on the multiple pilot signals and the multiple compressed pilot signals.

[0006] In one possible design, the process of generating a compressed pilot signal for each of the multiple pilot signals based on a preset compressed sensing algorithm and the pilot matrix of multiple pilot signals includes: performing sparsity processing on the pilot matrix of the multiple pilot signals to determine the sparse signal corresponding to each of the multiple pilot signals; and determining the compressed pilot signal corresponding to each of the multiple pilot signals based on the sparse signal corresponding to each of the multiple pilot signals and the preset compressed sensing algorithm.

[0007] In one possible design, the channel estimation device determines the reconstructed pilot signal for the current moment based on the target pilot signal in a first time period. The first time period includes multiple consecutive moments, and the target pilot signal is the signal received by the target device and transmitted by the relay device. The signal error is determined based on the current pilot signal and the reconstructed pilot signal. If the signal error is greater than a preset error, the number of multiple pilot signals is adjusted based on a preset value, resulting in a greater number of pilot signals after adjustment than before. If the signal error is less than or equal to the preset error, the number of multiple pilot signals is adjusted based on a preset value, resulting in a less number of pilot signals after adjustment than before.

[0008] In one possible design, the channel estimation device determines the reconstructed pilot signal at the current moment based on the target pilot signal of the first time period, including: predicting the predicted pilot signal for each moment in a second time period based on the target pilot signal of the first time period, wherein the second time period includes multiple consecutive moments and the first time period includes the second time period; determining a residual sequence based on the predicted pilot signal and the target pilot signal at each moment in the second time period, wherein the residual sequence includes the residual corresponding to each moment in the second time period; determining the reconstructed residual based on the residual sequence and a preset regression algorithm; predicting the predicted pilot signal at the current moment based on the target pilot signal of the first time period; and determining the reconstructed pilot signal based on the predicted pilot signal and the reconstructed residual at the current moment.

[0009] Secondly, a channel estimation apparatus is provided, comprising an acquisition unit, a generation unit, and a processing unit. The acquisition unit acquires multiple pilot signals transmitted by a target device to a relay device, wherein the target device is a base station device or a terminal device. The generation unit generates a compressed pilot signal for each of the multiple pilot signals based on a preset compressed sensing algorithm and the pilot matrix of the multiple pilot signals, thereby obtaining multiple compressed pilot signals. The processing unit estimates the channel state between the target device and the relay device based on the multiple pilot signals and the multiple compressed pilot signals.

[0010] In one possible design, the processing unit is further configured to perform sparse processing based on the pilot matrix of the multiple pilot signals to determine the sparse signal corresponding to each pilot signal among the multiple pilot signals. The processing unit is also configured to determine the compressed pilot signal corresponding to each pilot signal among the multiple pilot signals based on the sparse signal corresponding to each pilot signal among the multiple pilot signals and a preset compressed sensing algorithm.

[0011] In one possible design, the processing unit is further configured to determine the reconstructed pilot signal at the current moment based on the target pilot signal of the first time period, wherein the first time period includes multiple consecutive moments, and the target pilot signal is the signal received by the target device and transmitted by the relay device. The processing unit is further configured to determine the signal error based on the pilot signal at the current moment and the reconstructed pilot signal. The processing unit is further configured to adjust the number of multiple pilot signals based on a preset value when the signal error is greater than a preset error, such that the adjusted number of multiple pilot signals is greater than the original number of multiple pilot signals. The processing unit is further configured to adjust the number of multiple pilot signals based on a preset value when the signal error is less than or equal to the preset error, such that the adjusted number of multiple pilot signals is less than the original number of multiple pilot signals.

[0012] In one possible design, a processing unit is specifically configured to predict the predicted pilot signal at each moment in a second time period based on the target pilot signal in a first time period. The second time period includes multiple consecutive moments, and the first time period encompasses the second time period. The processing unit is also specifically configured to determine a residual sequence based on the predicted pilot signal at each moment in the second time period and the target pilot signal at each moment in the second time period. The residual sequence includes the residual corresponding to each moment in the second time period. The processing unit is further configured to determine a reconstructed residual based on the residual sequence and a preset regression algorithm. Finally, the processing unit is specifically configured to predict the predicted pilot signal at the current moment based on the target pilot signal in the first time period. Finally, the processing unit is specifically configured to determine a reconstructed pilot signal based on the predicted pilot signal at the current moment and the reconstructed residual.

[0013] Thirdly, an electronic device is provided, comprising a memory and a processor; the memory and the processor are coupled, the memory being used to store computer program code including computer instructions, wherein when the processor executes the computer instructions, the electronic device performs a channel estimation method as provided in the first aspect or any possible design thereof.

[0014] Fourthly, a computer-readable storage medium is provided, wherein instructions are stored therein, which, when executed on an electronic device, cause the electronic device to perform a channel estimation method as provided in the first aspect or any possible implementation thereof.

[0015] Fifthly, a computer program product is provided, comprising computer instructions that, when executed on an electronic device, enable the electronic device to perform a channel estimation method as provided in the first aspect or any possible implementation thereof.

