Channel estimation method and apparatus, communication device, and storage medium

Abstract

The application provides a channel estimation method and device, communication equipment and a storage medium, and relates to the technical field of communication. The channel estimation method is applied to a first device, and the method comprises the following steps: obtaining a communication signal transmitted by a second device and a predetermined pilot signal; performing association processing on the communication signal and the pilot signal to obtain a first-order correlation function between the communication signal and the pilot signal; obtaining a channel response matrix of a target channel of the communication signal for transmission according to the first-order correlation function and the communication signal; and performing channel estimation on the target channel by using the channel response matrix to obtain a channel estimation result of the target channel. The application greatly simplifies the channel estimation process, reduces the complexity of channel estimation, and has strong applicability.

Description

Technical Field

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

[0002] In existing channel estimation processes based on the covariance matrix, the presence of noise and spurious signals can affect the estimated covariance matrix and introduce errors. To address this issue, covariance compensation methods are introduced to remove these errors, resulting in a more accurate estimated covariance matrix. However, most channel estimation methods based on covariance matrix compensation employ non-blind estimation techniques using continuous pilot information. This method has high computational complexity, and pilot signals consume significant channel resources. Furthermore, for frequency-selective fading channels, the design and insertion of pilot signals are quite complex.

[0003] Other existing channel estimation methods, such as minimum mean squared error (MMSE) estimation and maximum likelihood estimation (MLE), are highly complex and require processing and computation of a large number of signals, so they may not be suitable for scenarios with high real-time requirements.

[0004] Sparse pilots can provide better coverage in both the time and frequency domains by selecting pilot positions with larger intervals, thus adapting to rapidly changing channels. Furthermore, using sparse pilots in multi-user systems can reduce interference between users by ensuring larger intervals between the pilot sequences of different users in the time or frequency domains, thereby reducing mutual interference. Currently, the main channel estimation method for sparse pilot sequences is compressed sensing algorithms, but they suffer from poor adaptability to complex environments.

[0005] Furthermore, since communication technology has been widely applied in people's production and daily life, and the resource optimization technology of wireless systems is a key research focus in the field of communication, channel estimation of wireless communication systems can effectively serve the resource optimization of wireless communication systems. However, for wireless communication systems with large capacity and dense users, the channel estimation methods have requirements such as high accuracy, high time-frequency resource efficiency, low computational complexity, adaptability to multi-path and multi-user interference, and real-time performance, which are difficult to meet by current channel estimation methods.

[0006] In summary, existing channel estimation methods suffer from high complexity and poor applicability. Summary of the Invention

[0007] The purpose of this invention is to provide a channel estimation method, apparatus, communication device, and storage medium to solve the problems of high complexity and poor applicability of existing channel estimation methods.

[0008] To address the aforementioned technical problems, the embodiments of the present invention provide the following technical solutions:

[0009] This invention provides a channel estimation method applied to a first device, the method comprising:

[0010] Acquire the communication signals sent by the second device and acquire the predetermined pilot signals;

[0011] The communication signal and the pilot signal are correlated to obtain a first-order correlation function between them.

[0012] Based on the first-order correlation function and the communication signal, the channel response matrix of the target channel for transmitting the communication signal is obtained;

[0013] Using the channel response matrix, channel estimation is performed on the target channel to obtain the channel estimation result of the target channel.

[0014] Optionally, before performing correlation processing on the communication signal and the pilot signal to obtain a first-order correlation function between the communication signal and the pilot signal, the method further includes:

[0015] The communication signal is subjected to Fourier transform processing to obtain the processed communication signal.

[0016] Optionally, the communication signal and the pilot signal are correlated to obtain a first-order correlation function between the communication signal and the pilot signal, including:

[0017] The communication signal and the pilot signal are correlated to obtain an initial first-order correlation function between them.

[0018] The initial first-order correlation function is statistically averaged to obtain the first-order correlation function between the communication signal and the pilot signal.

