Time domain channel estimation method and device based on multi-antenna diversity technology

Abstract

The invention discloses a time domain channel estimation method and device based on a multi-antenna diversity technology, and the method comprises the steps: mapping a bit stream into a symbol stream to construct a transmitting signal matrix, carrying out the STBC coding, inserting a time domain pilot signal, obtaining a matrix after insertion, selecting a vector in the matrix after insertion, and carrying out the time domain channel estimation. The method comprises the steps of obtaining a frequency domain receiving signal by combining with relevant parameters of Gaussian white noise and a Rayleigh fading channel, then performing matrix transformation to obtain an STBC decoding matrix, introducing a weight matrix to update an established MIMO-OFDM system input and output model to obtain a time domain channel estimation value, and finally performing FFT transformation on the time domain channel estimation value to obtain a frequency domain channel matrix estimation value. And substituting the frequency domain channel matrix estimation value into the STBC decoding matrix and then carrying out ML signal detection to obtain a final estimation value. A channel estimation algorithm is introduced to improve the estimation of a dynamic channel, a space-time block coding technology is combined to improve the demodulation signal precision of a MIMO system, and the full-state channel of the MIMO system is estimated.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a time domain channel estimation method and device based on multi-antenna diversity technology. Background Art

[0002] In the Additive White Gaussian Noise (AWGN) channel, when the signal-to-noise ratio (SNR) increases, the slope of the theoretical bit error rate (BER) curve approaches infinity, showing a cliff-like drop. In the Rayleigh fading channel, the slope of the BER curve is linear. Even if the system works in a good communication environment, the performance of the communication system is significantly limited after passing through the Rayleigh channel. For wireless communication environments, the channel is dynamically changing and unpredictable. The noise caused by AWGN is additive. Unlike additive noise, fading is caused by non-additive interference. Wireless signals are transmitted through different paths, and eventually irregular disturbances appear after superposition at the receiving end.

[0003] A Multiple-Input Multiple-Output (MIMO) system usually has multiple transmit antennas and multiple receive antennas. The receive antennas will receive transmit signals output by different transmit antennas, which causes receive signal aliasing and makes it impossible to distinguish the desired receive signals. Summary of the invention

[0004] The purpose of this application is to overcome the existing technical defects and provide a time domain channel estimation method and device based on multi-antenna diversity technology, introduce a channel estimation algorithm to improve the estimation of dynamic channels, combine space-time block coding technology to improve the demodulation signal accuracy of multiple-input multiple-output systems, and estimate the full-state channel of MIMO systems.

[0005] The purpose of this application is achieved through the following technical solutions:

[0006] In a first aspect, the present application proposes a time domain channel estimation method based on multi-antenna diversity technology, which is applied to a MIMO system. The method includes:

[0007] Mapping the bit stream into a symbol stream to construct a transmission signal matrix, performing STBC encoding on the transmission signal matrix and inserting a time domain pilot signal to obtain an inserted matrix;

[0008] Select the vector in the inserted matrix, combine it with the relevant parameters of Gaussian white noise and Rayleigh fading channel to obtain the frequency domain received signal, and then perform matrix transformation to obtain the STBC decoding matrix;

[0009] The weight matrix is ​​introduced to update the input and output model of the established MIMO-OFDM system to obtain the time domain channel estimation value;

[0010] The time domain channel estimation value is transformed by FFT to obtain the frequency domain channel matrix estimation value, and the frequency domain channel matrix estimation value is substituted into the STBC decoding matrix and then ML signal detection is performed to obtain the final estimation value.

[0011] In a possible implementation, the steps of mapping a bit stream into a symbol stream to construct a transmit signal matrix, performing STBC encoding on the transmit signal matrix, and inserting a time domain pilot signal to obtain an inserted matrix include:

[0012] Convert the bitstream x b Mapping to symbol stream Use the symbol stream to construct the OFDM transmit signal matrix X t :

[0013] For the transmitted signal matrix X t Perform STBC encoding to obtain the encoding matrix A:

[0014] The transmission signal matrix is ​​extracted from the coding matrix by column, and then the coded matrix is ​​obtained through the coding matrix;

[0015] The time domain pilot signals of multiple OFDM symbols are uniformly inserted into the encoded matrix to obtain an inserted matrix.

[0016] In a possible implementation, the steps of selecting a vector in the inserted matrix, combining it with relevant parameters of Gaussian white noise and Rayleigh fading channel to obtain a frequency domain received signal, and then performing matrix transformation to obtain an STBC decoding matrix include:

[0017] Select the vector in the inserted matrix and combine it with the relevant parameters of Gaussian white noise and Rayleigh fading channel to obtain the frequency domain received signal:

[0018]

[0019] Where [h1,h2,h3,h4] are the relevant parameters of the Rayleigh fading channel, [z1,z2,z3,z4,z5,z6,z7,z8] have a mean of 0 and a variance of σ 2 Gaussian white noise;

[0020] Perform matrix transformation on the frequency domain received signal to obtain the STBC decoding matrix:

[0021] In a possible implementation manner, the step of introducing a weight matrix to update the established MIMO-OFDM system input and output model to obtain a time domain channel estimation value includes:

