BEM channel estimation method for simplifying windowing

Abstract

The invention discloses a simplified windowing BEM channel estimation method, and belongs to the technical field of digital information transmission. The method comprises the following steps: converting a local pilot symbol into a transform domain to obtain a transform domain matrix; generating a primary function frequency domain matrix and a window function frequency domain matrix based on the complex exponential basis expansion model; according to the primary function frequency domain matrix, obtaining a block diagonal matrix used for estimating a primary coefficient, and further obtaining a frequency domain matrix used for estimating the primary coefficient; performing fast Fourier transform on the time domain receiving signal to obtain a frequency domain signal, and extracting a pilot frequency cluster symbol of a frequency domain from the frequency domain signal; estimating a basis coefficient vector; and directly obtaining the frequency domain channel estimation response of each OFDM symbol by using the estimated basis coefficient vector. According to the method, the BEM-based channel estimation method can be simplified, and the channel estimation precision is not lost.

Description

Technical Field

[0001] The invention belongs to the technical field of digital information transmission, and in particular relates to a simplified windowed BEM channel estimation method. Background Art

[0002] At present, high-speed railway, as an important application scenario of wireless communication, has been included in the performance indicators of the Fifth Generation (5G). At the same time, the communication standard released by the Third Generation Partnership Project (3GPP) in June 2018 confirmed that OFDM based on cyclic prefix (CP) is still used as the physical layer modulation technology. OFDM is still one of the indispensable key technologies in high-speed mobile communication. In high-speed mobile environment, multipath leads to frequency selective fading of the channel. At the same time, the large Doppler frequency spread caused by the relative movement of the transmitter and receiver will also seriously destroy the orthogonality between OFDM subcarriers, causing the channel to change dramatically and nonlinearly within an OFDM symbol, resulting in time selective fading. The dual-selective characteristics of the channel will seriously affect the estimation accuracy of the channel and the performance of the OFDM system. Therefore, accurate estimation of the dual-selective channel of the OFDM system is one of the research hotspots in the communication field.

[0003] The basis expansion model channel estimation algorithm uses a linear combination of finite basis functions to approximate the changes of the dual-selective channel, improves the estimation accuracy of the dual-selective channel, and can be well applied to communication systems under the action of dual-selective channels. MKTsatsanis et al. first proposed the use of the CE-BEM model to fit the time-varying characteristics of the channel in 1996. This model samples the Doppler power spectrum by equating the basis function to the impulse function. During sampling, a larger base frequency will cause the Gibbs effect, resulting in a larger modeling error. To address this problem, a generalized basis expansion model (GCE-BEM) was proposed. The sampling rate was increased by adding an oversampling factor to improve the modeling accuracy. Later, Zemen T and other scholars proposed suboptimal DKL-BEM and DPS-BEM models based on Doppler power spectrum statistical information, using the eigenvector of the channel autocorrelation function as the basis function vector, with better fitting performance. In order to reduce the impact of ICI, the BEM-based channel estimation algorithm usually uses pilot clustering and time domain windowing methods to perform a series of processing on the transmitted and received signals to reduce the interference of ICI on the channel estimation results, thereby improving the accuracy of channel time fitting. However, the algorithm involves numerous large matrix operations, resulting in high computational complexity and is not easy to implement.

[0004] In summary, existing channel estimation methods have high computational complexity, are difficult to implement, and have low accuracy, making them difficult to use in practical applications. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention provides a simplified windowed BEM channel estimation method. The present invention can simplify the BEM-based channel estimation method without losing the channel estimation accuracy.

[0006] The technical solution adopted by the present invention is:

[0007] A simplified windowed BEM channel estimation method is applied to a communication receiving end, and is used to perform BEM channel estimation according to a signal sent by a transmitting end. There is relative motion between the receiving end and the transmitting end, wherein the transmitting end signal adopts an OFDM signal with a scattered pilot structure inserted. The method comprises the following steps:

[0008] Step 1: Transform the local pilot symbol X P Convert to the transform domain and get the transform domain matrix S P ;

[0009] Step 2: Generate the basis function frequency domain matrix D based on the complex exponential basis expansion model q And the frequency domain matrix Θ of the window function P ;

[0010] Step 3: Based on the basis function frequency domain matrix D generated in step 2 q , get the block diagonal matrix D used to estimate the basis coefficients P , D P and S P Multiply to get the matrix Γ, find the pseudo-inverse of the matrix Γ Then and θ P Multiply them together to get the frequency domain matrix Mat used to estimate the base coefficients R ;