[0019] Optionally, channel estimation is performed on the target channel using the channel response matrix to obtain the channel estimation result of the target channel, including at least one of the following:

[0020] Using the time-domain information in the channel response matrix, channel attenuation is estimated for the target channel to obtain the channel attenuation estimation result for the target channel;

[0021] Using the channel response matrix, the channel multipath effect of the target channel is estimated to obtain the channel attenuation multipath effect result of the target channel;

[0022] Using the frequency domain information in the channel response matrix, the channel phase difference of the target channel is estimated to obtain the channel phase difference result of the target channel.

[0023] Optionally, using the channel response matrix, the target channel is estimated for multipath effects to obtain the channel attenuation multipath effect results of the target channel, including at least one of the following:

[0024] Based on the time-domain information in the channel response matrix, the multipath effect of the target channel is estimated to obtain the multipath component of the target channel.

[0025] The time-domain information in the channel response matrix is ​​subjected to Fourier transform processing to obtain the frequency response information of the target channel.

[0026] Optionally, the method further includes:

[0027] The communication signal is compensated based on the channel estimation results of the target channel.

[0028] Optionally, the communication signal is compensated based on the channel estimation result of the target channel, including at least one of the following:

[0029] The amplitude value of the communication signal is compensated for attenuation based on the channel attenuation estimation result;

[0030] The total channel delay is determined based on the channel attenuation multipath effect results, and multipath effect compensation is performed on the communication signal based on the total channel delay.

[0031] The signal phase difference is determined based on the channel phase difference result, and the communication signal is compensated for the phase difference effect based on the signal phase difference.

[0032] This invention also provides a channel estimation apparatus applied to a first device, the apparatus comprising:

[0033] The acquisition module is used to acquire the communication signals sent by the second device and to acquire the predetermined pilot signals;

[0034] The first processing module is used to perform correlation processing on the communication signal and the pilot signal to obtain a first-order correlation function between the communication signal and the pilot signal;

[0035] The second processing module is used to obtain the channel response matrix of the target channel for transmitting the communication signal based on the first-order correlation function and the communication signal.

[0036] The third processing module is used to perform channel estimation on the target channel using the channel response matrix to obtain the channel estimation result of the target channel.

[0037] This invention also provides a communication device, including: a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the channel estimation method as described above.

[0038] This invention also provides a readable storage medium storing a program that, when executed by a processor, implements the steps of the channel estimation method as described above.

[0039] At least one of the above technical solutions of the present invention has the following beneficial effects:

[0040] The channel estimation method provided by this invention involves a first device (i.e., a receiving device) acquiring a communication signal transmitted by a second device (i.e., a transmitting device) and a predetermined pilot signal. A correlation imaging algorithm is used to correlate the communication signal and the pilot signal to obtain a first-order correlation function between them. Based on the first-order correlation function and the communication signal, a channel response matrix for the target channel used for transmission is obtained. The channel response matrix is ​​then used to estimate the target channel, yielding the channel estimation result. The correlation imaging algorithm eliminates the need for complex preprocessing steps such as frequency offset compensation or symbol timing synchronization, significantly simplifying the channel estimation process and reducing its complexity. Furthermore, the correlation imaging algorithm possesses high temporal resolution and the ability to effectively handle rapidly changing channel environments, making it highly applicable. Attached Figure Description

[0041] Figure 1 A flowchart of the channel estimation method provided in the embodiments of the present invention;

[0042] Figure 2 A detailed flowchart of the channel estimation method provided in this embodiment of the invention;

[0043] Figure 3 This is a schematic diagram of the channel estimation device provided in an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of the structure of a communication device provided in an embodiment of the present invention. Detailed Implementation

[0045] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0046] Before describing the specific embodiments of the present invention, the following will be explained first:

[0047] Existing channel estimation methods for wireless communication systems include:

[0048] (1) Minimum Mean Square Error (MMSE) estimation: This method is based on minimizing the mean square error between the received signal and the estimated channel response, and estimates the channel by optimizing the parameters.