[0022] Establish MIMO-OFDM system input and output model: R i =X p H i +Z i , where R i =[r i [0],r i [1],...,r i [N-1]] T represents the pilot received signals of different subcarriers of the ith antenna, r i [n], n = 1, 2, ..., N-1 represents the pilot element on the nth subcarrier of the ith antenna, represents the pilot signal matrix, where j = 1, 2, ..., N Tx , x j =[x j [1],x j [2],...,x j [N-1]] T represents the pilot signal vector transmitted on the jth antenna, x j [n] represents the pilot element on the nth subcarrier on the jth antenna, n = 1, 2, ..., N, represents the full-state frequency domain channel matrix of the MIMO system, H j,i =[H j,i [1],H j,i [2],...,H j,i [n]] T represents the frequency domain channel vector corresponding to the jth transmitting antenna and the ith receiving antenna, H j,i [n] represents the frequency domain channel element on the nth subcarrier in the frequency domain channel vector corresponding to the jth transmitting antenna and the ith receiving antenna, Z i =[z i [1],z i [1],...,z i [n]] T represents the Gaussian white noise matrix of the ith antenna, z i [n] represents the Gaussian white noise element on the nth subcarrier of the i-th antenna;

[0023] Set the number of multipath channels to L and denote the time domain channel vector as h j,i =[h j,i [1],h j,i [2],...,h j,i[L-1]] T ,h j,i [l] represents the time domain channel element between the jth transmitting antenna and the ith receiving antenna, l = 1, 2, ..., L, and there is a conversion relationship between the time domain channel vector and the frequency domain channel vector: H j,i =F L h j,i , F L Represents the discrete Fourier transform matrix with dimension L×L.

[0024] The weight matrix W is used to update the input and output model of the MIMO-OFDM system:

[0025] R i =Wh i +Z i

[0026]

[0027] Get the time domain channel estimate:

[0028] In one possible implementation, the final estimate for: is the frequency domain channel matrix estimate, b i is the encoded symbol vector to be transmitted.

[0029] In a second aspect, the present application proposes a time domain channel estimation device based on multi-antenna diversity technology, the device comprising:

[0030] The encoding module is used to map the bit stream into a symbol stream to construct a transmission signal matrix, perform STBC encoding on the transmission signal matrix, and insert a time domain pilot signal to obtain an inserted matrix;

[0031] The matrix transformation module is used to select the vectors in the inserted matrix, combine them with the relevant parameters of the Gaussian white noise and the Rayleigh fading channel to obtain the frequency domain received signal, and then perform matrix transformation to obtain the STBC decoding matrix;

[0032] An updating module is used to introduce a weight matrix to update the established MIMO-OFDM system input and output model to obtain a time domain channel estimation value;

[0033] The generation module is used to perform FFT transformation on the time domain channel estimation value to obtain the frequency domain channel matrix estimation value, substitute the frequency domain channel matrix estimation value into the STBC decoding matrix, and then perform ML signal detection to obtain the final estimation value.

[0034] In a possible implementation, the encoding module is used to:

[0035] Convert the bitstream x b Mapping to symbol stream Use the symbol stream to construct the OFDM transmit signal matrix X t :

[0036] For the transmitted signal matrix X t Perform STBC encoding to obtain the encoding matrix A:

[0037] The transmission signal matrix is ​​extracted from the coding matrix by column, and then the coded matrix is ​​obtained through the coding matrix;

[0038] The time domain pilot signals of multiple OFDM symbols are uniformly inserted into the encoded matrix to obtain an inserted matrix.

[0039] In a possible implementation, the matrix transformation module is used to:

[0040] Select the vector in the inserted matrix and combine it with the relevant parameters of Gaussian white noise and Rayleigh fading channel to obtain the frequency domain received signal:

[0041]

[0042] Where [h1,h2,h3,h4] are the relevant parameters of the Rayleigh fading channel, [z1,z2,z3,z4,z5,z6,z7,z8] have a mean of 0 and a variance of σ 2 Gaussian white noise;

[0043] Perform matrix transformation on the frequency domain received signal to obtain the STBC decoding matrix:

[0044] In a possible implementation, the update module is used to:

[0045] Establish MIMO-OFDM system input and output model: R i =X p H i +Z i , where R i =[r i [0],r i [1],…,r i [N-1]] T represents the pilot received signals of different subcarriers of the ith antenna, r i [n], n = 1, 2, ..., N-1 represents the pilot element on the nth subcarrier of the ith antenna, represents the pilot signal matrix, where j = 1, 2, ..., N Tx , x j =[x j [1],x j [2],…,x j [N-1]] T represents the pilot signal vector transmitted on the jth antenna, x j [n] represents the pilot element on the nth subcarrier on the jth antenna, n = 1, 2, ..., N, represents the full-state frequency domain channel matrix of the MIMO system, H j,i =[H j,i [1],H j,i [2],...,H j,i [n]] T represents the frequency domain channel vector corresponding to the jth transmitting antenna and the ith receiving antenna, H j,i [n] represents the frequency domain channel element on the nth subcarrier in the frequency domain channel vector corresponding to the jth transmitting antenna and the ith receiving antenna, Z i =[z i [1],z i [1],…,z i [n]] T represents the Gaussian white noise matrix of the ith antenna, z i [n] represents the Gaussian white noise element on the nth subcarrier of the i-th antenna;

[0046] Set the number of multipath channels to L and denote the time domain channel vector as h j,i =[h j,i [1],h j,i [2],...,h j,i [L-1]] T ,h j,i [l] represents the time domain channel element between the jth transmitting antenna and the ith receiving antenna, l = 1, 2, ..., L, and there is a conversion relationship between the time domain channel vector and the frequency domain channel vector: H j,i =F L h j,i , F L Represents the discrete Fourier transform matrix with dimension L×L.