[0011] Step 4: Perform a fast Fourier transform on the time domain received signal y to obtain a frequency domain signal Y, and extract the frequency domain pilot cluster symbol Y from the frequency domain signal Y. P ;

[0012] Step 5: According to the pilot cluster symbol Y P and matrix Mat R , estimate the basis coefficient vector g′;

[0013] Step 6: Use the estimated basis coefficient vector g′ to directly obtain the frequency domain channel estimation response of each OFDM symbol

[0014] Furthermore, the specific method of step one is:

[0015] (1) Determine the number of basis functions Q of the complex exponential basis expansion model:

[0016]

[0017] Where c is the speed of light, f is the carrier frequency, v is the speed of the receiver relative to the transmitter, and T s is the signal sampling period, It is a round-up operation;

[0018] (2) Calculate the transform domain matrix S P :

[0019]

[0020] Among them, the transformation domain matrix S P The dimension is N(Q+1)×L(Q+1), diag() is an operation that converts a vector into a diagonal matrix. m is the number of the pilot cluster, which is arranged in a discrete pattern, Q is the number of basis functions, and I 2Q+1 is the identity matrix of 2Q+1 dimensions; is the Krone product symbol; are the first L columns of the N-point fast Fourier transform matrix F, L is the maximum multipath number of the channel, and its value is the cyclic prefix length.

[0021] Furthermore, the specific method of step 2 is:

[0022] (1) Generate basis function frequency domain matrix D q :

[0023] D q =Fdiag(b q (n))F H

[0024] Where F is the fast Fourier transform matrix, F H is the inverse fast Fourier transform matrix;

[0025] Basis function b q (n) is in the following form:

[0026]

[0027] Among them, N total =M(N f +N g ), N f The number of subcarriers per OFDM symbol, N g is the cyclic prefix length and N g ≥L, L is the maximum multipath number of the channel, M is the number of OFDM symbols, Q d is the order of the basis expansion model;

[0028] (2) Generate the frequency domain matrix Θ of the window function P :

[0029]

[0030] in, It is a 1×(2D+1)-dimensional vector consisting of the elements from the pDth to the p+Dth columns in the pth row of the frequency domain matrix Θ of the Hanning window function, where D is the length of the guard sideband.

[0031] Furthermore, the specific method of step three is:

[0032] (1) The frequency domain matrix D of the basis function q Divided into M blocks D m,q , m=1,2,...,M, for each block D m,q , extract the pilot cluster (D m,q ) p , if the position of the pth non-zero pilot in the mth OFDM subcarrier is i m , then (D m,q ) p Take D m,q No. i m The i-th row m -1 to i m +1 column, that is:

[0033] (D m,q ) p =[D m,q (i m ,i m -1),D m,q (i m ,i m ),D m,q (i m ,i m +1)];

[0034] Wherein, p=1,2,...,P, P is the pilot position;

[0035] (2) Take the pilot cluster (D) of the mth OFDM symbol m,q ) p Arrange them on the diagonal in order to form a block diagonal matrix As follows:

[0036]

[0037] (3) Q The matrices are arranged in order to obtain the column block matrix corresponding to the mth block of OFDM symbols

[0038] (4) Arrange them on the diagonal in order to obtain the block diagonal matrix D used to estimate the basis coefficients P :

[0039]

[0040] (5) Let Γ = D P S P , get the frequency domain matrix Mat used to estimate the basis coefficients R :

[0041]

[0042] in, is the pseudo-inverse of Γ.

[0043] Furthermore, the specific method of step four is:

[0044] (1) Perform fast Fourier transform on the time domain received signal y to obtain the frequency domain received signal Y:

[0045] Y=Fy

[0046] Where F is the N-point fast Fourier transform matrix;

[0047] (2) Extracting the pilot cluster symbol Y from the frequency domain received signal Y P ,The pilot cluster position index is determined by the transmitter and stored at the receiver.