[0016] In the channel estimation method provided in this application, the channel estimation problem is transformed into a low-rank matrix approximation problem, and the compressed sensing algorithm is used to reconstruct the cascaded signal to achieve hierarchical channel estimation, thereby reducing the complexity of channel estimation. Attached Figure Description

[0017] Figure 1 A schematic diagram of a communication system architecture provided for embodiments of this application. Figure 1 ;

[0018] Figure 2 A schematic diagram of a communication system architecture provided for embodiments of this application. Figure 2 ;

[0019] Figure 3 A schematic flowchart of a channel estimation method provided for embodiments of this application. Figure 1 ;

[0020] Figure 4 A schematic flowchart of a channel estimation method provided for embodiments of this application. Figure 2 ;

[0021] Figure 5 A schematic flowchart of a channel estimation method provided for embodiments of this application. Figure 3 ;

[0022] Figure 6 A schematic flowchart of a channel estimation method provided for embodiments of this application. Figure 4 ;

[0023] Figure 7 A schematic diagram illustrating adaptive adjustment of pilot signal sampling rate provided for an embodiment of this application;

[0024] Figure 8 A schematic diagram of a channel estimation device provided for an embodiment of this application;

[0025] Figure 9 A schematic diagram of an electronic device structure provided for embodiments of this application. Figure 1 ;

[0026] Figure 10 A schematic diagram of an electronic device structure provided for embodiments of this application. Figure 2 . Detailed Implementation

[0027] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0028] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0029] In the description of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. "And / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" and "multiple" refer to two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0030] In long-distance communication scenarios, signal attenuation is severe. Due to the high installation and maintenance costs of deploying fixed base stations, and constrained by cost, the most promising technology for achieving wireless connections between users or between users and base stations is the deployment of relay equipment. This is commonly referred to as a long-distance communication scenario. Relay equipment can improve communication quality at the cell edge without increasing the number of base stations.

[0031] Relay systems extend signal range through multi-hop connections. The distributed and mobile nature of relay devices means that traditional multiple-input multiple-output (MIMO) channel estimation techniques cannot be directly applied to channel estimation in multi-relay systems. Furthermore, traditional signal estimation in relay scenarios often derives expressions or probability distribution functions of the destination node's received signal-to-noise ratio (SNR) using channel statistical state information. This assumption relies on idealized assumptions such as a quasi-stationary channel state or slow signal fading, frequently resulting in low accuracy in channel estimation.

[0032] Currently, relaying is generally used in ad hoc networks between ships, where a ship's signal originates from signal relays on multiple ships. In this scenario, the relay equipment is mobile, meaning channel estimation needs to consider frequency offset characteristics. Therefore, traditional MIMO diversity techniques cannot be used for channel estimation in relay scenarios. Channel estimation methods that improve the accuracy of cascaded channel estimation can effectively reduce performance discrepancies between theoretical and practical results from various collaborative techniques, including relay beamforming and network precoding. Furthermore, increasing the understanding of the characteristics of cascaded channel transmission information helps in the overall planning and scheduling of collaborative communication, making it more reasonable in terms of both performance and overhead. As transmission distance increases, the received signal is prone to deep fading in amplitude, leading to a significant deterioration in transmission performance.

[0033] Among the channel estimation methods in related technologies is the semi-blind estimation method based on ensemble training sequences. However, this method is unsatisfactory in terms of computational complexity and accuracy due to the fuzziness in the estimation of channel characteristics.

[0034] Secondly, there is a method to provide the cascaded channel estimate from the transmitter to the receiver using linear minimum unbiased estimation. The basic idea of ​​this approach is as follows: first, the transmitter and relay equipment send training sequences to the relay equipment and receiver, respectively; then, the relay equipment forwards the signal received in the first stage to the receiver. Finally, the corresponding result is obtained using the linear minimum mean square estimation method. Its drawback is that the relay equipment needs to know the content of the training sequence used by the transmitter in advance, which is difficult to achieve in practical scenarios.

[0035] In addition, there is another method that uses cascaded channel estimation and segmented channel estimation. The most fatal feature of this study is that it assumes the influence of noise mixed in during the transmission of the signal, and does not take into account that the channel state of the cooperative channel changes greatly as the relay equipment moves.

[0036] Furthermore, in order to cope with the time-varying characteristics of the channel, some researchers have developed a training sequence structure that represents the time-varying cascaded channel as a weighted sum of complex exponential bases. The relevant weights are derived based on the received signal. However, the computational complexity of this weight derivation increases rapidly with the increase of relay nodes, and the calculation process of complex exponential bases is also not simple as the environment changes.

[0037] In summary, existing channel estimation methods for communication cascade channels in single-relay system networks can be categorized into two aspects.

[0038] Firstly, for cascaded cooperative communication channels with indistinct time-varying characteristics, estimation is performed using traditional methods (such as least squares and maximum likelihood) that minimize the mean square error, based on the training sequence signal from the receiver side. Secondly, for cascaded cooperative communication channels with significant relative movement between the transmitting and receiving terminals, channel estimation is achieved by estimating the weights and frequencies of the Basis Expansion Model (BEM), while simultaneously studying the time-domain autocorrelation characteristics of the channel by constructing a scatterer ring model.

[0039] However, certain drawbacks exist in related technologies. When using the least squares method for channel estimation, a large amount of prior knowledge about the cascaded channels is required. The amount of prior knowledge determines the model's accuracy, necessitating the sending of numerous training sequences before model building, thus increasing the complexity of channel estimation. Furthermore, the environment faced by single-relay cooperative communication is complex and variable. The changes in the cascaded channel from source to destination cannot be expressed by a constant but are typically random variables. Therefore, using the BEM model to superimpose weights on each cascaded channel has limited effectiveness in improving channel estimation.