[0047] The weight matrix W is used to update the input and output model of the MIMO-OFDM system:

[0048] R i =Wh i +Z i

[0049]

[0050] Get the time domain channel estimate:

[0051] In one possible implementation, the final estimate for: is the frequency domain channel matrix estimate, b i is the encoded symbol vector to be transmitted.

[0052] The above-mentioned main scheme of the present application and its further options can be freely combined to form multiple schemes, all of which are schemes that can be adopted and claimed for protection in the present application; and in the present application, (non-conflicting options) options and other options can also be freely combined. After understanding the scheme of the present application, those skilled in the art can understand that there are multiple combinations based on the prior art and common knowledge, all of which are technical schemes to be protected by the present application, and they are not exhaustively listed here.

[0053] The present application discloses a time domain channel estimation method and device based on multi-antenna diversity technology, firstly, the bit stream is mapped to the symbol stream to construct the transmission signal matrix, the transmission signal matrix is ​​STBC encoded and the time domain pilot signal is inserted to obtain the inserted matrix, then the vector in the inserted matrix is ​​selected, and the frequency domain receiving signal is obtained by combining with the relevant parameters of Gaussian white noise and Rayleigh fading channel, and then the matrix transformation is performed to obtain the STBC decoding matrix, and the weight matrix is ​​introduced to update the established MIMO-OFDM system input and output model to obtain the time domain channel estimation value, and finally the time domain channel estimation value is FFT transformed to obtain the frequency domain channel matrix estimation value, and the frequency domain channel matrix estimation value is substituted into the STBC decoding matrix and then ML signal detection is performed to obtain the final estimation value. The channel estimation algorithm is introduced to improve the estimation of dynamic channels, and the demodulation signal accuracy of the multi-input multi-output system is improved by combining the space-time block coding technology, and the full-state channel of the MIMO system is estimated. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 A flow chart of a time domain channel estimation method based on multi-antenna diversity technology proposed in an embodiment of the present application is shown.

[0056] Figure 2 The figure shows a structural diagram of the MIMO system proposed in the embodiment of the present application.

[0057] Figure 3 The figure shows the simulation diagram of the constellation diagram before decoding of the received signal of different antennas under Rayleigh fading channel.

[0058] Figure 4 The figure shows the constellation diagram simulation after decoding and equalization of the received signals of different antennas under Rayleigh fading channel.

[0059] Figure 5 The figure shows the bit error rate performance of the MIMO system under Rayleigh fading channel. DETAILED DESCRIPTION

[0060] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0061] Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of this application.

[0062] In the prior art, a Multiple-Input Multiple-Output (MIMO) system usually has multiple transmitting antennas and multiple receiving antennas. The receiving antenna will receive the transmission signals output by different transmitting antennas, thereby causing received signal aliasing and making it impossible to distinguish the desired received signals.

[0063] To this end, the embodiment of the present application proposes a time domain channel estimation method and device based on multi-antenna diversity technology. In order to combat the fast fading phenomenon caused by the Rayleigh fading channel and enhance the signal robustness, the present application uses transmit diversity technology to pre-process the signal. In particular, the STBC technology is used to make the signals transmitted by different antennas orthogonal to each other, and each pair of transmitting and receiving antennas work together to reduce multipath interference, but this requires accurate channel state information. This method can effectively track the dynamic changes of the channel and combat fast fading by simply controlling the insertion mode of the pilot. The design allows the pilot density to be adjusted to adapt to the channel change rate, ensuring the timeliness and accuracy of the channel estimation.

[0064] By using the preset weight matrix and the received pilot signal in close cooperation, the full-state MIMO channel matrix can be efficiently estimated. This method can effectively reduce the original huge computational load by flexibly adjusting the weight matrix parameters, especially the assumption about the number of multipaths, while ensuring high accuracy, which is suitable for resource constraints in practical applications.

[0065] In addition, this method can directly serve the STBC decoding process, improving the accuracy of the final signal demodulation. It is optimized for the characteristics of the MIMO system, especially considering the actual situation of antenna pairing, to ensure that the algorithm can maintain its effectiveness and practicality under complex MIMO configurations. It is described in detail below.

[0066] Please refer to Figure 1 , Figure 1 A schematic flow chart of a time domain channel estimation method based on a multi-antenna diversity technology proposed in an embodiment of the present application is shown. The method is applied to a MIMO system and includes the following steps:

[0067] Step S100: Map the bit stream to a symbol stream to construct a transmit signal matrix, perform STBC encoding on the transmit signal matrix, and insert a time domain pilot signal to obtain an inserted matrix.

[0068] The original binary data (bit stream) is converted into a form more suitable for wireless transmission through modulation - symbol stream. Modulation is like packaging data, and each symbol represents a certain number of bits of information. After obtaining the symbol stream, these symbols will be arranged on different subcarriers to form a two-dimensional signal matrix (X). This process occurs under the framework of OFDM (Orthogonal Frequency Division Multiplexing) technology, in which each column of the signal matrix corresponds to the arrangement of symbols on a subcarrier, and the row represents the distribution of symbols in time series.

[0069] Next, in order to enhance the transmission reliability and anti-interference ability of the signal, Space-Time Block Coding (STBC) is used. STBC acts on the columns of the transmit signal matrix through a specific coding matrix to generate a new coding matrix. This allows the same signal information to be transmitted on multiple antennas and multiple time slots, but in different ways, so that the receiving end can receive the signal from multiple angles, and even if some paths are interfered or attenuated, the original information can be restored through decoding.