[0048] Furthermore, the basis coefficient vector g′ in step 5 is calculated as follows:

[0049] g′=Mat R Y P

[0050] Furthermore, the specific method of step six is:

[0051] (1) For the frequency domain matrix D of the basis function in step 2 q , only keep D q The main diagonal of the matrix, B subdiagonals and B superdiagonals form a band matrix (D B ) q , B is half of the BEM order;

[0052] (2) Based on the basis coefficient vector g′ in step 2, calculate the frequency domain channel estimation response of the OFDM symbol

[0053]

[0054] Among them, g′ q is the qth element in g′, F L g′ q is for g′ q Find the N-point fast Fourier transform, diag() is an operation that converts a vector into a diagonal matrix.

[0055] The beneficial effects of the present invention are:

[0056] 1. The present invention selects a complex exponential basis expansion model with the smallest model error and determines the optimal number of basis functions to improve the estimation accuracy; the frequency domain matrix used to estimate the basis coefficients and the frequency domain matrix of the window function are calculated and stored in advance, thereby reducing the computational complexity; the mathematical relationship between the basis coefficients and the frequency domain channel response matrix is ​​derived by utilizing the time-frequency domain characteristics of the channel, thereby avoiding the channel time-frequency domain conversion process with high computational complexity, and facilitating the receiver to perform frequency domain equalization processing on the received signal.

[0057] 2. The simulation results show that the bit error rate (BER) performance of the BEM-based channel estimation algorithm is much better than that of the traditional DFT+linear interpolation algorithm. The simplified BEM receiver windowing method proposed has only slightly reduced the signal-to-noise ratio performance compared to the unsimplified method while greatly reducing the complexity. -3 When , both of them improve the performance by more than 2dB compared with the BEM channel estimation method without windowing at the receiving end. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0059] Figure 1 It is a flow chart of a simplified windowed BEM channel estimation method provided by an embodiment of the present invention.

[0060] Figure 2 4 is a schematic diagram of the windowing system structure provided in an embodiment of the present invention.

[0061] Figure 3 It is a schematic diagram comparing the computational complexity of the existing channel estimation algorithm provided by the embodiment of the present invention when the normalized maximum Doppler shift is 0.2 and the PDP obeys the EVA channel.

[0062] Figure 4It is a schematic diagram of MSE under the EVA channel when the normalized maximum Doppler shift provided by the embodiment of the present invention and the existing channel estimation algorithm is 0.2 and the PDP obeys.

[0063] Figure 5 It is a schematic diagram of a bit error rate curve under the condition that PDP obeys EVA channel when the normalized maximum Doppler shift is 0.2 provided by an embodiment of the present invention and an existing channel estimation algorithm. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0065] A simplified windowed BEM channel estimation method is applied to a communication receiving end to perform BEM channel estimation based on a signal sent by a transmitting end, wherein the receiving end and the transmitting end have relative motion, wherein the transmitting end signal uses an OFDM signal with a scattered pilot structure inserted. Figure 1 As shown, the method comprises the following steps:

[0066] S101: local pilot symbol X P Convert to the transform domain and get the transform domain matrix S P ;

[0067] S102: Generate a basis function frequency domain matrix D based on the complex exponential basis expansion model q And the frequency domain matrix Θ of the window function P ;

[0068] S103: Based on the basis function frequency domain matrix D generated in step 2 q , get the block diagonal matrix D used to estimate the basis coefficients P , D P and S P Multiply to get the matrix Γ, find the pseudo-inverse of the matrix Γ Then and θ P Multiply them together to get the frequency domain matrix Mat used to estimate the base coefficients R ;

[0069] S104: Perform a fast Fourier transform on the time domain received signal y to obtain a frequency domain signal Y, and extract the frequency domain pilot cluster symbol Y from the frequency domain signal Y P ;

[0070] S105: According to the pilot cluster symbol Y P and matrix Mat R , estimate the basis coefficient vector g′;

[0071] S106: Using the estimated basis coefficient vector g′, directly obtain the frequency domain channel estimation response of each OFDM symbol

[0072] like Figure 2 As shown, the simplified windowed BEM channel estimation system implemented based on the above method includes:

[0073] Domain transformation calculation module 1, used to transform the local pilot symbol X P Convert to the transform domain and get the transform domain matrix S P ;

[0074] Matrix storage module 2, used for storing the window function frequency domain matrix and base coefficient estimation frequency domain matrix of the receiving end channel estimation in advance;

[0075] Fast Fourier transform module 3, used for the receiving end to perform fast Fourier transform on the time domain received signal y to obtain the frequency domain signal Y, and extract the frequency domain pilot cluster symbol Y from the frequency domain signal Y P ;

[0076] The base coefficient estimation module 4 is used to receive the pilot cluster symbol Y P And the basis coefficient estimation frequency domain matrix Mat R Estimate the basis coefficient vector;

[0077] The channel frequency domain matrix construction module 5 is used to directly obtain the frequency domain channel estimation response of each OFDM symbol using the estimated basis coefficient vector.

