CN116319193A - GCE-BEM iterative channel estimation method, system, equipment and medium based on sub-block transmission - Google Patents

GCE-BEM iterative channel estimation method, system, equipment and medium based on sub-block transmission Download PDF

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CN116319193A
CN116319193A CN202310281371.0A CN202310281371A CN116319193A CN 116319193 A CN116319193 A CN 116319193A CN 202310281371 A CN202310281371 A CN 202310281371A CN 116319193 A CN116319193 A CN 116319193A
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channel estimation
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葛建华
刘俊
许铠翔
陈浦芳
江昊霖
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Xidian University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0224—Channel estimation using sounding signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0264—Arrangements for coupling to transmission lines
    • H04L25/028—Arrangements specific to the transmitter end
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0264—Arrangements for coupling to transmission lines
    • H04L25/0292—Arrangements specific to the receiver end
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract

GCE-BEM iterative channel estimation method, system, equipment and medium based on sub-block transmission, wherein the method comprises the following steps: the method comprises the steps of adopting a TDKD pilot cluster based on a sub-block as a transmission frame structure, encoding bit information to form a complex signal, completing framing operation, modulating the transmission frame signal to a high-frequency carrier signal to be sent out, obtaining initial channel information by a receiving end through a signal of a pilot frequency position by adopting an LS channel estimation method, obtaining soft decision information by a linear frequency domain equalization module and a soft decision module, obtaining a whole channel matrix by adopting an LMMSE channel estimation method for a known symbol, and sending a result of second channel estimation to the frequency domain equalization module to obtain balanced output soft decision information for the known symbol of next iteration channel estimation; the system, the device and the medium are used for realizing a GCE-BEM iterative channel estimation method based on sub-block transmission; the invention effectively improves the precision of channel estimation, reduces the complexity of channel estimation and improves the error rate performance of the system.

Description

GCE-BEM iterative channel estimation method, system, equipment and medium based on sub-block transmission
Technical Field
The invention belongs to the technical field of dual-channel wireless communication, and particularly relates to a GCE-BEM iterative channel estimation method, a system, equipment and a medium based on sub-block transmission.
Background
For wideband communication systems with high data rate transmission, the sampling period is smaller than the delay spread of the channel as the bandwidth becomes larger, which leads to frequency selective fading, especially in multipath transmission. The relative mobility between the transmitter and the receiver introduces doppler spread, especially at high speeds, which can lead to time variations in the channel, and corresponding fading is called time selectivity. The channel in which frequency selective fading and time selective fading coexist is called a dual-select channel (Doubly Selective Channel, DSC). The dual channel model generally employs a generalized Stationary uncorrelated scattering model (WSSUS). In a block transmission system, frequency selective fading and time selective fading cause intersymbol interference and inter-carrier interference, which greatly reduces system performance. To mitigate these fading effects, it becomes very important to perform reliable channel estimation on the dual-select channel.
Block transmission techniques provide an efficient and computationally affordable solution. They have the advantage that inter-block interference, such as orthogonal frequency division multiplexing (Orthogonal Frequency Division Multiplexing, OFDM) techniques, can be eliminated by adding a Cyclic Prefix (CP) at the top of the block or Zero Padding (ZP) at the end of the block. In a block transmission system, data is transmitted in blocks, thereby facilitating channel equalization and symbol detection. For block transmission with coherent detection, channel estimation will play an important role, especially in the dual-choice channel.
To track channel variations, dong et al in literature (Dong, m., to, l., & Sadler, b.m. (2004) & Optimal insertion of pilot symbols for transmissions over time-varying flat-fading channels.ieee Transactions on Signal Processing,52 (5), 1403-1418.) give optimal placement of training symbols of a block transmission system in a time-varying flat fading channel, ma et al in literature (Ma, x., giannakis, g.b., & Ohno, s., & 2003) & Optimal training for block transmissions over doubly selective wireless channels.ieee Transactions on Signal Processing,51 (5), 1351-1366.) give optimal placement of training symbols on a dual-choice channel by using a Base Extension Model (BEM). Rousseaux et al, in literature (Rousseaux, o., leus, g., & Moonen, m. (2006) & Estimation and equalization of doubly selective channels using known symbol padding.ieee Transactions on Signal Processing,54 (3), 979-990.), studied the problem of channel estimation and equalization in dual-select channels based on block transmission using known symbol-stuffing transmission techniques. By using BEM-based methods, many scholars have proposed different channel estimation and pilot designs. The design and analysis of pilot-assisted transmission of block transmission systems on single-antenna dual-channel is presented based on complex index BEM, kannu and Schniter in literature (Kannu, a.p. & Schniter, p. (2008), "Design and analysis ofMMSE pilot-spatial cyclic-prefixed block transmissions for doubly selective channels". IEEE trans.signal process, 56 (3), 1148-1160). Tugnait and He in literature (Tugnait, j.k., & He, s. (2007). Douply-selective channel estimation using data-dependent superim-posed training and exponential basis models.ieee Transactions on Wireless Communications,6 (11), 3877-3883.) give channel estimates for single-input multiple-output block transmission systems with data-dependent superposition training on dual selected channels. The channel estimation method based on the windowing technique is proposed by literature (Leus, g. (2004) On the estimation ofrapidly time-varying channels. In: proceedings of2004 European signal processing conference (pp. 2227-2230). Vienna, austria.) and the windowed least squares estimator and the optimal pilot design for the dual-selected channel. Optimization of the BEM parameters and training sequences for channel estimation of the dual-channel in a block transmission system is given for different BEMs by Whitworth et al (Whitworth, t., ghuo, m., & McLernon, d. (2009). Optimized training and basis expansion model parameters for doubly-selective channel stimation.ieee Transactions on Wireless Communica-tions,8 (3), 1490-1498.).