[0070] In order to perform channel estimation, pilot signals are inserted into the coded signal sequence. These pilot signals are predefined known sequences that are evenly distributed in the signal matrix. At the receiving end, by detecting and analyzing these pilot signals, the fading and delay characteristics experienced by the signal when passing through the wireless channel can be inferred, and then the received data can be corrected to ensure high-quality signal restoration.

[0071] Step S100 includes:

[0072] Convert the bitstream x b Mapping to symbol stream Use the symbol stream to construct the OFDM transmit signal matrix X t :

[0073] For the transmitted signal matrix X t Perform STBC encoding to obtain the encoding matrix A:

[0074] The transmission signal matrix is ​​extracted from the coding matrix by column, and then the coded matrix is ​​obtained through the coding matrix;

[0075] The time domain pilot signals of multiple OFDM symbols are uniformly inserted into the encoded matrix to obtain an inserted matrix.

[0076] Select a 4-transmit 4-receive MIMO system (the number of transmitting antennas N Tx =4, number of receiving antennas N Rx =4), the information bit stream x b Converted into a symbol stream through modulation The symbol stream is then further organized into an OFDM signal matrix X t , with a dimension of N×M, expressed as: OFDM technology can provide resistance to interference and fading in a multipath propagation environment by dispersing symbol streams onto multiple subcarriers.

[0077] In order to enhance the reliability of wireless communication, STBC is applied to extract the original signal matrix by column and operate it through a coding matrix, so that the signal can be dispersed in two dimensions of time and space, thereby improving the decoding success rate of the receiving end. Where j = 1, 2, ..., M, and the value of M must be an integer multiple of 4, then For the transmitted signal matrix X t Perform STBC encoding, and the encoding matrix is ​​expressed as:

[0078] From the coding matrix, we can see that the coding rate of STBC is 1 / 2, which means that the amount of information originally transmitted in one time unit is transmitted every two time units. The transmission signal matrix is ​​taken out by column. The signal in the encoding matrix is ​​obtained by encoding the matrix B, whose dimension is N×2M×4. Consider inserting N pThe pilot signals are inserted evenly into the matrix B. In order to enable the receiving end to accurately estimate the channel state, the pilot signals are inserted into the coded signal matrix. These pilot signals are evenly distributed in the matrix. This helps to perform channel estimation and equalization at the receiving end to ensure that the signal is correctly decoded.

[0079] Step S200: select a vector in the inserted matrix, combine it with Gaussian white noise and relevant parameters of the Rayleigh fading channel to obtain a frequency domain received signal, and then perform matrix transformation to obtain an STBC decoding matrix.

[0080] The part used for data transmission is extracted from the transmit signal matrix with the pilot signal inserted and STBC encoded. This part of the signal vector carries the information to be transmitted. These signal vectors interact with the uncertainty introduced in the wireless channel. Specifically, each signal element is multiplied by a complex coefficient from the Rayleigh fading channel, which represents the amplitude and phase changes caused by the signal reaching the receiving end through multiple paths.

[0081] The receiving end receives a signal that contains noise and is affected by channel fading. In order to restore the original signal, it is necessary to use the pre-designed STBC decoding matrix for processing. Then, channel estimation is performed, and the known pilot signal is used to estimate the unknown Rayleigh fading channel parameters. With these parameters, the matrix transformation can be performed more accurately, and decoding can be performed effectively, thereby restoring the original signal of the sender.

[0082] Step S200 includes:

[0083] Select the vector in the inserted matrix and combine it with the relevant parameters of Gaussian white noise and Rayleigh fading channel to obtain the frequency domain received signal:

[0084]

[0085] Where [h1,h2,h3,h4] are the relevant parameters of the Rayleigh fading channel, [z1,z2,z3,z4,z5,z6,z7,z8] have a mean of 0 and a variance of σ 2 Gaussian white noise;

[0086] Perform matrix transformation on the frequency domain received signal to obtain the STBC decoding matrix:

[0087] Figure 2The structure diagram of the MIMO system proposed in the embodiment of the present application is shown. The signal first passes through the input signal module and then is divided into four paths. Each signal passes through an "IDFT" (inverse discrete Fourier transform) module. 3. After each signal passes through the IDFT, the four signals are merged into the output signal module. The signal first passes through an input signal module, and then the signal is divided into four paths. Each signal passes through the DFT (discrete Fourier transform) module. After each signal passes through the DFT, the last four signals are merged into the output signal module.

[0088] The received signal on one receiving antenna in a MIMO system is expressed as:

[0089]

[0090] Where [y1, y2, y3, y4, y5, y6, y7, y8] represents the received signal in the frequency domain; [h1, h2, h3, h4] represents the relevant parameters of the Rayleigh fading channel, which are unknown parameters; [z1, z2, z3, z4, z5, z6, z7, z8] represents the mean value is 0 and the variance is σ 2 is Gaussian white noise, b1, b2, b3, b4 are all vectors in B.

[0091] The formula describes the signal model on a single antenna at the receiving end. The received signal vector [y1, y2, y3, y4, y5, y6, y7, y8] in the frequency domain consists of three parts: first, the response of the transmitted signal after passing through the channel, which reflects the Rayleigh fading characteristics between each transmitting antenna and the receiving antenna. These parameters are unknown in actual communication and change with time. Additive Gaussian white noise is an ideal noise model. Each element is independent and identically distributed, with a mean of 0 and a variance of (N_0), which reflects the inherent interference in the communication environment.