[0078] The following is a specific embodiment:

[0079] A simplified windowed BEM channel estimation method specifically comprises the following steps:

[0080] Step 1:

[0081] (1) Determine the number of basis functions Q of the complex exponential basis expansion model:

[0082]

[0083] Where c is the speed of light, f is the carrier frequency, v is the speed of the receiver relative to the transmitter, and T s is the signal sampling period, It is a round-up operation;

[0084] (2) Obtaining the transform domain matrix S P The receiving end converts the local pilot symbol X P Convert to the transform domain and get the transform domain matrix S P :

[0085]

[0086] Among them, the transformation domain matrix S P The dimension is N(Q+1)×L(Q+1), X P =diag(X pm ), diag() is an operation that converts a vector into a diagonal matrix, p m is the serial number of the pilot cluster, the pilot clusters are arranged in a discrete pattern, Q is the number of optimal basis functions, I 2Q+1 is the identity matrix of 2Q+1 dimensions; is the Krone product symbol; It is the first L columns of the N-point fast Fourier transform FFT matrix F, L is the maximum multipath number of the channel, and is generally set to the CP (cyclic prefix) length.

[0087] Step 2:

[0088] (1) Generate basis function frequency domain matrix D q :

[0089] D q =Fdiag(b q (n))F H

[0090] Where F is the Fourier transform matrix, F H is the inverse Fourier transform matrix.

[0091] Basis function b q (n) is in the following form:

[0092]

[0093] Among them, N total =M(N f +N g ), N f The number of subcarriers per OFDM symbol, N g is the cyclic prefix length and N g ≥L, L is the maximum multipath number of the channel, M is the number of OFDM symbols, Q d is the order of the basis expansion model;

[0094] (2) Generate the window function frequency domain matrix Θ P :

[0095]

[0096] in, It is a 1×(2D+1)-dimensional vector consisting of the elements from the pDth to the p+Dth columns in the pth row of the frequency domain matrix Θ of the Hanning window function, where D is the length of the guard sideband.

[0097] Step 3:

[0098] (1) Calculate the basis coefficient estimation matrix D P :

[0099] The frequency domain matrix D of the basis function q Divided into M blocks D m,q , the pilot positions of different OFDM symbols are respectively divided into (D m,q ) p If the position of the pth non-zero pilot in the m-block OFDM subcarrier is i m , then (D m,q ) p Take D m,q The i m The i-th row m -1 to i m +1 column, that is:

[0100] (D m,q ) p =[D m,q (i m ,i m -1),d m,q (i m ,i m ),D m,q (i m ,i m +1)];

[0101] The pilot cluster (D m,q ) p Arrange them on the diagonal in order to construct a block diagonal matrix denoted as As follows:

[0102]

[0103] Q The matrices are arranged in order to obtain the column block matrix corresponding to the mth block of OFDM symbols

[0104] Will Arrange to obtain the final block diagonal matrix used to estimate the basis coefficients:

[0105]

[0106] (2) Let Γ = D P S P , get the frequency domain matrix Mat used to estimate the basis coefficients R :

[0107]

[0108] in, is the pseudo-inverse of Γ.

[0109] Step 4:

[0110] (1) Perform fast Fourier transform on the time domain received signal y to obtain the frequency domain received signal Y:

[0111] Y=Fy

[0112] Where F is the N-point fast Fourier transform matrix;

[0113] (2) Extracting the pilot cluster symbol Y from the frequency domain received signal Y P ,The pilot cluster position index is determined by the transmitter and stored at the receiver.

[0114] Step 5:

[0115] g′=Mat R Y P

[0116] Step 6: Calculate the frequency domain channel estimation response matrix, including:

[0117] (1) For the frequency domain matrix D of the basis function in step 2 q , only keep D q The main diagonal of the matrix, B subdiagonals and B superdiagonals form a band matrix (D B ) q , B is half of the BEM order.

[0118] (2) Calculate the frequency domain channel estimation response of the OFDM symbol according to the base coefficient g′ in step 2:

[0119]

[0120] Among them, F L g′ q is for g′ q Find the N-point fast Fourier transform, diag() is an operation that converts a vector into a diagonal matrix.