[0040] To address the aforementioned problems, this application proposes a channel estimation method, apparatus, device, and storage medium. The channel estimation apparatus acquires multiple pilot signals transmitted by a target device to a relay device; and generates a compressed pilot signal for each of the multiple pilot signals based on a preset compressed sensing algorithm and the pilot matrix of the multiple pilot signals, thus obtaining multiple compressed pilot signals. Furthermore, the channel estimation apparatus estimates the channel state between the target device and the relay device based on the multiple pilot signals and the multiple compressed pilot signals.

[0041] In this way, the channel estimation method provided in this application transforms the channel estimation problem into a low-rank matrix approximation problem, and uses compressed sensing algorithms to reconstruct the cascaded signals to achieve hierarchical channel estimation, thereby reducing the complexity of channel estimation.

[0042] Figure 1 This application illustrates a communication system, and the channel estimation method provided in this embodiment can be applied to systems such as... Figure 1 The communication system shown is used to perform channel estimation for a single-relay cooperative communication cascaded channel. For example... Figure 1 As shown, the communication system 10 includes a channel estimation device 11, a base station device 12, a relay device 13, and a terminal device 14.

[0043] The base station device 12, relay device 13, and terminal device 14 are connected wirelessly. The channel estimation device 11 can be deployed on the relay device 13 or independently on other electronic devices. The electronic devices are connected to the relay device 13, and the specific connection method can be wired or wireless. This application embodiment does not specifically limit this.

[0044] The aforementioned electronic devices may include mobile phones, tablets, wearable devices, in-vehicle devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), desktop computers, cloud servers, etc. This application does not limit the specific type of electronic device.

[0045] The relay device 13 can be used to amplify the signal.

[0046] It should be noted that the amplification factor of the relay device 13 for amplifying the signal can be determined according to the following formula.

[0047]

[0048] Where h represents the channel state, and N 0,1 This represents the single-sided power spectral density.

[0049] In a bidirectional relay network, such as Figure 2 As shown, data transmission can be achieved with just two time slots.

[0050] In the first time slot, let x1(t) represent the signal sent from the base station to the relay device, and x2(t) represent the signal sent from the terminal device to the relay device. h1 and h2 represent the channel states between the base station and the relay device, and between the relay device and the terminal device, respectively. Let the transmission powers of the base station, relay device, and terminal device be P1, P2, and P3, respectively. r P2.

[0051] The signal received by the relay device in the first time slot is y r (t) is shown in Formula 2 below.

[0052]

[0053] Where, n r It is Gaussian white noise, where E and T represent the energy required to transmit 1 bit of information and the duration, respectively, and are generally considered to be known values.

[0054] In the second time slot, signal y r The signals y1(t) and y2(t) are amplified and transmitted to the base station equipment and the terminal equipment, respectively. The signals received by the base station equipment and the terminal equipment are y1(t) and y2(t), as shown in Formula 3 and Formula 4 below.

[0055] y1(t)=G1y r (t)×h1+n1 (Formula 3)

[0056] y2(t)=G2y r (t)×h²+n² (Formula 4)

[0057] Wherein, G1 is the amplification factor used when the relay device sends a signal to the base station device, h1 is the channel state of the channel between the relay device and the base station device, and n1 is the Gaussian white noise of the channel between the relay device and the base station device; G2 is the amplification factor used when the relay device sends a signal to the base station device, h2 is the channel state of the channel between the relay device and the base station device, and n2 is the Gaussian white noise of the channel between the relay device and the base station device.

[0058] Figure 3 This is a flowchart illustrating a channel estimation method according to some exemplary embodiments. In some embodiments, the above-described channel estimation method can be applied to, for example... Figure 1 The communication system 10 shown includes a channel estimation device 11. Hereinafter, embodiments of this application will describe the channel estimation method by taking the application of the channel estimation method to the channel estimation device 11 as an example.

[0059] like Figure 3 As shown, the channel estimation method provided in this application includes the following steps S201-S203.

[0060] S201, The channel estimation device acquires multiple pilot signals sent by the target device to the relay device.

[0061] The target equipment is either a base station or a terminal device.

[0062] As one possible implementation, the channel estimation device acquires multiple pilot signals sent by the target device to the relay device, and further determines the pilot matrix of each pilot signal among the multiple pilot signals.

[0063] For example, the pilot matrix of each of the multiple pilot signals can be shown in Formula 5 below.

[0064] X = [x1, x2, ..., x n ] T (Formula 5)

[0065] Where x1,x2,...,x n These are the pilot matrices for each of the multiple pilot signals, where n is the number of pilot signals.

[0066] S202. The channel estimation device generates a compressed pilot signal for each of the multiple pilot signals based on a preset compressed sensing algorithm and the pilot matrix of multiple pilot signals, thereby obtaining multiple compressed pilot signals.

[0067] The magnitude of the compressed pilot signal is much smaller than the magnitude of the pilot signal.

[0068] As one possible implementation, the channel estimation device has a built-in preset compressed sensing algorithm. After acquiring multiple pilot signals based on the above step S201 and determining the pilot matrix of each pilot signal, the preset compressed sensing algorithm is input for the pilot matrix of each pilot signal, and the compressed pilot signal is output.

[0069] For example, the compressed pilot signal Z is shown in Formula 6 below.

[0070] Z = φX (Formula 6)

[0071] Where Z is the compressed pilot signal, φ is the compressed sensing matrix, and X is the pilot matrix of the pilot signal.

[0072] It should be noted that the specific method for generating compressed pilot signals based on the pilot matrix of each pilot signal using the preset compressed sensing algorithm can be found in the subsequent description of the embodiments of this application. The preset compressed sensing algorithm can be pre-set in the channel estimation device by the operation and maintenance personnel of the communication system, and the embodiments of this application do not specifically limit it.