[0092] Change the received signal to get Then simplify it to get the STBC decoding matrix:

[0093]

[0094] The received signal, transmitted signal, noise term and unknown channel parameters are directly associated through matrix operations, and then further simplified to obtain the decoding matrix form. The purpose of STBC coding is to increase the robustness of the signal, and the decoding matrix is ​​the key to its inverse process. The results of channel estimation are used to perform reverse operations to try to separate the original signal stream.

[0095] Since channel parameters are unknown and constantly changing in actual communications, channel estimation methods are used to estimate actual channel conditions based on received signals and known information (such as pilot signals). Accurate channel estimation can greatly improve the quality of received signals and ensure effective recovery of the original transmitted signals, thereby ensuring the accuracy of data transmission.

[0096] Step S300: introducing a weight matrix to update the established MIMO-OFDM system input and output model to obtain a time domain channel estimation value.

[0097] By combining the weight matrix with the original input-output model, a new estimation model is formed. Based on the updated model, the time domain channel estimation value can be calculated by minimizing a certain criterion function.

[0098] Step S300 includes:

[0099] Establish MIMO-OFDM system input and output model: R i =X p H i +Z i , where R i =[r i [0],r i [1],…,r i [N-1]] T represents the pilot received signals of different subcarriers of the ith antenna, r i [n], n = 1, 2, ..., N-1 represents the pilot element on the nth subcarrier of the ith antenna, represents the pilot signal matrix, where j = 1, 2, ..., N Tx , x j =[x j [1],x j [2],...,x j [N-1]] T represents the pilot signal vector transmitted on the jth antenna, x j [n] represents the pilot element on the nth subcarrier on the jth antenna, n = 1, 2, ..., N, represents the full-state frequency domain channel matrix of the MIMO system, H j,i =[H j,i [1],H j,i [2],...,H j,i [n]] T represents the frequency domain channel vector corresponding to the jth transmitting antenna and the ith receiving antenna, H j,i [n] represents the frequency domain channel element on the nth subcarrier in the frequency domain channel vector corresponding to the jth transmitting antenna and the ith receiving antenna, Z i=[z i [1],z i [1],...,z i [n]] T represents the Gaussian white noise matrix of the ith antenna, z i [n] represents the Gaussian white noise element on the nth subcarrier of the i-th antenna;

[0100] Set the number of multipath channels to L and denote the time domain channel vector as h j,i =[h j,i [1],h j,i [2],...,h j,i [L-1]] T ,h j,i [l] represents the time domain channel element between the jth transmitting antenna and the ith receiving antenna, l = 1, 2, ..., L, and there is a conversion relationship between the time domain channel vector and the frequency domain channel vector: H j,i =F L h j,i , F L Represents the discrete Fourier transform matrix with dimension L×L.

[0101] The weight matrix W is used to update the input and output model of the MIMO-OFDM system:

[0102] R i =Wh i +Z i

[0103]

[0104] Get the time domain channel estimate:

[0105] In traditional channel estimation, the received signal of the MIMO-OFDM system contains time domain pilots of multiple OFDM symbols. These pilots are first converted to the frequency domain by FFT to prepare for subsequent channel estimation. The least squares (LS) method is used in the frequency domain to directly calculate the estimated value of the channel matrix based on the pilot signal and the known pilot sequence. However, this method does not perform well in a noisy environment and has a high computational complexity because it involves matrix inversion operations.

[0106] This application first establishes the MIMO-OFDM system input and output model based on the time domain pilot signal: i =X p H i +Z i , where i = 1, 2, ..., N Rx -1; where R i =[r i[0],r i [1],...,r i [N-1]] T represents the pilot received signals of different subcarriers of the ith antenna, whose dimension is N×1, where r i [n], n = 1, 2, ..., N-1 represents the pilot element on the nth subcarrier of the ith antenna. represents the pilot signal matrix, where j = 1, 2, ..., N Tx , x j =[x j [1],x j [2],…,x j [N-1]] T represents the pilot signal vector transmitted on the jth antenna, x j [n] represents the pilot element on the nth subcarrier on the jth antenna, n = 1, 2, …, N. represents the full-state frequency domain channel matrix of the MIMO system, H j,i =[H j,i [1],H j,i [2],...,H j,i [N]] T represents the frequency domain channel vector corresponding to the jth transmitting antenna and the ith receiving antenna, H j,i [n] represents the frequency domain channel element on the nth subcarrier in the frequency domain channel vector corresponding to the jth transmit antenna and the ith receive antenna. i =[z i [1],z i [1],...,z i [N]] T represents the Gaussian white noise matrix of the ith antenna, z i [n] represents the Gaussian white noise element on the nth subcarrier of the ith antenna.

[0107] Assuming the number of multipath channels is L, the time domain channel vector is represented by h j,i =[h j,i [1],h j,i [2],…,h j,i [L-1]] T , where h j,i [l] represents the time domain channel element between the jth transmitting antenna and the ith receiving antenna, l = 1, 2, ..., L. There is a conversion relationship between the time domain channel vector and the frequency domain channel vector, satisfying: H j,i =F L h j,i , where F Lrepresents the discrete Fourier transform matrix with dimension L×L. The input and output model of the traditional MIMO-OFDM system is updated and expressed as:

[0108] R i =Wh i +Z i

[0109]

[0110] A weight matrix (W) is introduced and applied in the update formula of the input-output model. The weight matrix applies different weights to different signal components to reduce the impact of noise. The selection of the weight matrix can be based on prior knowledge or obtained through an optimization algorithm.