[0121] The method is simulated below. The processing in step S101 and step S102 can be performed by FPGA using hardware resources such as DSP to calculate in real time and store the required information; the matrix in step S103 All elements of are known, so they can be calculated in advance using software such as MATLAB and stored in the ROM resources in the hardware to avoid complex matrix operations to meet the real-time requirements of hardware processing; step S104 uses the results calculated in step S103 to calculate the final channel frequency domain response matrix. Therefore, in theory, this method can be implemented through programmable logic.

[0122] 1. Simulation conditions

[0123] The system used in the performance simulation is the CP-OFDM transmission system specified by the 3GPP standard, with a system bandwidth of 20MHz, a maximum Doppler shift of 3KHz, 2048 subcarriers, 15KHz between subcarriers, QPSK modulation, Turbo coding, BEM order of 5, oversampling factor of 3, number of OFDM symbols of 4, and pilot insertion format of discrete pilot clusters using guard sidebands. The simulated channel PDP distribution is the extended vehicular channel model EVA in 3GPP, the multipath channel delay is [0, 130.1, 390.4, 520.5, 910.9, 1301.3] ns, and the corresponding power attenuation of each path is [-3.8515, -3.0972, -15.2762, -13.1762, -18.1762, -23.0762] dB.

[0124] 2. Simulation content and results

[0125] The performance simulation mainly compares the performance of two algorithms:

[0126] 1) Unsimplified BEM channel estimation method based on receiving end windowing.

[0127] 2) The method of the present invention (simplified windowed BEM channel estimation method).

[0128] The computational complexity analysis when the normalized maximum Doppler shift is 0.2 is as follows: Figure 3 As shown, the MSE curve and system bit error rate performance simulation are as follows Figure 4 and Figure 5 Compared with the unsimplified multi-symbol BEM channel estimation method based on receiving-end windowing, the simplified windowed BEM channel estimation method of the present invention has a significantly reduced computational complexity with a slight loss in performance.

[0129] In summary, the present invention calculates and stores the corresponding frequency domain matrix and the basis coefficient estimation matrix of the windowed receiving end in advance, estimates the basis coefficient; derives the mathematical relationship between the basis coefficient and the frequency domain channel response matrix, estimates the frequency domain channel response of the received signal and retains the effective bandwidth of the matrix according to the channel characteristics. The present invention selects a complex exponential basis expansion model with the smallest model error, determines the optimal number of basis functions, and improves the estimation accuracy; calculates and stores the frequency domain matrix used to estimate the basis coefficient and the frequency domain matrix of the window function in advance, reducing the computational complexity; utilizes the time-frequency domain characteristics of the channel, derives the mathematical relationship between the basis coefficient and the frequency domain channel response matrix, avoids the channel time-frequency domain conversion process with high computational complexity, and facilitates the receiver to perform frequency domain equalization processing on the received signal.

[0130] The present invention can be used in wireless communication systems under high-speed mobility in the 5G standard, for example, to ensure reliable wireless communication services in the high-speed mobility environment of high-speed railways, to be used in vehicle networking systems to provide a reliable data transmission basis for intelligent driving, and to be used in various application scenarios such as underwater acoustic communication technology.