These developments greatly enrich the BEM-based channel estimation methods for dual-channel selection in block transmission systems, improving system performance, butThe above document basically considers channel estimation over the whole block period when the block length N is large or the maximum normalized doppler frequency f nom When the number of BEM coefficients needed is larger, the calculation complexity of estimation is greatly improved, especially under the fast fading condition, the CE-BEM model can not well reflect the real channel, and the error of channel estimation can be increased; the channel is modeled by using oversampling BEM (GCE-BEM), the number of base coefficients becomes large, and thus, a high-dimensional matrix needs to be inverted to perform Linear Minimum Mean Square Error (LMMSE) estimation or equalization, and calculation is relatively complex.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a GCE-BEM iterative channel estimation method, a system, equipment and a medium based on sub-block transmission, which divide a long data block into a plurality of short data sub-blocks, respectively carry out channel estimation on each sub-block, then reconstruct a real channel by using the estimation values, carry out iterative estimation through balanced soft symbol information, continuously update the information of the channel estimation, effectively improve the precision of the channel estimation, reduce the complexity of the channel estimation and improve the bit error rate performance of the system.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a GCE-BEM iterative channel estimation method based on sub-block transmission comprises the following steps:
step 1, adopting time domain Croneck TDKD pilot clusters based on sub-blocks as a transmission frame structure, wherein each single carrier frequency domain equalization SC-FDE block has M sub-blocks, each sub-block comprises pilot clusters of P TDKD structures, each pilot cluster consists of a data part and a pilot part, only one position in the middle of the pilot part is a non-zero pilot symbol, both sides of the pilot part are filled with zeros with a certain length as a guard interval, and the pilot symbols and the positions are known to a transmitting end and a receiving end;
step 2, coding bit information at a transmitting end, modulating the coded information to different constellation points through binary baseband signals to form complex signals required by transmission, blocking data according to the frame structure of the step 1, inserting local pilot frequency to complete framing operation, forming transmission frame signals, modulating the signals to high-frequency carrier signals, and transmitting the signals through an antenna;
step 3, the receiving antenna carries out timing synchronization according to the signals received in the step 2 to obtain receiving signals required by channel estimation, selects signals of pilot frequency positions, carries out primary channel estimation by adopting a least square LS channel estimation method based on GCE-BEM to obtain base coefficients corresponding to sub-blocks, estimates the base coefficients of the whole data block according to an oversampling complex exponential base function, and obtains initial channel information of the whole data block through the base coefficients;
Step 4, according to the received signal and the estimated channel information in the step 3, obtaining balanced soft decision information by a linear frequency domain equalization module and a soft decision module for the known symbol of the next channel estimation;
step 5, according to the known symbol obtained in the step 4, a minimum mean square error LMMSE channel estimation method based on GCE-BEM is adopted for the second channel estimation, the base coefficient of the sub-block in the SC-FDE block is obtained, the base coefficient of the whole data block is obtained, and finally the channel matrix H of the whole data block is fitted through an oversampling complex exponential base function;
step 6, according to the result of the second channel estimation obtained in step 5, sending the result to a frequency domain equalization module to obtain soft decision information of equalization output, wherein the soft decision information is used for the known symbol of the next iteration channel estimation; and when the iteration times are over, sequentially carrying out frequency domain equalization modules on the channel information obtained by the last iteration and the inter-symbol interference elimination of the received signal to obtain the finally required bit information.
In the step 1, a time domain kronecker TDKD pilot cluster based on a sub-block is adopted as a transmission frame structure, and the specific process is as follows:
step 1.1: for each single carrier frequency domain equalized SC-FDE data block, length n=n s +N t Wherein N is s For the length of the information sequence, N t For training sequence length, it is divided into M equal-length sub-blocks, each of which has length N 0 The choice of M depends on the maximum doppler frequency and the block =n/MA length;
step 1.2: for each sub-block information symbol and training sequence, according to the time domain Cronecker TDKD pilot frequency placement method, firstly dividing each sub-block into P pilot frequency clusters, wherein M epsilon {1, …, M } and P epsilon {1, …, P } are respectively the indexes of the M-th sub-block and the P-th small sub-block, and for each pilot frequency cluster, the length of the information symbol is N s,b ＝N s /(MP), training sequence length N t,b ＝N t /(MP)＝2L+1；
Step 1.3: adding L guard zeros to the end of each sub-block by setting the last L elements of each training sequence to zero, where L is the number of multipath channels;
when the single carrier frequency domain equalization SC-FDE data block x arrives at the receiving end through the dual-selection channel, the nth receiving symbol may be expressed as:
Figure BDA0004138094800000051
wherein z (n) is 0 as the mean and 0 as the variance
Figure BDA0004138094800000052
Additive white gaussian noise AWGN;
the above is written in matrix form:
y＝Hx+z
wherein z= [ z (0), z (1), …, z (N-1)] T ，y＝[y(0),y(1),…,y(N-1)] T H is an NxN dimensional matrix, and is specifically shown as follows:
Figure BDA0004138094800000061
step 1.3: using GCE-BEM, the channel matrix H can be expressed as
Figure BDA0004138094800000062
In the method, in the process of the invention,
Figure BDA0004138094800000063
C q is a toeplitz matrix, specifically shown in the following formula, and +.>
Figure BDA0004138094800000066
Figure BDA0004138094800000064
In the step 3, a least square LS channel estimation method based on GCE-BEM is adopted to carry out first channel estimation, and the specific process is as follows:
step 3.1: when the iteration number i=0, knowing the data corresponding to the pilot frequency position of the received signal and the basis function matrix, obtaining the basis coefficient of each path by the following formula:
Figure BDA0004138094800000065
wherein y is t,l ＝[y(n 0,l ),…,y(n P-1,l )] T ,n p,l ＝n p +l,l＝0,1,…L；
Step 3.2: the basis function matrix corresponding to the pilot positions is expressed as:
Figure BDA0004138094800000071
the base coefficient matrix of the sub-block is obtained from the above
Figure BDA0004138094800000072
Base coefficient correlation matrix->
Figure BDA0004138094800000073
The initial channel information of the whole data block can be obtained through the GCE-BEM channel model.