[0073] Understandably, by combining the pilot matrix of the pilot signal with compressed sensing algorithms to extract channel features, the computational resources required for channel estimation can be greatly reduced.

[0074] S203. The channel estimation device estimates the channel state between the target device and the relay device based on multiple pilot signals and multiple compressed pilot signals.

[0075] As one possible implementation, the channel estimation device substitutes multiple compressed pilot signals into Equation 2 above to obtain Equation 7 below, thus obtaining the signal received by the relay device. Further, based on the determined signal received by the relay device, if the target device is a base station, the channel estimation device substitutes the signal received by the relay device into Equation 3 above to obtain Equation 8 below; if the target device is a terminal device, the channel estimation device substitutes the signal received by the relay device into Equation 4 above to obtain Equation 9 below. Further, based on Equations 7, 8, and 9, and the known multiple pilot signals, the channel estimation device estimates the channel state between the target device and the relay device.

[0076] For example, Formulas 7, 8, and 9 are shown below.

[0077]

[0078] Y1(t)=G1Y r (t)×h1+n1 (Formula 8)

[0079] Y2(t)=G2Y r (t)×h²+n² (Formula Nine)

[0080] Understandably, the channel estimation method provided in this application transforms the channel estimation problem into a low-rank matrix approximation problem, uses compressed sensing algorithms to reconstruct the cascaded signals, and achieves hierarchical channel estimation based on known pilot signals, thereby reducing the complexity of channel estimation.

[0081] In one design, to further reduce the complexity of channel estimation, such as Figure 4 As shown, the channel estimation method provided in this application embodiment also includes S301-S302.

[0082] S301. The channel estimation device performs sparse processing based on the pilot matrix of multiple pilot signals to determine the sparse signals corresponding to the multiple pilot signals.

[0083] As one possible implementation, the channel estimation device determines the sparse representation corresponding to each of the multiple pilot signals based on each pilot signal and a sparse decomposition algorithm. Further, the channel estimation device determines the sparse signal corresponding to each pilot signal based on the sparse representation corresponding to each pilot signal and a sparse transform basis.

[0084] It should be noted that the sparse decomposition algorithm can be pre-configured in the channel estimation device by the communication system's operation and maintenance personnel. For example, the sparse decomposition algorithm can be the matching pursuit (MP) algorithm, the orthogonal matching pursuit (OMP) algorithm, the conpressive sampling matching pursuit (CoSaMP) algorithm, the regularized orthogonal matching pursuit (ROMP) algorithm, the stagewise orthogonal matching pursuit (StOMP) algorithm, the subspace pursuit (SP) algorithm, etc. This application embodiment does not specifically limit it.

[0085] Similarly, the sparse transform basis can be pre-set in the channel estimation device by the operators of the communication system. For example, the sparse transform basis can be a discrete cosine transform basis, a fast Fourier transform basis, a discrete wavelet transform basis, a Curvelet basis, a Gabor basis, and a redundant dictionary, etc. The embodiments of this application do not specifically limit this.

[0086] For example, combining the pilot matrix of multiple pilot signals shown in Formula 5 above, the sparse signal corresponding to each pilot signal can be calculated using the method shown in Formula 10 below.

[0087]

[0088] Where X is the sparse signal corresponding to the pilot signal, S is the sparse representation corresponding to the pilot signal, and ψ is the sparse transform basis.

[0089] Understandably, due to the redundancy of signals, in order to simplify the representation of signals, the channel estimation method provided in this application performs sparse processing on the pilot signals, so as to reduce the computational complexity of the algorithm in the subsequent process.

[0090] S302. The channel estimation device determines the compressed pilot signal corresponding to each pilot signal among the multiple pilot signals based on the sparse signal corresponding to each pilot signal among the multiple pilot signals and the preset compressed sensing algorithm.

[0091] As one possible implementation, the channel estimation device trains a preset compressed sensing algorithm based on a certain number of pilot signals to obtain a compressed sensing matrix. Further, based on the compressed sensing matrix obtained from the preset compressed sensing algorithm training and the sparse signal corresponding to each pilot signal, the channel estimation device performs calculations to obtain the compressed pilot signal corresponding to each pilot signal.

[0092] For example, combining Formula 6 and Formula 10 above, the calculation method for determining the compressed pilot signal based on the compressed sensing matrix and sparse signal is shown in Formula 11 below.

[0093] Z=φψS (Formula Eleven)

[0094] Where Z is the compressed pilot signal corresponding to the pilot signal, φ is the compressed sensing matrix, ψ is the sparse transform basis, and S is the sparse representation corresponding to the pilot signal.

[0095] It is understood that in the channel estimation method provided in this application embodiment, the processing of pilot signals, including sparse processing of pilot signals and compressed sensing processing of sparse processed signals, reduces the algorithm complexity and reduces the consumption of computing resources when performing channel estimation.

[0096] In one design, the aforementioned channel estimation method requires a certain number of pilot signals to improve the accuracy of the estimated channel state. However, as the number of pilot signals increases, pilot pollution occurs. To meet the needs of channel estimation in dynamic environments and reduce pilot signal overhead, such as... Figure 5 As shown, the channel estimation method provided in this application embodiment also includes S401-S405.

[0097] S401. The channel estimation device determines the reconstructed pilot signal at the current moment based on the target pilot signal in the first time period.

[0098] The first time period includes multiple consecutive moments, and the target pilot signal is the signal received by the target device from the relay device.