[0111] Using the updated model and the discrete Fourier transform (DFT) relationship, the time domain channel estimation value can be obtained.

[0112] In some communication scenarios (e.g., open environments with line-of-sight transmission), the computational burden can be further reduced by adjusting the assumed multipath number (L). This is done because under these conditions, the actual multipath effect may be small, simplifying the model without sacrificing too much accuracy.

[0113] Step S400: Perform FFT transformation on the time domain channel estimation value to obtain the frequency domain channel matrix estimation value, substitute the frequency domain channel matrix estimation value into the STBC decoding matrix, and then perform ML signal detection to obtain the final estimation value.

[0114] For the channel estimation algorithm based on time domain pilot assistance, the time domain channel estimation value estimated by the algorithm through the pilot needs to be used for the subsequent data signal part, assuming that the channel state in the data signal part is unchanged. Perform FFT transformation to obtain the frequency domain channel matrix estimation value of the data part Substitute the frequency domain channel matrix estimate into the STBC decoding matrix and replace [h1,h2,h3,h4] to get [b1,b2,b3,b4] T . Perform ML signal detection on it to get the final estimated value, the final estimated value for: is the frequency domain channel matrix estimate, b i is the encoded symbol vector to be transmitted.

[0115] In a possible implementation manner, the time domain channel estimation proposed in the embodiment of the present application is simulated, and Table 1 shows the simulation parameters:

[0116] Table 1

[0117] parameter Parameter settings Number of multipath channels 20 Number of antennas 4 Number of time domain pilot OFDM symbols 16 Number of data OFDM symbols 64 Number of subcarriers 64 Cyclic prefix length 16 Doppler shift 6000Hz Modem QPSK Symbol rate 20Msps Monte Carlo simulation times 1000 Simulation Tools Matlab 2018a

[0118] The MIMO system uses the above simulation parameters, Figure 3 The figure shows the simulation diagram of the constellation diagram before decoding of the received signal of different antennas under Rayleigh fading channel, which simulates the performance of the algorithm after using it in MIMO system and the performance under Rayleigh fading channel. Figure 4 It shows the constellation diagram simulation after decoding and equalization of the received signal of different antennas under Rayleigh fading channel. Figure 3 and Figure 4 The constellation diagrams of the received signals of different antennas at different stages are shown. The simulation is carried out under the condition of a sensitivity of 6dB. It can be seen that after decoding and equalization, the constellation points tend to converge, but are still relatively messy. This is because the sensitivity is low, the working environment is harsh, and there is frequency offset interference. However, the bit error rate of the MIMO system is 0 at this time, which can meet normal communication needs.

[0119] Figure 5 The performance diagram of the bit error rate of the MIMO system under the Rayleigh fading channel is shown. As the sensitivity increases, the bit error rate decreases. And under low sensitivity, the bit error rate is still less than 10%, which verifies the effectiveness of the proposed algorithm. Because the Rayleigh fading channel has fast fading and time-varying characteristics, the channel estimation algorithm and the signal detection algorithm can effectively eliminate the influence of the inter-subcarrier interference in the OFDM system, and the use of STBC technology in the multi-antenna system can effectively eliminate the interference between antennas and further improve the demodulation performance.

[0120] A possible implementation of a time domain channel estimation device based on a multi-antenna diversity technology is given below, which is used to execute each execution step and corresponding technical effect of the time domain channel estimation method shown in the above embodiment and possible implementation, and the device includes:

[0121] The encoding module is used to map the bit stream into a symbol stream to construct a transmission signal matrix, perform STBC encoding on the transmission signal matrix, and insert a time domain pilot signal to obtain an inserted matrix;

[0122] The matrix transformation module is used to select the vectors in the inserted matrix, combine them with the relevant parameters of the Gaussian white noise and the Rayleigh fading channel to obtain the frequency domain received signal, and then perform matrix transformation to obtain the STBC decoding matrix;

[0123] An updating module is used to introduce a weight matrix to update the established MIMO-OFDM system input and output model to obtain a time domain channel estimation value;

[0124] The generation module is used to perform FFT transformation on the time domain channel estimation value to obtain the frequency domain channel matrix estimation value, substitute the frequency domain channel matrix estimation value into the STBC decoding matrix, and then perform ML signal detection to obtain the final estimation value.

[0125] In a possible implementation, the encoding module is used to:

[0126] Convert the bitstream x b Mapping to symbol stream Use the symbol stream to construct the OFDM transmit signal matrix X t :

[0127] For the transmitted signal matrix X t Perform STBC encoding to obtain the encoding matrix A:

[0128] The transmission signal matrix is ​​extracted from the coding matrix by column, and then the coded matrix is ​​obtained through the coding matrix;

[0129] The time domain pilot signals of multiple OFDM symbols are uniformly inserted into the encoded matrix to obtain an inserted matrix.