The step 4 of eliminating intersymbol interference to the received signal, the known symbol for the next channel estimation of the obtained balanced soft decision information is specifically: soft decision is carried out on the balanced output to obtain symbol soft information for the next iteration, and the soft information corresponding to the nth symbol in the p-th small sub-block of the m-th sub-block is obtained by the following formula:
Figure BDA0004138094800000074
in the method, in the process of the invention,
Figure BDA0004138094800000075
and->
Figure BDA0004138094800000076
Log likelihood ratio information representing the equalized output.
The LMMSE channel estimation method based on GCE-BEM in the step 5 comprises the following specific processes:
step 5.1: when the iteration number i is more than 0, the symbol soft information obtained in the last iteration is inserted into a local pilot frequency as a known symbol to perform LMMSE channel estimation based on GCE-BEM;
Step 5.2: the base coefficient of the m-th sub-block is obtained by:
Figure BDA0004138094800000077
in the method, in the process of the invention,
Figure BDA0004138094800000078
and (3) a base coefficient correlation matrix calculated for the last iteration result, wherein y (m) is a received signal.
Figure BDA0004138094800000079
U q,p (m) is U q A sub-matrix of a training symbol corresponding portion in the p-th sub-block of (m);
Figure BDA0004138094800000081
Figure BDA0004138094800000082
divided into two parts t p,n (m) and->
Figure BDA0004138094800000083
t p,n (m) pilot sequence re-inserted for receiving end, ">
Figure BDA0004138094800000084
The symbol soft information output by the equalization module in the previous iteration is composed;
step 5.3: the channel vector of the first path of the mth sub-block is derived by
Figure BDA0004138094800000085
Figure BDA0004138094800000086
In the method, in the process of the invention,
Figure BDA0004138094800000087
is the base coefficient vector of the first path,
Figure BDA0004138094800000088
the matrix of the basis function corresponding to the mth subblock has the dimension of N 0 ×(Q 0 +1), wherein
Figure BDA0004138094800000089
Step 5.4: obtaining the base coefficient of the whole data block through M channel vectors obtained according to the step 5.3, wherein the vector dimension is N multiplied by 1:
Figure BDA00041380948000000810
the base coefficient vector corresponding to the first path of the entire data block can be obtained by:
Figure BDA00041380948000000811
wherein U= [ b ] 0 ,b 1 ,…,b Q ]Is a matrix of basis functions corresponding to a period of an SC-FDE data block, and has a dimension of N× (Q+1), wherein
Figure BDA0004138094800000091
Step 5.5: based on
Figure BDA0004138094800000092
Obtaining estimated base coefficients
Figure BDA0004138094800000093
Then further obtaining an estimated base coefficient matrix +.>
Figure BDA0004138094800000094
The estimated channel matrix H is obtained by:
Figure BDA0004138094800000095
in the step 6, the iteration is started from the second channel estimation, and the channel estimation method in the iteration adopts a minimum mean square error (LMMSE) channel estimation method based on the GCE-BEM to update the channel information in an iteration mode.
A GCE-BEM iterative channel estimation system based on sub-block transmission, comprising:
the channel coding module is used for coding the original bit information, and enhancing the reliability of the information in the transmission process by adding redundancy;
the constellation mapping module is used for modulating the binary baseband signals to different constellation points to form complex signals required by transmission;
the framing module is used for forming a specific frame structure required by transmission and is convenient for a receiving end to process;
the radio frequency module is used for modulating a transmitting signal to a high-frequency carrier signal and transmitting the transmitting signal through an antenna;
a least squares LS channel estimation module for providing initial channel information;
a minimum mean square error (LMMSE) channel estimation module for providing iteratively updated channel information;
the frequency domain equalization module is used for eliminating intersymbol interference on the received signal through the output of the channel estimation module;
the soft decision module is used for providing an input signal of the channel estimation module based on the GCE-BEM and soft information input by the decoding module;
and the channel decoding module is used for decoding the final useful bit information from the soft information.
A GCE-BEM iterative channel estimation device based on sub-block transmission, comprising:
A memory: a computer program for storing a GCE-BEM iterative channel estimation method based on sub-block transmission;
a processor: the GCE-BEM iterative channel estimation method based on sub-block transmission is realized when the computer program is executed.
A computer-readable storage medium, comprising:
the computer readable storage medium stores a computer program which, when executed by a processor, enables a GCE-BEM iterative channel estimation method based on sub-block transmission.
Compared with the prior art, the invention has the beneficial effects that:
(1) Under the SC-FDE system, the invention combines the channel estimation based on GCE-BEM with the balanced soft decision information, and the balanced soft decision information is used as feedback information, so that not only is intersymbol interference (ISI) and inter-frequency interference (IFI) eliminated, but also the channel state information is updated together with a local training sequence as a known sequence, and the SC-FDE system under the double-selection channel has higher channel estimation performance by carrying out iterative updating on the channel estimation information.
(2) The invention uses pilot frequency sequence to adopt LS channel estimation method based on GCE-BEM to get initial channel information, then enters soft decision information of initial symbol of linear frequency domain equalization module; and in the next iteration, the symbol soft decision information obtained in the last iteration is inserted into an LMMSE channel estimation module based on GCE-BEM and used as a known sequence, the channel estimation value is iteratively updated, meanwhile, the adopted LMMSE channel is estimated, and meanwhile, the statistical characteristic information of the channel is required to be updated according to the information of the last iteration, so that the improvement of the channel estimation performance is realized through continuous iteration.
(3) The invention carries out the blocking processing again on the SC-FDE data block, and the number of the used base coefficients is smaller, thereby reducing the complexity of LMMSE channel estimation based on GCE-BEM in the iterative process and improving the system performance.