[0049] (2) Maximum Likelihood Estimation (MLE): This method is based on the maximum likelihood criterion and performs channel estimation by finding the channel parameters that maximize the probability of the received signal appearing under known conditions.

[0050] (3) Equivalent Channel Estimation: In some cases, the equivalent channel estimation method can be used to simplify the complexity of channel estimation. This method treats the multipath channel as a single equalized channel and approximates the actual channel by estimating the equivalent channel.

[0051] (4) Matrix decomposition methods: such as Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), which obtain channel state information by decomposing the covariance matrix of the received signal. For example, covariance matrix compensation method, etc.

[0052] (5) Methods to eliminate temporal redundancy: These methods utilize the time-varying nature of multipath channels to estimate the channel through correlation in the time domain. These methods include adaptive filters and cyclic prediction methods.

[0053] (6) Pilot signal method: In some wireless communication systems, the transmitter usually inserts a pilot signal with a known pattern. The receiver can estimate the channel by demodulating and analyzing the pilot signal.

[0054] To address the issues of high complexity and poor applicability of existing channel estimation methods, embodiments of the present invention provide a channel estimation method, apparatus, communication device, and storage medium.

[0055] like Figure 1 As shown, this embodiment of the invention provides a channel estimation method applied to a first device, the method comprising:

[0056] Step 101: Acquire the communication signal sent by the second device and acquire the predetermined pilot signal.

[0057] It should be noted that in this embodiment of the invention, the first device is a receiving device in a wireless communication system, and the second device is a transmitting device in a wireless communication system. It is assumed that the receiving end of the wireless communication system has M antennas deployed and the transmitting end has N antennas deployed, where M is an integer greater than or equal to 1 and N is an integer greater than or equal to 1.

[0058] In this step, the received signal is sampled. First, the wireless communication signal of the target (transmitting device) is obtained through the receiving device. These communication signals are usually digital signals containing information such as signal strength.

[0059] The expression for the sampled communication signal is as follows:

[0060] y(t)=[y1(t),y2(t),…,y m (t)]

[0061] In the above formula, m is the number of antennas deployed by the receiving device (the first device), and y m (t) represents a sample of the communication signal received by the m-th antenna of the receiving device at time t.

[0062] In this step, the pilot signal (or sparse pilot sequence) is known to the receiving device, and the specific acquisition process will not be described in detail.

[0063] Step 102: Perform correlation processing on the communication signal and the pilot signal to obtain the first-order correlation function between the communication signal and the pilot signal.

[0064] In this step, the communication signal and the pilot signal are correlated using a correlation imaging algorithm to obtain a first-order correlation function between them. Specifically, the correlation processing in the correlation imaging algorithm is used to calculate the first-order correlation function between the sparse pilot sequence and the communication signal.

[0065] It should be noted that the correlation imaging algorithm has advantages such as good applicability in scenarios with high target resolution, good real-time performance, strong adaptability to complex environments, high mobility environments, large-scale fading channels, high-frequency channels and multi-user systems. It has been widely used in spatiotemporal two-dimensional random signal processing. Since spatiotemporal two-dimensional random signals often have certain sparsity characteristics, the correlation imaging algorithm can also be used in wireless communication systems with sparse pilot sequences.

[0066] Prior to step 102, the method further includes:

[0067] The communication signal is subjected to a Fourier transform to obtain a processed communication signal, that is, the received communication signal is converted into a processed communication signal Y through a Fourier transform. In step 102 and subsequent steps, the communication signal is the processed communication signal Y.

[0068] In an optional embodiment, step 102 includes:

[0069] The communication signal and the pilot signal are correlated to obtain an initial first-order correlation function between them.

[0070] The initial first-order correlation function is statistically averaged to obtain the first-order correlation function between the communication signal and the pilot signal.

[0071] Specifically, a schematic diagram illustrating the correlation processing of communication signals and pilot signals using a correlation imaging algorithm is shown below. Figure 2 As shown.