[0099] As one possible implementation, the channel estimation device acquires the signal received by the target device from the relay device at each moment within the first time period and identifies it as the target pilot signal. Further, the channel estimation device inputs the target pilot signal from the first time period into a preset reconstruction algorithm according to the time interval to determine the reconstructed pilot signal for the current moment.

[0100] It should be noted that the signal received by the target device from the relay device is amplified and transmitted by the relay device in response to the pilot signal sent by the target device.

[0101] In some embodiments, the preset reconstruction algorithm includes a regression model and adjustment parameters. First, the reconstruction pilot signal at the current time is predicted based on the regression model. Then, the predicted reconstruction pilot signal is adjusted based on the adjustment parameters to obtain the reconstructed pilot signal.

[0102] S402. The channel estimation device determines the signal error based on the pilot signal at the current moment and the reconstructed pilot signal.

[0103] As one possible implementation, the channel estimation device obtains the signal received by the target device from the relay device at the current moment. Furthermore, the channel estimation device calculates the error between the pilot signal at the current moment and the reconstructed pilot signal, and determines this error as the signal error.

[0104] S403. The channel estimation device determines the relationship between the signal error and the preset error.

[0105] It should be noted that the preset error can be set in advance in the channel estimation device by the operation and maintenance personnel of the communication system, and this application embodiment does not specifically limit this.

[0106] S404. When the signal error is greater than the preset error, the channel estimation device adjusts the number of multiple pilot signals based on the preset value.

[0107] The number of pilot signals after adjustment is greater than the number of pilot signals before adjustment.

[0108] As one possible implementation, based on the judgment of the channel estimation device on the magnitude of the signal error and the preset error in step S403 above, when the signal error is greater than the preset error, the channel estimation device determines the number of multiple pilot signals, and adds a preset value of pilot signals on the basis of multiple pilot signals to adjust the number of multiple pilot signals, so that the channel estimation device estimates the channel state with a larger number of adjusted pilot signals in the next moment.

[0109] It should be noted that the preset values ​​can be set in advance in the channel estimation device by the operation and maintenance personnel of the communication system, and this application embodiment does not specifically limit this.

[0110] For example, if the sampling rate of the pilot signal at the current moment is m and the preset value is k, and the signal error is greater than the preset error, the channel estimation device determines the next moment and estimates the channel state using m+k pilot signals. If the signal error at the next moment is still greater than the preset error, the channel estimation device determines the moment after that and estimates the channel state using m+2k pilot signals.

[0111] S405. When the signal error is less than or equal to the preset error, the channel estimation device adjusts the number of multiple pilot signals based on the preset value.

[0112] The number of pilot signals after adjustment is less than the number of pilot signals before adjustment.

[0113] As one possible implementation, based on the judgment of the channel estimation device on the magnitude of the signal error and the preset error in step S403 above, when the signal error is less than or equal to the preset error, the channel estimation device determines the number of multiple pilot signals, and reduces the number of pilot signals by a preset value based on the multiple pilot signals, thereby adjusting the number of multiple pilot signals so that the channel estimation device can estimate the channel state with the adjusted number of multiple pilot signals at the next moment.

[0114] For example, if the sampling rate of the pilot signal at the current moment is m and the preset value is k, and the signal error is less than or equal to the preset error, the channel estimation device determines the next moment and estimates the channel state using mk pilot signals. If the signal error at the next moment is still greater than the preset error, the channel estimation device determines the moment after that and estimates the channel state using m-2k pilot signals.

[0115] It is understandable that in the channel estimation method provided in the embodiments of this application, the sampling rate of the pilot signal is corrected according to the signal error. Therefore, when the relay equipment moves slowly, the pilot signal can be sent at a low sampling rate to avoid pilot pollution. Conversely, when the relay equipment moves at a high speed, the pilot signal is sent at a high sampling rate to ensure the accuracy of channel estimation.

[0116] In one design, to determine the reconstructed pilot signal at the current moment based on the target pilot signal in the first time period, such as... Figure 6 As shown, the channel estimation method provided in this application embodiment also includes S501-S505.

[0117] S501, The channel estimation device predicts the predicted pilot signal at each moment in the second time period based on the target pilot signal in the first time period.

[0118] The second time period includes multiple consecutive moments, and the first time period includes the second time period.

[0119] As one possible implementation, at each moment within the first time period, the target device receives the signal transmitted by the relay device and identifies it as the target pilot signal. Further, the channel estimation device determines the (n+1)th moment as the start moment of the second time period, following the time sequence. Further still, the channel estimation device inputs the target pilot signals from the previous n moments within the second time period into a preset regression model to determine the predicted pilot signal for each moment within the second time period.

[0120] It should be noted that the preset regression model is used to predict the target pilot signal and can be set in advance in the channel estimation device by the operation and maintenance personnel of the communication system. This application embodiment does not specifically limit this.

[0121] For example, the predicted pilot signal at time t can be calculated and determined according to the following formula 12.

[0122]

[0123] in, Let Y be the predicted pilot signal at time t, f() be the preset regression model, and Y be the pilot signal at time t. t-n-1 Y is the target pilot signal at time tn-1. t-1 Let be the target pilot signal at time t-1.

[0124] S502, The channel estimation device determines the residual sequence based on the predicted pilot signal at each time point in the second time period and the target pilot signal at each time point in the second time period.

[0125] The residual sequence includes the residuals corresponding to each time point within the second time period.

[0126] As one possible implementation, the channel estimation device calculates the residual at each time point within the second time interval based on the times corresponding to the predicted pilot signal and the target pilot signal. Furthermore, the channel estimation device generates a residual sequence based on the determined residuals at each time point.