(4) In the step 1 of the invention, the time domain Cronecker TDKD pilot cluster based on the sub-blocks is adopted as a transmission frame structure, and the time domain Cronecker TDKD pilot cluster structure can decouple information symbols from training symbols, can eliminate inter-block interference, is better suitable for dual-selection channel communication, divides the data blocks in the data frame into the sub-blocks, can reduce the number of base coefficients to be estimated in channel estimation, and has the characteristic of low complexity.
(5) In the invention, step 3 adopts LS estimation to carry out first channel estimation, can provide initial channel information for subsequent iteration, and has the characteristics of simple realization and low complexity.
(6) In the step 4 of the invention, the frequency domain equalization is adopted and the frequency domain equalization output is subjected to the soft decision operation, and compared with the time domain equalization, the frequency domain equalization can reduce the complexity of an equalization algorithm and has the characteristic of low complexity. The soft decision can keep all information of the received signal and has the characteristic of improving channel estimation and decoding performance.
(7) In the step 5 of the invention, the LMMSE estimation is adopted to carry out iterative channel estimation, compared with the first LS channel estimation, the channel information can be estimated more accurately because of taking noise influence into consideration, compared with the existing LMMSE channel estimation, the complexity of the algorithm can be effectively reduced because of adopting a transmission mode based on sub-blocks, the method has the characteristics of low complexity and good performance,
(8) The step 6 of the invention adopts the processing method of iterative channel estimation, can effectively improve the accuracy of the channel estimation of the system through multiple iterations, and has the characteristic of improving the performance.
In summary, compared with the prior art, the method and the device can improve the accuracy of channel estimation, reduce the computational complexity, and have the characteristics of higher performance and low complexity.
Drawings
Fig. 1 is a flowchart of a method provided in an embodiment of the present invention.
Fig. 2 is a schematic diagram of a frame structure of an SC-FDE system according to an embodiment of the present invention.
Fig. 3 is a flowchart of an iterative channel estimation system based on GCE-BEM according to an embodiment of the present invention.
Fig. 4 is a comparative diagram of BER performance simulation provided by an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings.
As shown in fig. 1, the method provided by the embodiment of the invention includes the following steps:
Step 1, adopting time domain Croneck TDKD pilot clusters based on sub-blocks as a transmission frame structure, wherein each single carrier frequency domain equalization SC-FDE block has M sub-blocks, each sub-block comprises pilot clusters of P TDKD structures, each pilot cluster consists of a data part and a pilot part, only one position in the middle of the pilot part is a non-zero pilot symbol, both sides of the pilot part are filled with zeros with a certain length as a guard interval, and the pilot symbols and the positions are known to a transmitting end and a receiving end;
step 2, coding bit information at a transmitting end, modulating the coded information to different constellation points through binary baseband signals to form complex signals required by transmission, blocking data according to the frame structure of the step 1, inserting local pilot frequency to complete framing operation, forming transmission frame signals, modulating the signals to high-frequency carrier signals, and transmitting the signals through an antenna;
step 3, the receiving antenna carries out timing synchronization according to the signals received in the step 2 to obtain receiving signals required by channel estimation, selects signals of pilot frequency positions, carries out primary channel estimation by adopting a least square LS channel estimation method based on GCE-BEM to obtain base coefficients corresponding to sub-blocks, estimates the base coefficients of the whole data block according to an oversampling complex exponential base function, and obtains initial channel information of the whole data block through the base coefficients;
Step 4, according to the received signal and the estimated channel information in the step 3, obtaining balanced soft decision information by a linear frequency domain equalization module and a soft decision module for the known symbol of the next channel estimation;
step 5, according to the known symbol obtained in the step 4, a minimum mean square error LMMSE channel estimation method based on GCE-BEM is adopted for the second channel estimation, the base coefficient of the sub-block in the SC-FDE block is obtained, the base coefficient of the whole data block is obtained, and finally the channel matrix H of the whole data block is fitted through an oversampling complex exponential base function;
step 6, according to the result of the second channel estimation obtained in step 5, sending the result to a frequency domain equalization module to obtain soft decision information of equalization output, wherein the soft decision information is used for the known symbol of the next iteration channel estimation; and when the iteration times are over, sequentially carrying out frequency domain equalization modules on the channel information obtained by the last iteration and the inter-symbol interference elimination of the received signal to obtain the finally required bit information.
As shown in fig. 2, a frame structure diagram of the SC-FDE system has a length of n=n for each SC-FDE data block s +N t Wherein N is s For the length of the information sequence, N t Is the training sequence length. We first divide it into M equal-length sub-blocks, each of length N 0 The choice of M depends on the maximum doppler frequency and the length of the block.
For each sub-block information symbol and training sequence, according to the TDKD pilot frequency placement method, each sub-block is firstly divided into P pilot frequency clusters (from eachAnd pilot portion), where M e {1, …, M } and P e {1, …, P } are the indices of the M-th and P-th sub-blocks, respectively. For each pilot cluster, the length of the information symbol is N s,b ＝N s /(MP), training sequence length N t,b ＝N t /(MP) =2l+1. To eliminate Inter Block Interference (IBI), L guard zeros need to be added to the end of each (sub) block, which is achieved by setting the last L elements of each training sequence to zero, where L is the number of multipath channels.
As shown in fig. 3, an iterative channel estimation system flow diagram based on GCE-BEM.
The original bit information is used for generating complex signals at a transmitting end through a channel coding and constellation mapping module, then framing is carried out through the frame structure shown in figure 2, the signals are further transmitted through an antenna through a radio frequency module,
when an SC-FDE data block x arrives at the receiving end through the dual-selection channel, an effective received signal is obtained through ideal timing synchronization, where the nth received symbol can be expressed as:
Figure BDA0004138094800000131
Wherein z (n) is 0 as the mean and 0 as the variance
Figure BDA0004138094800000132
Additive White Gaussian Noise (AWGN).