[0072] The transmitting device sends signals and pilot signals to the receiving device, which are transmitted through the channel, and the receiving device receives the communication signals. Among them, X... P ∈P×P represents the pilot subcarrier transmission symbol, i.e., the pilot signal, Y. P ∈P×1 represents the pilot subcarrier receiving symbol, i.e., the communication signal processed by the receiving equipment.

[0073] Correlation processing is performed on the transmitted and received symbols of the pilot subcarriers to obtain the first-order correlation function between the pilot sequence and the processed communication signal, as shown in the following formula:

[0074]

[0075] In the above formula, G (1) X is the first-order correlation function between the pilot sequence and the processed communication signal. P Pilot signal, This represents the processed communication signal obtained from the i-th sample. This is the initial first-order correlation function. <> indicates the calculation of the statistical average. The calculation of the statistical average here is equivalent to normalizing the total number of samples. The range of the first-order correlation function obtained in this way is [0,1].

[0076] Step 103: Based on the first-order correlation function and the communication signal, obtain the channel response matrix of the target channel for transmitting the communication signal.

[0077] It should be noted that in this step, a channel model is first established using the processed communication signal Y and the known pilot signal.

[0078] Assuming there are P subcarriers used for pilot transmission, the channel transmission model is as follows:

[0079] Y p =H p X p +N

[0080] In the above formula, Y P ∈P×1 represents the pilot subcarrier received symbol, i.e., the processed communication signal; X P ∈P×P represents the pilot subcarrier transmission symbol, i.e., the pilot signal, and N represents noise.

[0081] It should be noted that the channel transmission model described above only represents the channel response matrix H. P There is a model relationship between the processed communication signal and the pilot signal, but it does not indicate that there is a simple linear relationship between the processed communication signal and the pilot signal, or between the processed communication signal and the channel response matrix.

[0082] Channel response matrix H P The specific solution process is as follows:

[0083] The observation matrix Φ is constructed using the first-order correlation function between the pilot signal and the processed communication signal; that is, the first-order correlation function is directly used as the observation matrix. Using: Y P =ΦH P Find the channel response matrix H P , where Y P This is the processed communication signal.

[0084] Solve using the least squares method: The channel response matrix is ​​finally obtained.

[0085] It should be noted that the least squares method is a classic channel estimation method used to estimate the channel response. In the least squares method, the channel estimate is obtained by minimizing the mean square error between the observed signal and the estimated signal.

[0086] Suppose we have a known training sequence / reference signal x, which is transmitted by the transmitter and received as an observed signal y after passing through the channel. The goal is to estimate the channel response (channel response matrix) h using the least squares method.

[0087] The following are the basic steps of least squares channel estimation:

[0088] Assume the channel response is h = [h1, h2, ..., h nThe training sequence x is transmitted through the channel to obtain the observed signal y. A system of linear equations is constructed: y = Hh + w, where H is the channel matrix of size m × n, m is the observed signal length, and w is the noise vector. The mean square error is minimized as follows: E = ||y - Hh|| 2 .

[0089] Solving the least squares problem: minimize E, that is, obtain h that minimizes E. The solution of the least squares method can be obtained by the following formula:

[0090] min||y-Hh|| 2

[0091] Here, H represents the channel response matrix, and x represents the transmitted symbol sequence. By solving the optimization problem, the optimal estimate of the channel response can be obtained.

[0092] Compared with existing channel estimation methods for processing sparse pilot information, this embodiment of the invention uses a correlation imaging algorithm to perform correlation processing on the received signal of sparse pilot information. The sparse pilot signal is used as a reference signal, the first-order correlation function between the reference signal and the received signal is calculated, the first-order correlation function is used to construct the observation matrix, and finally the least squares method is used to solve for the channel response.

[0093] Step 104: Using the channel response matrix, perform channel estimation on the target channel to obtain the channel estimation result of the target channel.