[0127] For example, when the channel estimation device calculates the residual at each time point within the second time period, it can calculate and determine it according to the following formula thirteen.

[0128]

[0129] Among them, e t To predict the residual between the pilot signal and the target pilot signal at time t, Y t Let be the target pilot signal at time t. Let t be the predicted pilot signal at time t.

[0130] S503. The channel estimation device determines the reconstruction residual based on the residual sequence and the preset regression algorithm.

[0131] As one possible implementation, the channel estimation device calculates the reconstructed residuals by inputting the residual sequence generated in step S502 above into a preset regression algorithm.

[0132] It should be noted that the preset regression algorithm is used to obtain the reconstructed residual value based on the correlation of the residual at each time step. It can be preset in the channel estimation device by the operation and maintenance personnel of the communication system. This application embodiment does not specifically limit this.

[0133] For example, when the channel estimation device calculates the reconstruction residual, it can calculate and determine it according to the following formula fourteen.

[0134]

[0135] in, For the reconstructed residual at time t, e t Let be the residual sequence at time t, and ε and b be the parameters of the preset regression algorithm.

[0136] S504. The channel estimation device predicts the predicted pilot signal at the current time based on the target pilot signal in the first time period.

[0137] As one possible implementation, the channel estimation device obtains the target pilot signals from the target pilot signals of the previous n time periods from the target pilot signals of the first time period. Further, the channel estimation device inputs the obtained target pilot signals into a preset regression algorithm to determine the predicted pilot signal for the current time period.

[0138] It should be noted that the specific implementation of the channel estimation device predicting the pilot signal at the current moment can be referred to step S501 of the above embodiment of this application, and will not be repeated here.

[0139] S505 The channel estimation device determines the reconstructed pilot signal based on the predicted pilot signal at the current moment and the reconstruction residual.

[0140] As one possible implementation, the channel estimation device determines the reconstructed pilot signal by adding the reconstruction error determined in step S503 and the predicted pilot signal predicted in step S504.

[0141] For example, when the channel estimation device calculates the reconstructed pilot signal, it can calculate and determine it according to the following formula fifteen.

[0142]

[0143] in, The reconstructed pilot signal at time t, The predicted pilot signal at time t, This represents the reconstruction error.

[0144] Understandably, in the channel estimation method provided in this application embodiment, the compressed signal is used to reconstruct the signal error. Based on the compressed signal, the influence of other noise is eliminated, making the signal prediction more reflective of the signal change pattern and improving the accuracy of the algorithm.

[0145] In some embodiments, the adaptive adjustment of the sampling rate of the pilot signal in the above channel estimation method can be as follows: Figure 7As shown, a predicted pilot signal is determined based on multiple target pilot signals. Further, a reconstruction residual is determined based on the target pilot signal and the predicted pilot signal. Further, the pilot signal is reconstructed based on the predicted pilot signal and the reconstruction residual to obtain the reconstructed pilot signal. Further, the signal error is determined based on the reconstructed pilot signal and the target pilot signal, and compared with a preset error. The sampling rate of the pilot signal at the next time step is adjusted according to the relationship between the signal error and the preset error.

[0146] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, it includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0147] This application embodiment can divide the user equipment into functional modules according to the above method example. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0148] Figure 8 This is a schematic diagram of a channel estimation device provided in an embodiment of this application. This channel estimation device is used to perform the channel estimation method described above. Figure 8 As shown, the channel estimation device 60 includes an acquisition unit 601, a generation unit 602, and a processing unit 603.

[0149] The acquisition unit 601 is used to acquire multiple pilot signals sent by the target device to the relay device, where the target device is a base station device or a terminal device. For example, such as Figure 3 As shown, the acquisition unit 601 can be used to execute S201.

[0150] The generation unit 602 is used to generate a compressed pilot signal for each of the multiple pilot signals based on a preset compressed sensing algorithm and the pilot matrix of multiple pilot signals, thereby obtaining multiple compressed pilot signals. For example, as Figure 3 As shown, the generation unit 602 can be used to execute S202.

[0151] Processing unit 603 is used to estimate the channel state between the target device and the relay device based on multiple pilot signals and multiple compressed pilot signals. For example, Figure 3 As shown, the processing unit 603 can be used to execute S203.

[0152] Optional, such as Figure 8 As shown, in the channel estimation apparatus 60 provided in this application embodiment, the processing unit 603 is further configured to perform sparse processing based on the pilot matrix of multiple pilot signals to determine the sparse signal corresponding to each pilot signal among the multiple pilot signals. For example, as Figure 4 As shown, the processing unit 603 can be used to execute S301.

[0153] The processing unit 603 is further configured to determine the compressed pilot signal corresponding to each pilot signal among the multiple pilot signals based on the sparse signal corresponding to each pilot signal among the multiple pilot signals and a preset compressed sensing algorithm. For example, as Figure 4 As shown, the processing unit 603 can be used to execute S302.

[0154] Optional, such as Figure 8 As shown, in the channel estimation device 60 provided in this application embodiment, the processing unit 603 is further configured to determine the reconstructed pilot signal at the current moment based on the target pilot signal of the first time period. The first time period includes multiple consecutive moments, and the target pilot signal is the signal received by the target device and transmitted by the relay device. For example, such as Figure 5 As shown, the processing unit 603 can be used to execute S401.

[0155] The processing unit 603 is also used to determine the signal error based on the pilot signal at the current moment and the reconstructed pilot signal. For example, such as Figure 5 As shown, the processing unit 603 can be used to execute S402.