The above is written in matrix form:
y＝Hx+z
wherein z= [ z (0), z (1), …, z (N-1)] T ，y＝[y(0),y(1),…,y(N-1)] T H is an NxN dimensional matrix, and is specifically shown as follows:
Figure BDA0004138094800000141
first, with GCE-BEM, the channel matrix H can be expressed as
Figure BDA0004138094800000142
In the method, in the process of the invention,
Figure BDA0004138094800000143
C q is a toeplitz matrix, specifically shown in the following formula, and +.>
Figure BDA0004138094800000145
Figure BDA0004138094800000144
For the iterative channel estimation section, the following iterations are performed:
first iterative channel estimation:
the LS channel estimation does not need any channel statistics, so the receiver processes with the LS channel estimation when the number of iterations i=0. Knowing the data corresponding to the pilot positions of the received signals and the basis function matrix, we can obtain the basis coefficients of each path by:
Figure BDA0004138094800000151
wherein y is t,l ＝[y(n 0,l ),…,y(n P-1,l )] T ,n p,l ＝n p +l,l＝0,1,…L。
The basis function matrix corresponding to the pilot positions is expressed as:
Figure BDA0004138094800000152
thus obtaining the base coefficient matrix of the SC-FDE data block
Figure BDA0004138094800000159
Base coefficient correlation matrix
Figure BDA0004138094800000153
The channel statistics required for the next LMMSE iteration estimate.
Initial channel estimation information can be obtained through the GCE-BEM channel model.
Next, performing equalization processing through the linear frequency domain equalizer, and performing soft decision on the equalization output to obtain symbol soft information for the next iteration, wherein the soft information corresponding to the nth symbol in the p-th small sub-block of the m-th sub-block is obtained by the following formula:
Figure BDA0004138094800000154
In the method, in the process of the invention,
Figure BDA0004138094800000155
and->
Figure BDA0004138094800000156
Log likelihood ratio information representing the equalized output.
The i-th iterative channel estimation:
next, when the iteration number i > 0, we insert the symbol soft information obtained in the last iteration into the local pilot as the known symbol to perform the LMMSE channel estimation based on GCE-BEM.
We also use the GCE-BEM based channel estimation method, so the base coefficients of the mth small sub-block are obtained by:
Figure BDA0004138094800000157
in the method, in the process of the invention,
Figure BDA0004138094800000158
and (3) a base coefficient correlation matrix calculated for the last iteration result, wherein y (m) is a received signal.
Figure BDA0004138094800000161
U q,p (m) is U q The sub-matrix of the corresponding part of training symbols in the p-th sub-block in (m).
Figure BDA0004138094800000162
Figure BDA0004138094800000163
Divided into two parts t p,n (m) and->
Figure BDA0004138094800000164
t p,n (m) pilot sequence re-inserted for receiving end, ">
Figure BDA0004138094800000165
And the symbol soft information output by the equalization module in the previous iteration.
By the following formula, we can estimate the channel vector of the first path of the mth sub-block
Figure BDA0004138094800000166
Figure BDA0004138094800000167
In the method, in the process of the invention,
Figure BDA0004138094800000168
is the base coefficient vector of the first path, < +.>
Figure BDA0004138094800000169
The matrix of the basis function corresponding to the mth subblock has the dimension of N 0 ×(Q 0 +1), wherein
Figure BDA00041380948000001610
Further, with the above estimated M channel vectors, we reconstruct the channel vector of the entire data block, with vector dimensions n×1:
Figure BDA00041380948000001611
then, a base coefficient vector corresponding to the first path of the entire data block can be obtained by:
Figure BDA0004138094800000171
Wherein U= [ b ] 0 ,b 1 ,…,b Q ]Is a matrix of basis functions corresponding to a period of an SC-FDE data block, and has a dimension of N× (Q+1), wherein
Figure BDA0004138094800000172
Based on
Figure BDA0004138094800000173
We can obtain the estimated base coefficients from the above equation
Figure BDA0004138094800000174
Then further obtaining an estimated base coefficient matrix +.>
Figure BDA0004138094800000175
Finally, an estimated channel matrix H is obtained by the following formula:
Figure BDA0004138094800000176
after the iterative channel estimation value is updated, the soft decision information output after equalization can be more accurate, after pilot frequency is inserted, the soft decision information is fed back to the channel estimation part again to be used as a new known pilot frequency sequence for LMMSE channel estimation, so that new channel update is obtained, and the iteration is circulated until the iteration termination condition is met.
The principle of application of the invention is further described below in connection with specific embodiments.
In an embodiment, the SC-FDE system adopts a single-shot system. The channel model adopts a double-selection channel of a time-varying underwater sound channel scene, and the carrier frequency is f c =12khz, the propagation velocity of sound waves in the ocean is about 1500m/s, the symbol period is T s =250 μs. Normalized Doppler shift f nom Characterizing the time-varying speed of the channel, f nom =0.005 denotes a slow time-varying channel with a speed of 10km/h, f nom =0.033 denotes a fast time-varying channel with a speed of 60 km/h. Setting the iteration times to be 3 times, and carrying out iterative channel estimation on the received signal. The Bit Error Rate (BER) of the method is simulated for different signal-to-noise ratios (SNR), and the system performance under the traditional non-iterative channel estimation (hereinafter referred to as the existing method) and ideal channel conditions is compared.
As shown in the following table, the complexity of the LMMSE channel estimation method based on GCE-BEM is compared with that of the schematic diagram.
LMMSE channel estimation method complexity analysis based on GCE-BEM in iterative process
Figure BDA0004138094800000181
The improved LMMSE channel estimation method based on GCE-BEM in the iterative process of the invention is adopted to compare with the complexity of the existing LMMSE channel estimation method based on BEM. For long data blocks, compared with the situation that the channel estimation performance is not lost, the method directly estimates the channel of the whole data block through BEM, the method divides the data block into sub-blocks, uses fewer base coefficients, and therefore reduces the complexity of LMMSE channel estimation based on GCE-BEM in the iterative process.