[0094] In this embodiment of the invention, the correlation imaging algorithm eliminates the need for complex preprocessing steps such as frequency offset compensation or symbol timing synchronization, simplifying the channel estimation process. Furthermore, the correlation imaging algorithm possesses high temporal resolution and the ability to effectively handle rapidly changing channel environments. Therefore, in ultra-high-capacity high-speed mobile systems, the correlation imaging algorithm can provide accurate channel estimation results through correlation analysis of pilot signals and received signals.

[0095] In multi-user systems, correlation imaging algorithms can achieve independent estimation of the channels of each user in the multi-user system by selecting appropriate sparse pilot signals and correlation analysis methods, thereby reducing mutual interference between users.

[0096] In an optional embodiment, at least one of channel attenuation, multipath effect, and phase difference estimation is performed using the channel response function, i.e., step 104 includes at least one of the following:

[0097] Using the time-domain information in the channel response matrix, channel attenuation estimation is performed on the target channel to obtain the channel attenuation estimation result of the target channel. Specifically, the attenuation coefficient A = |h(t)| is obtained by performing channel attenuation estimation, where h(t) represents the time-domain information in the channel response matrix.

[0098] Using the channel response matrix, the channel multipath effect of the target channel is estimated to obtain the channel attenuation multipath effect result of the target channel;

[0099] Using the frequency domain information in the channel response matrix, the channel phase difference of the target channel is estimated to obtain the channel phase difference result of the target channel. Specifically, arg(H(f)) can be used to estimate the phase difference caused by multipath effect, where H(f) is the frequency domain information in the channel response matrix.

[0100] Furthermore, regarding the multipath effect estimation, there are at least one of two schemes: using the channel response matrix to estimate the channel multipath effect of the target channel, and obtaining the channel attenuation multipath effect result of the target channel, including at least one of the following:

[0101] First, based on the time-domain information in the channel response matrix, the multipath effect of the target channel is estimated to obtain the multipath components of the target channel. Specifically, the multipath components are discovered by observing the time-domain graph of the channel response h(t) using the time-domain characteristic h(t) in the channel response matrix. The time delay differences between multipath components can be estimated by the time interval between peak values.

[0102] Second, perform Fourier transform processing on the time-domain information in the channel response matrix to obtain the frequency response information of the target channel. That is, use the frequency domain characteristics to perform Discrete Fourier Transform (DFT) on the time-domain information h(t) of the channel response to obtain the frequency response H(f).

[0103] Furthermore, the method also includes:

[0104] The communication signal is compensated based on the channel estimation result of the target channel, that is, the communication signal received by the receiving end is compensated based on the channel estimation result.

[0105] In some embodiments, the communication signal is compensated based on the channel estimation result of the target channel, including at least one of the following:

[0106] The amplitude of the communication signal is compensated for attenuation based on the channel attenuation estimation result. Specifically, if the estimated attenuation coefficient is A, the attenuation effect can be eliminated by compensating for the amplitude of the received communication signal. The compensated received communication signal y(n) can be calculated using the following formula:

[0107] y(n)=x(n) / A

[0108] Where x(n) represents the original received communication signal and y(n) represents the compensated received communication signal.

[0109] The total channel delay is determined based on the channel attenuation multipath effect results. Multipath effect compensation is then applied to the communication signal based on this total channel delay. Specifically, if the total delay caused by multipath effects estimated based on the channel attenuation multipath effect results is τ, the multipath effect can be compensated by delaying the received signal. The compensated received communication signal y(n) can be calculated using the following formula:

[0110] y(n)=x(n-τ)

[0111] Where x(n) represents the original received communication signal and y(n) represents the compensated received communication signal.

[0112] The signal phase difference is determined based on the channel phase difference result, and phase difference effect compensation is performed on the communication signal based on the signal phase difference. Specifically, if the phase difference estimated based on the channel phase difference result is... The phase difference effect can be compensated by performing a phase rotation operation on the received communication signal. The compensated received communication signal y(n) can be calculated using the following formula:

[0113]

[0114] Where x(n) represents the original received communication signal, y(n) represents the compensated received communication signal, and j represents the imaginary unit.