[0156] The processing unit 603 is further configured to adjust the number of multiple pilot signals based on a preset value when the signal error is greater than a preset error, such that the number of multiple pilot signals after adjustment is greater than the number of multiple pilot signals before adjustment. For example, Figure 5 As shown, the processing unit 603 can be used to execute S404.

[0157] The processing unit 603 is further configured to adjust the number of multiple pilot signals based on a preset value when the signal error is less than or equal to a preset error, such that the number of multiple pilot signals after adjustment is less than the number of multiple pilot signals before adjustment. For example, Figure 5 As shown, the processing unit 603 can be used to execute S405.

[0158] Optional, such as Figure 8As shown, in the channel estimation device 60 provided in this application embodiment, the processing unit 603 is specifically used to predict the predicted pilot signal at each moment in the second time period based on the target pilot signal in the first time period. The second time period includes multiple consecutive moments, and the first time period includes the second time period. For example, as... Figure 6 As shown, the processing unit 603 can be used to execute S501.

[0159] Processing unit 603 is specifically used to determine a residual sequence based on the predicted pilot signal at each time point within the second time period and the target pilot signal at each time point within the second time period. The residual sequence includes the residual corresponding to each time point within the second time period. For example, ... Figure 6 As shown, the processing unit 603 can be used to execute S502.

[0160] Processing unit 603 is specifically used to determine the reconstructed residuals based on the residual sequence and a preset regression algorithm. For example, Figure 6 As shown, the processing unit 603 can be used to execute S503.

[0161] The processing unit 603 is specifically used to predict the predicted pilot signal at the current time based on the target pilot signal in the first time period. For example, Figure 6 As shown, the processing unit 603 can be used to execute S504.

[0162] Processing unit 603 is specifically used to determine the reconstructed pilot signal based on the predicted pilot signal and the reconstruction residual at the current moment. For example, Figure 6 As shown, the processing unit 603 can be used to execute S505.

[0163] In implementing the functions of the integrated modules described above in hardware, this application provides a possible structural diagram of an electronic device. This electronic device is used to execute the channel estimation method performed by the channel estimation device in the above embodiments. Figure 9 As shown, the electronic device 70 includes a processor 701, a memory 702, and a bus 703. The processor 701 and the memory 702 can be connected via the bus 703.

[0164] Processor 701 is the control center of an electronic device. It can be a single processor or a collective term for multiple processing elements. For example, processor 701 can be a general-purpose central processing unit (CPU) or other general-purpose processors. The general-purpose processor can be a microprocessor or any conventional processor.

[0165] As one embodiment, processor 701 may include one or more CPUs, for example Figure 9CPU 0 and CPU 1 are shown in the diagram.

[0166] The memory 702 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0167] As one possible implementation, the memory 702 can exist independently of the processor 701. The memory 702 can be connected to the processor 701 via a bus 703 and is used to store instructions or program code. When the processor 701 calls and executes the instructions or program code stored in the memory 702, it can implement the channel estimation method provided in the embodiments of this application.

[0168] In another possible implementation, the memory 702 can also be integrated with the processor 701.

[0169] Bus 703 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0170] It should be pointed out that, Figure 9 The structure shown does not constitute a limitation on the electronic device 70. Except... Figure 9 In addition to the components shown, the electronic device 70 may include more than Figure 9 It can show more or fewer parts, or combine certain parts, or arrange different parts.

[0171] As an example, combined Figure 8The functions implemented by the acquisition unit 601, generation unit 602, and processing unit 603 in the channel estimation device 60 are the same as those of the acquisition unit 601, generation unit 602, and processing unit 603. Figure 9 The processor 701 in it has the same function.

[0172] Optional, such as Figure 9 As shown, the electronic device provided in this application embodiment may further include a communication interface 704.

[0173] Communication interface 704 is used to connect with other devices via a communication network. This communication network can be Ethernet, a wireless access network, a wireless local area network (WLAN), etc. Communication interface 704 may include an acquisition unit for receiving data and a transmission unit for sending data.

[0174] In one design, the communication interface in the electronic device provided in this application embodiment can also be integrated into the processor.

[0175] Figure 10 Another hardware structure of the electronic device in an embodiment of this application is shown. For example... Figure 10 As shown, the electronic device 80 may include a processor 801 and a communication interface 802. The processor 801 is coupled to the communication interface 802.

[0176] The functions of processor 801 can be referred to in the description of processor 701 above. In addition, processor 801 also has storage functions, which can be referred to in the description of memory 702 above.

[0177] The communication interface 802 is used to provide data to the processor 801. The communication interface 802 can be an internal interface of the electronic device or an external interface of the electronic device (equivalent to the communication interface 704).

[0178] It should be pointed out that, Figure 10 The structures shown do not constitute a limitation on electronic devices, except... Figure 10 In addition to the components shown, the electronic device 80 may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0179] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional units is used as an example. In practical applications, the above functions can be assigned to different functional units as needed, that is, the internal structure of the device can be divided into different functional units to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0180] This application also provides a computer-readable storage medium storing instructions. When a computer executes these instructions, the computer performs each step of the method flow shown in the above-described method embodiments.

[0181] Embodiments of this application provide a computer program product containing instructions that, when executed on a computer, cause the computer to perform the channel estimation method in the above method embodiments.