Fig. 4 is a schematic diagram showing the comparison between the SNR performance of the iterative channel estimation method based on the GCE-BEM of the sub-block and the existing channel estimation method in the SC-FDE system using the method of the present invention, wherein the abscissa represents the SNR of the signal to noise ratio, and the ordinate represents the BER of the bit error probability. Therefore, the method provided by the invention can effectively improve the error rate performance of the SC-FDE system under the condition of time-varying channel fading by utilizing iterative channel estimation.
The embodiment can show that the channel estimation method can obviously improve the accuracy of channel estimation of the SC-FDE system, and can effectively reduce the complexity of LMMSE channel estimation based on GCE-BEM in the iterative process by dividing the data block into sub-blocks.
A signal detection apparatus for a GCE-BEM iterative channel estimation system based on sub-block transmission, comprising:
a memory: a computer program for storing a GCE-BEM iterative channel estimation method based on sub-block transmission;
and the processor is used for realizing the GCE-BEM iterative channel estimation method based on the sub-block transmission when executing the computer program.
The processor may be a central processing unit (CentralProcessingUnit, CPU), other general purpose processors, digital signal processors (DigitalSignalProcessor, DSP), application specific integrated circuits (ApplicationSpecificIntegratedCircuit, ASIC), off-the-shelf programmable gate arrays (Field-ProgrammableGateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the GCE-BEM iterative channel estimation device based on sub-block transmission, and connects various parts of the whole GCE-BEM iterative channel estimation device based on sub-block transmission by using various interfaces and lines.
The processor, when executing the computer program, implements the steps of the signal detection method of the GCE-BEM iterative channel estimation system based on sub-block transmission, for example: the method comprises the steps that a TDKD pilot cluster based on a sub-block is adopted as a transmission frame structure, each SC-FDE block is provided with M sub-blocks, each sub-block comprises P pilot clusters of the TDKD structure, pilot symbols and positions are known to a transmitting end and a receiving end, bit information is encoded to form complex signals required for transmission, framing operation is completed, transmission frame signals are formed, modulation is carried out on the complex signals to be transmitted, a receiving end utilizes signals of pilot positions, an LS channel estimation method based on GCE-BEM is adopted to obtain initial channel information of the whole data block, a receiving signal and estimated channel information are used for obtaining balanced soft decision information through a linear frequency domain equalization module and a soft decision module to obtain a known symbol for next channel estimation, the known symbol is used for obtaining a channel matrix of the whole data block through an LMMSE channel estimation method based on GCE-BEM, a second channel estimation result is transmitted to a frequency domain equalization module, and balanced output soft decision information is obtained for obtaining a known symbol for next iteration channel estimation; a GCE-BEM iterative channel estimation method based on sub-block transmission is realized.
Alternatively, the processor may implement functions of each module in the above system when executing the computer program, for example: the channel coding module is used for coding the original bit information, and enhancing the reliability of the information in the transmission process by adding redundancy; the constellation mapping module is used for modulating the binary baseband signals to different constellation points to form complex signals required by transmission; the framing module is used for forming a specific frame structure required by transmission and is convenient for a receiving end to process; the radio frequency module is used for modulating a transmitting signal to a high-frequency carrier signal and transmitting the transmitting signal through an antenna; a least squares LS channel estimation module for providing initial channel information; a minimum mean square error (LMMSE) channel estimation module for providing iteratively updated channel information; the frequency domain equalization module is used for eliminating intersymbol interference on the received signal through the output of the channel estimation module; the soft decision module is used for providing an input signal of the channel estimation module based on the GCE-BEM and soft information input by the decoding module; a channel decoding module for decoding the final useful bit information from the soft information; outputting a signal detection result of the GCE-BEM iterative channel estimation system based on the sub-block transmission.
The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor to accomplish the present invention, for example. The one or more modules/units may be a series of computer program instruction segments capable of performing a predetermined function, the instruction segments describing the execution of the computer program in the apparatus for signal detection of the GCE-BEM iterative channel estimation system based on sub-block transfer. For example, the computer program may be divided into channel coding modules for encoding the original bit information, thereby enhancing the reliability of the information during transmission by adding redundancy; the constellation mapping module is used for modulating the binary baseband signals to different constellation points to form complex signals required by transmission; the framing module is used for forming a specific frame structure required by transmission and is convenient for a receiving end to process; the radio frequency module is used for modulating a transmitting signal to a high-frequency carrier signal and transmitting the transmitting signal through an antenna; a least squares LS channel estimation module for providing initial channel information; a minimum mean square error (LMMSE) channel estimation module for providing iteratively updated channel information; the frequency domain equalization module is used for eliminating intersymbol interference on the received signal through the output of the channel estimation module; the soft decision module is used for providing an input signal of the channel estimation module based on the GCE-BEM and soft information input by the decoding module; and the channel decoding module is used for solving the final useful bit information from the soft information and outputting a signal detection result of the GCE-BEM iterative channel estimation system based on the sub-block transmission.
The equipment of the signal detection system of the GCE-BEM iterative channel estimation system based on sub-block transmission can be computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The signal detection device of the GCE-BEM iterative channel estimation system based on the sub-block transmission can comprise, but is not limited to, a processor and a memory. It will be appreciated by those skilled in the art that the foregoing is an example of a device for signal detection of a GCE-BEM iterative channel estimation system based on sub-block transmission, and does not constitute a limitation of a device for signal detection of a GCE-BEM iterative channel estimation system based on sub-block transmission, and may include more components than the foregoing, or may combine certain components, or different components, e.g., the device for signal detection of a GCE-BEM iterative channel estimation system based on sub-block transmission may further include an input/output device, a network access device, a bus, etc.
The memory may be used to store the computer program and/or the module, and the processor may implement various functions of the signal detection device of the GCE-BEM iterative channel estimation system based on sub-block transfer by running or executing the computer program and/or the module stored in the memory and invoking data stored in the memory.