[0115] Compared with existing channel parameter estimation methods, the channel estimation method proposed in this embodiment of the invention is a channel estimation processing method based on sparse pilot sequences. After obtaining the channel estimation parameters, a feedback mechanism is established to compensate the receiver in real time and then demodulate the received signal.

[0116] The following is combined Figure 3 The specific process of the channel estimation method provided in the embodiments of the present invention will be described in detail.

[0117] The transmitting end sends a communication signal and a known pilot signal, and the receiving end receives the communication signal. The received communication signal and the known pilot signal are input into the correlation imaging algorithm for correlation processing to obtain a first-order correlation function. An observation matrix is ​​constructed based on the first-order correlation function, the channel response matrix is ​​solved, and the received communication signal is compensated based on the channel response matrix.

[0118] The channel estimation method provided in this embodiment of the invention has the following advantages:

[0119] 1. Low complexity: Correlation imaging algorithms do not require complex preprocessing steps such as frequency offset compensation or symbol timing synchronization, greatly simplifying the channel estimation process. Correlation imaging algorithms obtain direct channel parameter estimates by calculating correlations, providing more accurate channel information.

[0120] 2. Improve spectral efficiency: Correlation imaging algorithms can utilize the sparsity of pilot signals and achieve higher spectral efficiency by allocating pilot signals in the frequency domain.

[0121] 3. Reduce user interference: In multi-user systems, correlation imaging algorithms can achieve independent estimation of each user's channel by selecting appropriate sparse pilot signals and correlation analysis methods, thereby reducing mutual interference between users.

[0122] 4. High adaptability: This embodiment can dynamically adjust and optimize the design of the pilot signal according to changes in the channel environment, making it more adaptable to different channel conditions.

[0123] 5. High channel estimation accuracy: The correlation imaging algorithm has high temporal resolution and the ability to effectively handle rapidly changing channel environments. Therefore, in ultra-high-capacity high-speed mobile systems, the correlation imaging algorithm can provide accurate channel estimation results through correlation analysis of pilot signals and received signals.

[0124] like Figure 3 As shown, embodiments of the present invention also provide a channel estimation apparatus, applied to a first device, the apparatus comprising:

[0125] The acquisition module 301 is used to acquire the communication signal sent by the second device and to acquire the predetermined pilot signal;

[0126] The first processing module 302 is used to perform correlation processing on the communication signal and the pilot signal to obtain a first-order correlation function between the communication signal and the pilot signal;

[0127] The second processing module 303 is used to obtain the channel response matrix of the target channel for transmitting the communication signal based on the first-order correlation function, the pilot signal, and the communication signal.

[0128] The third processing module 304 is used to perform channel estimation on the target channel using the channel response matrix to obtain the channel estimation result of the target channel.

[0129] Optionally, the device further includes:

[0130] The fourth processing module is used to perform Fourier transform processing on the communication signal to obtain the processed communication signal.

[0131] Optionally, the first processing module 302 includes:

[0132] The first processing unit is used to perform correlation processing on the communication signal and the pilot signal to obtain an initial first-order correlation function between the communication signal and the pilot signal;

[0133] The second processing unit is used to perform statistical average calculation on the initial first-order correlation function to obtain the first-order correlation function between the communication signal and the pilot signal.

[0134] Optionally, the third processing module 304 includes: a third processing unit;

[0135] The third processing unit is used for at least one of the following:

[0136] Using the time-domain information in the channel response matrix, channel attenuation is estimated for the target channel to obtain the channel attenuation estimation result for the target channel;

[0137] Using the channel response matrix, the channel multipath effect of the target channel is estimated to obtain the channel attenuation multipath effect result of the target channel;

[0138] Using the frequency domain information in the channel response matrix, the channel phase difference of the target channel is estimated to obtain the channel phase difference result of the target channel.

[0139] Optionally, the third processing unit is specifically used for at least one of the following:

[0140] Based on the time-domain information in the channel response matrix, the multipath effect of the target channel is estimated to obtain the multipath component of the target channel.