[0182] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), registers, hard disks, optical fibers, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing, or any other form of computer-readable storage medium in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may reside in an application-specific integrated circuit (ASIC). In the embodiments of this application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0183] Since the apparatus, device, computer-readable storage medium, and computer program product in the embodiments of this application can be applied to the above methods, the technical effects that can be obtained can also be referred to the above method embodiments. The embodiments of this application will not be repeated here.

[0184] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of channel estimation, characterized by, The method comprises: acquiring a plurality of pilot signals sent by a target device to a relay device, the target device being a base station device or a terminal device; generating compressed pilot signals of each of the plurality of pilot signals according to a preset compressed sensing algorithm and a pilot matrix of the plurality of pilot signals, to obtain a plurality of compressed pilot signals; estimating a channel state between the target device and the relay device according to the plurality of pilot signals and the plurality of compressed pilot signals; predicting a predicted pilot signal at each time point in a second time period according to a target pilot signal at a first time point, the second time period comprising a plurality of continuous time points, and the first time period containing the second time period; determining the predicted pilot signal at each time point in the second time period based on target pilot signals at the previous n time points of each time point in the second time period; determining a residual sequence according to the predicted pilot signal at each time point in the second time period and the target pilot signal at each time point in the second time period, the residual sequence comprising a residual corresponding to each time point in the second time period; determining a reconstructed residual according to the residual sequence and a preset regression algorithm; predicting a predicted pilot signal at a current time point according to the target pilot signal at the first time point; determining a reconstructed pilot signal according to the predicted pilot signal at the current time point and the reconstructed residual; the first time period comprises a plurality of continuous time points, and the target pilot signal is a signal sent by the relay device and received by the target device; determining a signal error according to the pilot signal at the current time point and the reconstructed pilot signal; in a case where the signal error is greater than a preset error, adjusting the number of the plurality of pilot signals based on a preset numerical value, the number of the plurality of pilot signals after adjustment being greater than the number of the plurality of pilot signals before adjustment; in a case where the signal error is less than or equal to the preset error, adjusting the number of the plurality of pilot signals based on the preset numerical value, the number of the plurality of pilot signals after adjustment being less than the number of the plurality of pilot signals before adjustment.

2. The channel estimation method of claim 1, wherein The method comprises: performing sparse processing on the pilot matrix of the plurality of pilot signals to determine a sparse signal corresponding to each of the plurality of pilot signals; determining a compressed pilot signal corresponding to each of the plurality of pilot signals according to the sparse signal corresponding to each of the plurality of pilot signals and the preset compressed sensing algorithm.

3. A channel estimation apparatus characterized by comprising: The method comprises an acquisition unit, a generation unit and a processing unit. The acquisition unit is configured to acquire a plurality of pilot signals sent by a target device to a relay device, the target device being a base station device or a terminal device. The generation unit is configured to generate compressed pilot signals of each of the plurality of pilot signals according to a preset compressed sensing algorithm and a pilot matrix of the plurality of pilot signals, to obtain a plurality of compressed pilot signals. The processing unit is configured to perform processing on the plurality of pilot signals. The processing unit is configured to estimate a channel state between the target device and the relay device according to the plurality of pilot signals and the plurality of compressed pilot signals. The processing unit is specifically configured to predict a predicted pilot signal at each time point in a second time period according to a target pilot signal at a first time point in the second time period, the second time period including a plurality of continuous time points, and the first time period containing the second time period. The processing unit is specifically configured to determine the predicted pilot signal at each time point in the second time period based on the target pilot signals at the previous n time points of each time point in the second time period. The processing unit is specifically configured to determine a residual sequence according to the predicted pilot signal at each time point in the second time period and the target pilot signal at each time point in the second time period, the residual sequence including a corresponding residual of each time point in the second time period. The processing unit is specifically configured to determine a reconstructed residual according to the residual sequence and a preset regression algorithm. The processing unit is specifically configured to predict a predicted pilot signal at a current time point according to the target pilot signal at the first time point. The processing unit is specifically configured to determine a reconstructed pilot signal according to the predicted pilot signal at the current time point and the reconstructed residual. The first time period includes a plurality of continuous time points, and the target pilot signal is a signal received by the target device and sent by the relay device. The processing unit is further configured to determine a signal error according to the pilot signal at the current time point and the reconstructed pilot signal. The processing unit is further configured to adjust the number of the plurality of pilot signals based on a preset value in a case where the signal error is greater than a preset error, and the adjusted number of the plurality of pilot signals is greater than the number of the plurality of pilot signals before adjustment. The processing unit is further configured to adjust the number of the plurality of pilot signals based on the preset value in a case where the signal error is less than or equal to the preset error, and the adjusted number of the plurality of pilot signals is less than the number of the plurality of pilot signals before adjustment.

4. The channel estimation apparatus of claim 3, wherein The generation unit is specifically configured to determine a sparse signal corresponding to each pilot signal in the plurality of pilot signals by performing sparse processing on a pilot matrix of the plurality of pilot signals. The generation unit is specifically configured to determine a compressed pilot signal corresponding to each pilot signal in the plurality of pilot signals according to the sparse signal corresponding to each pilot signal in the plurality of pilot signals and the preset compressed sensing algorithm.

5. An electronic device, comprising: The memory and the processor are coupled; The memory is configured to store computer program codes, the computer program codes including computer instructions; When the processor executes the computer instructions, the electronic device performs the channel estimation method in any one of claims 1-2.

6. A computer readable storage medium, the computer readable storage medium storing instructions, and the instructions comprising: ​ When the instructions are run on the electronic device, the electronic device is caused to perform the channel estimation method as claimed in any one of claims 1-2.

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