[0131] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0132] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A simplified windowed BEM channel estimation method, characterized in that: The method is applied to a communication receiving end and is used to perform BEM channel estimation according to a signal sent from a transmitting end. There is relative motion between the receiving end and the transmitting end, wherein the transmitting end signal adopts an OFDM signal with a scattered pilot structure inserted. The method comprises the following steps: Step 1: Transform the local pilot symbol X P Convert to the transform domain and get the transform domain matrix S P ; Step 2: Generate the basis function frequency domain matrix D based on the complex exponential basis expansion model q And the frequency domain matrix Θ of the window function P ; Step 3: Based on the basis function frequency domain matrix D generated in step 2 q , get the block diagonal matrix D used to estimate the basis coefficients P , D P and S P Multiply to get the matrix Γ, find the pseudo-inverse of the matrix Γ Then and θ P Multiply them together to get the frequency domain matrix Mat used to estimate the base coefficients R ; Step 4: Perform a fast Fourier transform on the time domain received signal y to obtain a frequency domain signal Y, and extract the frequency domain pilot cluster symbol Y from the frequency domain signal Y. P ; Step 5: According to the pilot cluster symbol Y P and matrix Mat R , estimate the basis coefficient vector g′; Step 6: Use the estimated basis coefficient vector g′ to directly obtain the frequency domain channel estimation response of each OFDM symbol 2. A simplified windowed BEM channel estimation method as claimed in claim 1, characterized in that: The specific method of step one is: (1) Determine the number of basis functions Q of the complex exponential basis expansion model: Where c is the speed of light, f is the carrier frequency, v is the speed of the receiver relative to the transmitter, and T s is the signal sampling period, It is a round-up operation; (2) Calculate the transform domain matrix S P : Among them, the transformation domain matrix S P The dimension is N(Q+1)×L(Q+1), diag() is an operation that converts a vector into a diagonal matrix. m is the number of the pilot cluster, which is arranged in a discrete pattern, Q is the number of basis functions, and I 2Q+1 is the identity matrix of 2Q+1 dimensions; is the Krone product symbol; are the first L columns of the N-point fast Fourier transform matrix F, L is the maximum multipath number of the channel, and its value is the cyclic prefix length.

3. A simplified windowed BEM channel estimation method as claimed in claim 2, characterized in that: The specific method of step 2 is: (1) Generate basis function frequency domain matrix D q : D q =Fdiag(b q (n))F H Where F is the fast Fourier transform matrix, F H is the inverse fast Fourier transform matrix; Basis function b q (n) is in the following form: Among them, N total =M(N f +N g ), N f The number of subcarriers per OFDM symbol, N g is the cyclic prefix length and N g ≥L, L is the maximum multipath number of the channel, M is the number of OFDM symbols, Q d is the order of the basis expansion model; (2) Generate the frequency domain matrix Θ of the window function P : in, It is a 1×(2D+1)-dimensional vector consisting of the elements from the pDth to the p+Dth columns in the pth row of the frequency domain matrix Θ of the Hanning window function, where D is the length of the guard sideband.

4. A simplified windowed BEM channel estimation method as claimed in claim 3, characterized in that: The specific method of step three is: (1) The frequency domain matrix D of the basis function q Divided into M blocks D m,q , m=1,2,...,M, for each block D m,q , extract the pilot cluster (D m,q ) p , if the position of the pth non-zero pilot in the mth OFDM subcarrier is i m , then (D m,q ) p Take D m,q No. i m The i-th row m -1 to i m +1 column, that is: (D m,q ) p =[D m,q (i m ,i m -1),D m,q (i m ,i m ),D m,q (i m ,i m +1)]; Wherein, p=1,2,...,P, P is the pilot position; (2) Take the pilot cluster (D) of the mth OFDM symbol m,q ) p Arrange them on the diagonal in order to form a block diagonal matrix As follows: (3) Q The matrices are arranged in order to obtain the column block matrix corresponding to the mth block of OFDM symbols (4) Arrange them on the diagonal in order to obtain the block diagonal matrix D used to estimate the basis coefficients P : (5) Let Γ = D P S P , get the frequency domain matrix Mat used to estimate the basis coefficients R : in, is the pseudo-inverse of Γ.

5. A simplified windowed BEM channel estimation method as claimed in claim 4, characterized in that: The specific method of step 4 is: (1) Perform fast Fourier transform on the time domain received signal y to obtain the frequency domain received signal Y: Y=Fy Where F is the N-point fast Fourier transform matrix; (2) Extracting the pilot cluster symbol Y from the frequency domain received signal Y P ,The pilot cluster position index is determined by the transmitter and stored at the receiver.

6. A simplified windowed BEM channel estimation method as claimed in claim 5, characterized in that: The calculation method of the basis coefficient vector g′ in step 5 is: g′=Mat R Y P 7. A simplified windowed BEM channel estimation method as claimed in claim 6, characterized in that: The specific method of step six is: (1) For the frequency domain matrix D of the basis function in step 2 q , only keep D q The main diagonal of the matrix, B subdiagonals and B superdiagonals form a band matrix (D B ) q , B is half of the BEM order; (2) Based on the basis coefficient vector g′ in step 2, calculate the frequency domain channel estimation response of the OFDM symbol Among them, g′ q is the qth element in g′, F L g′ q is for g′ q Find the N-point fast Fourier transform, diag() is an operation that converts a vector into a diagonal matrix.

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