[0141] The time-domain information in the channel response matrix is ​​subjected to Fourier transform processing to obtain the frequency response information of the target channel.

[0142] Optionally, the device further includes:

[0143] The compensation module is used to compensate the communication signal based on the channel estimation result of the target channel.

[0144] Optionally, the compensation module includes a compensation unit;

[0145] The compensation unit is used for at least one of the following:

[0146] The amplitude value of the communication signal is compensated for attenuation based on the channel attenuation estimation result;

[0147] The total channel delay is determined based on the channel attenuation multipath effect results, and multipath effect compensation is performed on the communication signal based on the total channel delay.

[0148] The signal phase difference is determined based on the channel phase difference result, and the communication signal is compensated for the phase difference effect based on the signal phase difference.

[0149] It should be noted that the channel estimation device provided in the embodiments of the present invention is a device capable of executing the above-described channel estimation method. Therefore, all embodiments of the above-described channel estimation method are applicable to this device and can achieve the same or similar technical effects.

[0150] like Figure 4 As shown, this embodiment of the invention also provides a communication device, including: a processor 401; and a memory 403 connected to the processor 401 via a bus interface 402, the memory 403 being used to store programs and data used by the processor 401 when performing operations, and the processor 401 calling and executing the programs and data stored in the memory 403.

[0151] The transceiver 404 is connected to the bus interface 402 and is used to receive and send data under the control of the processor 401. Specifically, the processor 401 is used to read the program in the memory 403 and execute the following processes:

[0152] Acquire the communication signals sent by the second device and acquire the predetermined pilot signals;

[0153] The communication signal and the pilot signal are correlated to obtain a first-order correlation function between them.

[0154] Based on the first-order correlation function and the communication signal, the channel response matrix of the target channel for transmitting the communication signal is obtained;

[0155] Using the channel response matrix, channel estimation is performed on the target channel to obtain the channel estimation result of the target channel.

[0156] Optionally, the processor 401 is further configured to:

[0157] The communication signal is subjected to Fourier transform processing to obtain the processed communication signal.

[0158] Optionally, the processor 401 is used to:

[0159] The communication signal and the pilot signal are correlated to obtain an initial first-order correlation function between them.

[0160] The initial first-order correlation function is statistically averaged to obtain the first-order correlation function between the communication signal and the pilot signal.

[0161] Optionally, the processor 401 is further configured to perform at least one of the following:

[0162] Using the time-domain information in the channel response matrix, channel attenuation is estimated for the target channel to obtain the channel attenuation estimation result for the target channel;

[0163] Using the channel response matrix, the channel multipath effect of the target channel is estimated to obtain the channel attenuation multipath effect result of the target channel;

[0164] Using the frequency domain information in the channel response matrix, the channel phase difference of the target channel is estimated to obtain the channel phase difference result of the target channel.

[0165] Optionally, the processor 401 is specifically used for at least one of the following:

[0166] Based on the time-domain information in the channel response matrix, the multipath effect of the target channel is estimated to obtain the multipath component of the target channel.

[0167] The time-domain information in the channel response matrix is ​​subjected to Fourier transform processing to obtain the frequency response information of the target channel.

[0168] Optionally, the processor 401 is further configured to:

[0169] The communication signal is compensated based on the channel estimation results of the target channel.

[0170] Optionally, the processor 401 is further specifically used for at least one of the following:

[0171] The amplitude value of the communication signal is compensated for attenuation based on the channel attenuation estimation result;

[0172] The total channel delay is determined based on the channel attenuation multipath effect results, and multipath effect compensation is performed on the communication signal based on the total channel delay.

[0173] The signal phase difference is determined based on the channel phase difference result, and the communication signal is compensated for the phase difference effect based on the signal phase difference.

[0174] Among them, Figure 4In this context, the bus architecture may include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 401) and memory (memory 403). The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides a user interface 405. A transceiver 404 may be multiple elements, including transmitters and receivers, providing units for communicating with various other devices over a transmission medium. Processor 401 is responsible for managing the bus architecture and general processing, and memory 403 may store data used by processor 401 during operation.