The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart memory card (SmartMediaCard, SMC), secure digital (SecureDigital, SD) card, flash card (FlashCard), at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements the steps of the GCE-BEM iterative channel estimation method based on sub-block transmission.
The modules/units integrated with the signal detection system of the GCE-BEM iterative channel estimation system based on sub-block transmission may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a separate product.
The present invention realizes all or part of the above-mentioned processes in a GCE-BEM iterative channel estimation method based on sub-block transmission, and may also be accomplished by instructing related hardware by a computer program, where the computer program may be stored in a computer readable storage medium, and the computer program, when executed by a processor, may implement the steps of the above-mentioned GCE-BEM iterative channel estimation method based on sub-block transmission. The computer program comprises computer program code, and the computer program code can be in a source code form, an object code form, an executable file or a preset intermediate form and the like.
The computer readable storage medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM), a random access memory (RandomAccessMemory, RAM), an electrical carrier signal, a telecommunication signal, a software distribution medium, and so forth.
It should be noted that the computer readable storage medium may include content that is subject to appropriate increases and decreases as required by jurisdictions and by jurisdictions in which such computer readable storage medium does not include electrical carrier signals and telecommunications signals.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware.
Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, 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., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.

Claims (9)

1. The GCE-BEM iterative channel estimation method based on sub-block transmission is characterized by comprising the following steps of:
Step 1, adopting time domain Croneck TDKD pilot clusters based on sub-blocks as a transmission frame structure, wherein each single carrier frequency domain equalization SC-FDE block has M sub-blocks, each sub-block comprises pilot clusters of P TDKD structures, each pilot cluster consists of a data part and a pilot part, only one position in the middle of the pilot part is a non-zero pilot symbol, both sides of the pilot part are filled with zeros with a certain length as a guard interval, and the pilot symbols and the positions are known to a transmitting end and a receiving end;
step 2, coding bit information at a transmitting end, modulating the coded information to different constellation points through binary baseband signals to form complex signals required by transmission, blocking data according to the frame structure of the step 1, inserting local pilot frequency to complete framing operation, forming transmission frame signals, modulating the signals to high-frequency carrier signals, and transmitting the signals through an antenna;
step 3, the receiving antenna carries out timing synchronization according to the signals received in the step 2 to obtain receiving signals required by channel estimation, selects signals of pilot frequency positions, carries out primary channel estimation by adopting a least square LS channel estimation method based on GCE-BEM to obtain base coefficients corresponding to sub-blocks, estimates the base coefficients of the whole data block according to an oversampling complex exponential base function, and obtains initial channel information of the whole data block through the base coefficients;
Step 4, according to the received signal and the estimated channel information in the step 3, obtaining balanced soft decision information by a linear frequency domain equalization module and a soft decision module for the known symbol of the next channel estimation;
step 5, according to the known symbol obtained in the step 4, a minimum mean square error LMMSE channel estimation method based on GCE-BEM is adopted for the second channel estimation, the base coefficient of the sub-block in the SC-FDE block is obtained, the base coefficient of the whole data block is obtained, and finally the channel matrix H of the whole data block is fitted through an oversampling complex exponential base function;
step 6, according to the result of the second channel estimation obtained in step 5, sending the result to a frequency domain equalization module to obtain soft decision information of equalization output, wherein the soft decision information is used for the known symbol of the next iteration channel estimation; and when the iteration times are over, sequentially carrying out frequency domain equalization modules on the channel information obtained by the last iteration and the inter-symbol interference elimination of the received signal to obtain the finally required bit information.
2. The GCE-BEM iterative channel estimation method based on sub-block transmission according to claim 1, wherein the step 1 adopts a time domain kronecker TDKD pilot cluster based on sub-blocks as a transmission frame structure, and the specific process is as follows:
Step 1.1: each single carrier frequency domain equalization SC-FDE data block has a length of n=n s +N t Wherein N is s For the length of the information sequence, N t For training sequence length, it is divided into M equal-length sub-blocks, each of which has length N 0 The choice of M depends on the maximum doppler frequency and the length of the block;
step 1.2: each sub-block information symbol and training sequence are placed according to a time domain Cronecker TDKD pilot frequency placement method, firstly, each sub-block is divided into P pilot frequency clusters, wherein M epsilon {1, …, M } and P epsilon {1, …, P } are respectively the indexes of the M sub-block and the P small sub-block, and for each pilot frequency cluster, the length of the information symbol is N s,b ＝N s /(MP), training sequence length N t,b ＝N t /(MP)＝2L+1；
Step 1.3: adding L guard zeros to the end of each sub-block by setting the last L elements of each training sequence to zero, where L is the number of multipath channels;
when the single carrier frequency domain equalization SC-FDE data block x arrives at the receiving end through the dual-selection channel, the nth receiving symbol may be expressed as:
Figure FDA0004138094790000021
wherein z (n) is 0 as the mean and 0 as the variance
Figure FDA0004138094790000022
Additive white gaussian noise AWGN;
the above is written in matrix form:
y＝Hx+z
wherein z= [ z (0), z (1), …, z (N-1)] T ，y＝[y(0),y(1),…,y(N-1)] T H is an NxN dimensional matrix, and is specifically shown as follows:
Figure FDA0004138094790000031
Step 1.4: with GCE-BEM, the channel matrix H can be expressed as:
Figure FDA0004138094790000032
in the method, in the process of the invention,
Figure FDA0004138094790000033
C q is a toeplitz matrix, specifically shown in the following formula, and +.>
Figure FDA0004138094790000034
Figure FDA0004138094790000035
3. The method for iterative channel estimation of GCE-BEM based on sub-block transmission according to claim 1, wherein in the step 3, the first channel estimation is performed by using a least squares LS channel estimation method based on GCE-BEM, and the specific process is as follows:
step 3.1: when the iteration number i=0, knowing the data corresponding to the pilot frequency position of the received signal and the basis function matrix, obtaining the basis coefficient of each path by the following formula:
Figure FDA0004138094790000036
wherein y is t,l ＝[y(n 0,l ),…,y(n P-1,l )] T ,n p,l ＝n p +l,l＝0,1,…L；
Step 3.2: the basis function matrix corresponding to the pilot positions is expressed as:
Figure FDA0004138094790000041
the base coefficient matrix of the sub-block is obtained from the above
Figure FDA0004138094790000042
Base coefficient correlation matrix->
Figure FDA0004138094790000043
The initial channel information of the whole data block can be obtained through the GCE-BEM channel model.