[0130] In a possible implementation, the matrix transformation module is used to:

[0131] Select the vector in the inserted matrix and combine it with the relevant parameters of Gaussian white noise and Rayleigh fading channel to obtain the frequency domain received signal:

[0132]

[0133] Where [h1,h2,h3,h4] are the relevant parameters of the Rayleigh fading channel, [z1,z2,z3,z4,z5,z6,z7,z8] have a mean of 0 and a variance of σ 2 Gaussian white noise;

[0134] Perform matrix transformation on the frequency domain received signal to obtain the STBC decoding matrix:

[0135] In a possible implementation, the update module is used to:

[0136] Establish MIMO-OFDM system input and output model: R i =X p H i +Z i , where R i =[r i [0],r i [1],…,r i [N-1]] T represents the pilot received signals of different subcarriers of the ith antenna, r i[n], n = 1, 2, ..., N-1 represents the pilot element on the nth subcarrier of the ith antenna, represents the pilot signal matrix, where j = 1, 2, ..., N Tx , x j =[x j [1],x j [2],...,x j [N-1]] T represents the pilot signal vector transmitted on the jth antenna, x j [n] represents the pilot element on the nth subcarrier on the jth antenna, n = 1, 2, ..., N, H i =[H 1,i ,...,H j,i ,...,H NTx-1,i ] T represents the full-state frequency domain channel matrix of the MIMO system, H j,i =[H j,i [1],H j,i [2],...,H j,i [n]] T represents the frequency domain channel vector corresponding to the jth transmitting antenna and the ith receiving antenna, H j,i [n] represents the frequency domain channel element on the nth subcarrier in the frequency domain channel vector corresponding to the jth transmitting antenna and the ith receiving antenna, Z i =[z i [1],z i [1],...,z i [n]] T represents the Gaussian white noise matrix of the ith antenna, z i [n] represents the Gaussian white noise element on the nth subcarrier of the i-th antenna;

[0137] Set the number of multipath channels to L and denote the time domain channel vector as h j,i =[h j,i [1],h j,i [2],...,h j,i [L-1]] T ,h j,i [l] represents the time domain channel element between the jth transmitting antenna and the ith receiving antenna, l = 1, 2, ..., L, and there is a conversion relationship between the time domain channel vector and the frequency domain channel vector: H j,i =F L h j,i , F L Represents the discrete Fourier transform matrix with dimension L×L.

[0138] The weight matrix W is used to update the input and output model of the MIMO-OFDM system:

[0139] R i =Wh i +Z i

[0140]

[0141] Get the time domain channel estimate:

[0142] In one possible implementation, the final estimate for: is the frequency domain channel matrix estimate, b i is the encoded symbol vector to be transmitted.

[0143] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A time domain channel estimation method based on multi-antenna diversity technology, characterized in that: Applied to a MIMO system, the method comprises: Mapping the bit stream into a symbol stream to construct a transmit signal matrix, performing STBC encoding on the transmit signal matrix and inserting a time domain pilot signal to obtain an inserted matrix; Select the vector in the inserted matrix, combine it with the relevant parameters of Gaussian white noise and Rayleigh fading channel to obtain the frequency domain received signal, and then perform matrix transformation to obtain the STBC decoding matrix; The weight matrix is ​​introduced to update the input and output model of the established MIMO-OFDM system to obtain the time domain channel estimation value; The time domain channel estimation value is transformed by FFT to obtain the frequency domain channel matrix estimation value, and the frequency domain channel matrix estimation value is substituted into the STBC decoding matrix and then ML signal detection is performed to obtain the final estimation value.

2. The time domain channel estimation method according to claim 1, characterized in that: The steps of mapping a bit stream into a symbol stream to construct a transmission signal matrix, performing STBC encoding on the transmission signal matrix and inserting a time domain pilot signal to obtain an inserted matrix include: Convert the bitstream x b Mapping to symbol stream Use the symbol stream to construct the OFDM transmit signal matrix X t : For the transmitted signal matrix X t Perform STBC encoding to obtain the encoding matrix A: The transmission signal matrix is ​​extracted from the coding matrix by column, and then the coded matrix is ​​obtained through the coding matrix; The time domain pilot signals of multiple OFDM symbols are uniformly inserted into the encoded matrix to obtain an inserted matrix.

3. The time domain channel estimation method according to claim 1, characterized in that: The steps of selecting a vector in the inserted matrix, combining it with the relevant parameters of the Gaussian white noise and the Rayleigh fading channel to obtain a frequency domain received signal, and then performing matrix transformation to obtain an STBC decoding matrix include: Select the vector in the inserted matrix and combine it with the relevant parameters of Gaussian white noise and Rayleigh fading channel to obtain the frequency domain received signal: Where [h1,h2,h3,h4] are the relevant parameters of the Rayleigh fading channel, [z1,z2,z3,z4,z5,z6,z7,z8] have a mean of 0 and a variance of σ 2 Gaussian white noise; Perform matrix transformation on the frequency domain received signal to obtain the STBC decoding matrix:

4. The time domain channel estimation method according to claim 1, characterized in that: The steps of introducing a weight matrix to update the established MIMO-OFDM system input and output model to obtain a time domain channel estimation value include: Establish MIMO-OFDM system input and output model: R i =X p H i +Z i , where R i =[r i [0],r i [1],...,r i [N-1]] T represents the pilot received signals of different subcarriers of the ith antenna, r i [n], n = 1, 2, ..., N-1 represents the pilot element on the nth subcarrier of the ith antenna, represents the pilot signal matrix, where j = 1, 2, ..., N Tx , x j =[x j [1],x j [2],...,x j [N-1]] T represents the pilot signal vector transmitted on the jth antenna, x j [n] represents the pilot element on the nth subcarrier on the jth antenna, n = 1, 2, ..., N, represents the full-state frequency domain channel matrix of the MIMO system, H j,i =[H j,i [1],H j,i [2],...,H j,i [n]] T represents the frequency domain channel vector corresponding to the jth transmitting antenna and the ith receiving antenna, H j,i [n] represents the frequency domain channel element on the nth subcarrier in the frequency domain channel vector corresponding to the jth transmitting antenna and the ith receiving antenna, Z i =[z i [1],z i [1],...,z i [n]] T represents the Gaussian white noise matrix of the ith antenna, z i [n] represents the Gaussian white noise element on the nth subcarrier of the i-th antenna; Set the number of multipath channels to L and denote the time domain channel vector as h j,i =[h j,i [1],h j,i [2],...,h j,i [L-1]] T ,h j,i [l] represents the time domain channel element between the jth transmitting antenna and the ith receiving antenna, l = 1, 2, ..., L, and there is a conversion relationship between the time domain channel vector and the frequency domain channel vector: H j,i =F L h j,i , F L represents the discrete Fourier transform matrix with dimension L×L; The weight matrix W is used to update the input and output model of the MIMO-OFDM system: R i =Wh i +Z i Get the time domain channel estimate:

5. The time domain channel estimation method according to claim 1, characterized in that: Final estimate for: is the frequency domain channel matrix estimate, b i is the encoded symbol vector to be transmitted.

6. A time domain channel estimation device based on multi-antenna diversity technology, characterized in that: The device comprises: The encoding module is used to map the bit stream into a symbol stream to construct a transmission signal matrix, perform STBC encoding on the transmission signal matrix, and insert a time domain pilot signal to obtain an inserted matrix; The matrix transformation module is used to select the vectors in the inserted matrix, combine them with the relevant parameters of the Gaussian white noise and the Rayleigh fading channel to obtain the frequency domain received signal, and then perform matrix transformation to obtain the STBC decoding matrix; An updating module is used to introduce a weight matrix to update the established MIMO-OFDM system input and output model to obtain a time domain channel estimation value; The generation module is used to perform FFT transformation on the time domain channel estimation value to obtain the frequency domain channel matrix estimation value, substitute the frequency domain channel matrix estimation value into the STBC decoding matrix, and then perform ML signal detection to obtain the final estimation value.

7. The time domain channel estimation device according to claim 6, characterized in that: Encoding module for: Convert the bitstream x b Mapping to symbol stream Use the symbol stream to construct the OFDM transmit signal matrix X t : For the transmitted signal matrix X t Perform STBC encoding to obtain the encoding matrix A: The transmission signal matrix is ​​extracted from the coding matrix by column, and then the coded matrix is ​​obtained through the coding matrix; The time domain pilot signals of multiple OFDM symbols are uniformly inserted into the encoded matrix to obtain an inserted matrix.

8. The time domain channel estimation device according to claim 6, characterized in that: Matrix transformation module, used for: Select the vector in the inserted matrix and combine it with the relevant parameters of Gaussian white noise and Rayleigh fading channel to obtain the frequency domain received signal: Where [h1,h2,h3,h4] are the relevant parameters of the Rayleigh fading channel, [z1,z2,z3,z4,z5,z6,z7,z8] have a mean of 0 and a variance of σ 2 Gaussian white noise; Perform matrix transformation on the frequency domain received signal to obtain the STBC decoding matrix:

9. The time domain channel estimation device according to claim 6, characterized in that: Update modules for: Establish MIMO-OFDM system input and output model: R i =X p H i +Z i , where R i =[r i [0],r i [1],…,r i [N-1]] T represents the pilot received signals of different subcarriers of the ith antenna, r i [n], n = 1, 2, ..., N-1 represents the pilot element on the nth subcarrier of the ith antenna, represents the pilot signal matrix, where j = 1, 2, ..., N Tx , x j =[x j [1],x j [2],...,x j [N-1]] T represents the pilot signal vector transmitted on the jth antenna, x j [n] represents the pilot element on the nth subcarrier on the jth antenna, n = 1, 2, ..., N, represents the full-state frequency domain channel matrix of the MIMO system, H j,i =[H j,i [1],H j,i [2],...,H j,i [n]] T represents the frequency domain channel vector corresponding to the jth transmitting antenna and the ith receiving antenna, H j,i [n] represents the frequency domain channel element on the nth subcarrier in the frequency domain channel vector corresponding to the jth transmitting antenna and the ith receiving antenna, Z i =[z i [1],z i [1],...,z i [n]] T represents the Gaussian white noise matrix of the ith antenna, z i [n] represents the Gaussian white noise element on the nth subcarrier of the i-th antenna; Set the number of multipath channels to L and denote the time domain channel vector as h j,i =[h j,i [1],h j,i [2],...,h j,i [L-1]] T ,h j,i [l] represents the time domain channel element between the jth transmitting antenna and the ith receiving antenna, l = 1, 2, ..., L, and there is a conversion relationship between the time domain channel vector and the frequency domain channel vector: H j,i =F L h j,i , F L represents the discrete Fourier transform matrix with dimension L×L; The weight matrix W is used to update the input and output model of the MIMO-OFDM system: R i =Wh i +Z i Get the time domain channel estimate:

10. The time domain channel estimation device according to claim 6, characterized in that: Final estimate for: is the frequency domain channel matrix estimate, b i is the encoded symbol vector to be transmitted.

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