[0175] In addition, specific embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps in the channel estimation method as described above.

[0176] In the several embodiments provided in this application, it should be understood that the disclosed methods and apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units 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; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0177] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can be physically comprised separately, or two or more units can be integrated into one unit. The integrated unit described above can be implemented in hardware or in the form of hardware plus software functional units.

[0178] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions that cause a computer device (which may be a personal computer, server, or network device, etc.) to execute some steps of the transmission and reception methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0179] The above describes the 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 principles 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 estimation method, characterized in that, Applied to a first device, the method includes: Acquire the communication signals sent by the second device and acquire the predetermined pilot signals; The communication signal and the pilot signal are correlated to obtain a first-order correlation function between them. Based on the first-order correlation function and the communication signal, the channel response matrix of the target channel for transmitting the communication signal is obtained; Using the channel response matrix, channel estimation is performed on the target channel to obtain the channel estimation result of the target channel.

2. The method according to claim 1, characterized in that, Before performing correlation processing on the communication signal and the pilot signal to obtain the first-order correlation function between the communication signal and the pilot signal, the method further includes: The communication signal is subjected to Fourier transform processing to obtain the processed communication signal.

3. The method according to claim 1, characterized in that, The communication signal and the pilot signal are correlated to obtain a first-order correlation function between them, including: The communication signal and the pilot signal are correlated to obtain an initial first-order correlation function between them. The initial first-order correlation function is statistically averaged to obtain the first-order correlation function between the communication signal and the pilot signal.

4. The method according to claim 1, characterized in that, Using the channel response matrix, channel estimation is performed on the target channel to obtain the channel estimation result of the target channel, including at least one of the following: Using the time-domain information in the channel response matrix, channel attenuation is estimated for the target channel to obtain the channel attenuation estimation result for the target channel; Using the channel response matrix, the channel multipath effect of the target channel is estimated to obtain the channel attenuation multipath effect result of the target channel; Using the frequency domain information in the channel response matrix, the channel phase difference of the target channel is estimated to obtain the channel phase difference result of the target channel.

5. The method according to claim 4, characterized in that, Using the channel response matrix, the target channel is estimated for multipath effects, yielding the channel attenuation multipath effect results, including at least one of the following: Based on the time-domain information in the channel response matrix, the multipath effect of the target channel is estimated to obtain the multipath component of the target channel. The time-domain information in the channel response matrix is ​​subjected to Fourier transform processing to obtain the frequency response information of the target channel.

6. The method according to claim 4, characterized in that, The method further includes: The communication signal is compensated based on the channel estimation results of the target channel.

7. The method according to claim 6, characterized in that, Based on the channel estimation results of the target channel, the communication signal is compensated, including at least one of the following: The amplitude value of the communication signal is compensated for attenuation based on the channel attenuation estimation result; The total channel delay is determined based on the channel attenuation multipath effect results, and multipath effect compensation is performed on the communication signal based on the total channel delay. The signal phase difference is determined based on the channel phase difference result, and the communication signal is compensated for the phase difference effect based on the signal phase difference.

8. A channel estimation device, characterized in that, Applied to a first device, the device includes: The acquisition module is used to acquire the communication signals sent by the second device and to acquire the predetermined pilot signals; The first processing module is used to perform correlation processing on the communication signal and the pilot signal to obtain a first-order correlation function between the communication signal and the pilot signal; The second processing module is used to obtain the channel response matrix of the target channel for transmitting the communication signal based on the first-order correlation function and the communication signal. The third processing module is used to perform channel estimation on the target channel using the channel response matrix to obtain the channel estimation result of the target channel.

9. A communication device, characterized in that, include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the channel estimation method as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that, The readable storage medium stores a program that, when executed by a processor, implements the steps of the channel estimation method as described in any one of claims 1 to 7.

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