4. The method for iterative channel estimation of GCE-BEM based on sub-block transmission according to claim 1, wherein the step 4 obtains the equalized soft decision information for the known symbol of the next channel estimation, and the specific process is as follows:
soft decision is carried out on the balanced output to obtain symbol soft information for the next iteration, and the soft information corresponding to the nth symbol in the p-th small sub-block of the m-th sub-block is obtained by the following formula:
Figure FDA0004138094790000044
In the method, in the process of the invention,
Figure FDA0004138094790000045
and->
Figure FDA0004138094790000046
Log likelihood ratio information representing the equalized output.
5. The GCE-BEM iterative channel estimation method based on sub-block transmission according to claim 1, wherein the LMMSE channel estimation method based on GCE-BEM in step 5 comprises the following specific steps:
step 5.1: when the iteration number i is more than 0, the symbol soft information obtained in the last iteration is inserted into a local pilot frequency as a known symbol to perform LMMSE channel estimation based on GCE-BEM;
step 5.2: the base coefficient of the m-th sub-block is obtained by:
Figure FDA0004138094790000047
in the method, in the process of the invention,
Figure FDA0004138094790000048
and (3) a base coefficient correlation matrix calculated for the last iteration result, wherein y (m) is a received signal.
Figure FDA0004138094790000049
U q,p (m) is U q A sub-matrix of a training symbol corresponding portion in the p-th sub-block of (m);
Figure FDA0004138094790000051
Figure FDA0004138094790000052
divided into two parts t p,n (m) and->
Figure FDA0004138094790000053
t p,n (m) pilot sequence reinserted for the receiving end,
Figure FDA0004138094790000054
the symbol soft information output by the equalization module in the previous iteration is composed;
step 5.3: the channel vector of the first path of the mth sub-block is derived by
Figure FDA0004138094790000055
Figure FDA0004138094790000056
In the method, in the process of the invention,
Figure FDA0004138094790000057
is the base coefficient vector of the first path, < +.>
Figure FDA0004138094790000058
The matrix of the basis function corresponding to the mth subblock has the dimension of N 0 ×(Q 0 +1), wherein>
Figure FDA0004138094790000059
Step 5.4: obtaining the base coefficient of the whole data block through M channel vectors obtained according to the step 5.3, wherein the vector dimension is N multiplied by 1:
Figure FDA00041380947900000510
The base coefficient vector corresponding to the first path of the entire data block can be obtained by:
Figure FDA00041380947900000511
wherein U= [ b ] 0 ,b 1 ,…,b Q ]Is a matrix of basis functions corresponding to a period of an SC-FDE data block, and has a dimension of N× (Q+1), wherein
Figure FDA0004138094790000061
Step 5.5: based on
Figure FDA0004138094790000062
Obtaining estimated base coefficient->
Figure FDA0004138094790000063
Then further obtaining an estimated base coefficient matrix +.>
Figure FDA0004138094790000064
The estimated channel matrix H is obtained by:
Figure FDA0004138094790000065
6. the method for iterative channel estimation of GCE-BEM based on sub-block transmission according to claim 1, wherein in step 6, the iterative channel estimation is performed by using a minimum mean square error LMMSE channel estimation method based on GCE-BEM, starting from the second channel estimation.
7. A GCE-BEM iterative channel estimation system based on sub-block transmission, comprising:
the channel coding module is used for coding the original bit information, and enhancing the reliability of the information in the transmission process by adding redundancy;
the constellation mapping module is used for modulating the binary baseband signals to different constellation points to form complex signals required by transmission;
the framing module is used for forming a specific frame structure required by transmission and is convenient for a receiving end to process;
The radio frequency module is used for modulating a transmitting signal to a high-frequency carrier signal and transmitting the transmitting signal through an antenna;
a least squares LS channel estimation module for providing initial channel information;
a minimum mean square error (LMMSE) channel estimation module for providing iteratively updated channel information;
the frequency domain equalization module is used for eliminating intersymbol interference on the received signal through the output of the channel estimation module;
the soft decision module is used for providing an input signal of the channel estimation module based on the GCE-BEM and soft information input by the decoding module;
and the channel decoding module is used for decoding the final useful bit information from the soft information.
8. A GCE-BEM iterative channel estimation device based on sub-block transmission, comprising:
a memory: a computer program for storing a GCE-BEM iterative channel estimation method implementing a sub-block transmission based method according to claims 1-6;
a processor: the GCE-BEM iterative channel estimation method based on sub-block transmission is realized when the computer program is executed.
9. A computer-readable storage medium, comprising:
the computer readable storage medium stores a computer program which, when executed by a processor, enables a GCE-BEM iterative channel estimation method based on sub-block transmission according to claims 1-6.
CN202310281371.0A 2023-03-22 2023-03-22 GCE-BEM iterative channel estimation method, system, equipment and medium based on sub-block transmission Pending CN116319193A (en)

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CN117081894A (en) * 2023-07-17 2023-11-17 中国科学院声学研究所 Underwater sound signal detection method utilizing channel sparse characteristics
CN117081894B (en) * 2023-07-17 2024-05-24 中国科学院声学研究所 Underwater sound signal detection method and system utilizing channel sparse characteristics

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