CN107124213B - Direct self-adaptive bidirectional turbo equalization method in multi-input multi-output system - Google Patents
Direct self-adaptive bidirectional turbo equalization method in multi-input multi-output system Download PDFInfo
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Abstract
The invention relates to a bidirectional turbo equalizing method based on direct self-adaptation in a multiple-input multiple-output system, which is applied to a bidirectional soft decision feedback equalizer, wherein the bidirectional soft decision feedback equalizer comprises a conventional soft decision feedback equalizer and a soft decision feedback equalizer with time reversal; the soft decision feedback equalizer with time reversal is respectively connected with a component capable of realizing time reversal operation at the input end and the output end of the conventional soft decision feedback equalizer; the method comprises the following steps: in the bidirectional soft decision feedback equalizer, the received signal and the feedback signal of the received signal are utilized to carry out bidirectional turbo equalization, and the result of the forward turbo equalization and the result of the backward turbo equalization are linearly added to realize the estimation of the signal. The method has the advantages of strong stability, high detection precision, fast algorithm convergence and low complexity, and has certain practical value and application prospect in the actual underwater acoustic communication system.
Description
Technical Field
The invention relates to the field of underwater acoustic communication, in particular to a direct self-adaptive bidirectional turbo equalization method in a multi-input multi-output system.
Background
Because the underwater acoustic channel bandwidth is limited, a Single Input Single Output (SISO) system is difficult to meet the actual communication requirement, and a Multiple-Input Multiple-Output (MIMO) system is researched more. In the MIMO system, a transmitting end adopts multi-array element transmission to improve the communication rate, and a receiving end adopts multi-array element reception to obtain diversity gain. In addition to intersymbol interference and error propagation, the MIMO system also has co-channel interference between multi-channel transmissions, which limits the practical application of the MIMO system.
For co-channel interference, in reference 1 "a.song, m.badiey, v.k.mcdonald, and t.c.yang," Time reversal receivers for high data rate associated multiple-input-multiple-output communication, "IEEE j.ocean.eng., vol.36, No.4, pp.525-538, oct.2011", Song et al proposes to adopt a Time reversal technique, design a filter using an estimated Time reversal form of a channel impulse response, achieve signal focusing, and add techniques such as parallel interference cancellation and serial interference cancellation, and restore an original signal. In addition, in reference 2 "m.t.tuchler, r.koetter, and a.c.singer," Turbo equalization: Principles and newresults, "IEEE trans.comm., vol.50, No.5, pp.754-767, May 2002", Tuchler et al propose a minimum mean square error based decision feedback equalizer (MMSE-DFE) in MIMO systems, and use the estimated channel impulse response to achieve signal deconvolution.
For the error propagation phenomenon in DFE, in reference 3 "w.dual and y.r.zheng," Bidirectional soft-decision feedback turbo equalization for MIMO systems, "IEEE trans.veh.technology, pp.1-11, aug.2015", Duan et al proposed the idea of Bidirectional equalization, and utilized the randomness of error propagation and the very low correlation of forward and backward equalization results to linearly add the output results of Bidirectional equalization to obtain diversity gain. In addition, in order to improve the equalization performance of the MIMO system, the bidirectional DFE and the turbo equalization are combined, and the error rate is further reduced.
Aiming at the complex and variable underwater acoustic channel environment and the requirement of high-speed underwater acoustic communication, the disadvantages of the equalization scheme in the existing MIMO system mainly exist in the following aspects:
1. the equalizer in the existing MIMO system needs channel estimation, whether based on time reversal or MMSE-DFE, the channel estimation algorithm involves multiplication and inversion operations of large-scale matrix, and the computation complexity is too high to be beneficial to real-time processing of data.
2. The turbo equalization method in existing MIMO systems relies heavily on the accuracy of the channel estimation. For time reversal operation, if the signal focusing fails due to inaccurate channel estimation, diversity gain cannot be obtained; for MMSE-DFE based systems, filter tap coefficients will be inaccurate if the channel estimate is inaccurate, thereby rendering the equalizer and decoder ineffective. In addition, in a time-varying underwater acoustic channel environment, the channel impulse response estimated by the training sequence hardly represents the channel impulse response of effective data, which makes the existing equalization method not highly stable.
3. The number of filters in the MIMO system is more than that of the SISO system, which increases the calculation complexity of the algorithm, the existing turbo equalization method does not utilize the sparsity of the impulse response of the underwater sound channel and lacks an effective fast self-adaptive algorithm, so that the redundancy of signal processing is too large, the convergence is too slow, and the real-time processing of data is influenced.
Disclosure of Invention
The invention aims to overcome the defects of the equalization method in the existing MIMO system, thereby providing a method with high detection precision and high stability.
In order to achieve the above object, the present invention provides a bidirectional turbo equalization method based on direct adaptation in a mimo system, which is applied in a bidirectional soft decision feedback equalizer, wherein the bidirectional soft decision feedback equalizer comprises a conventional soft decision feedback equalizer and a soft decision feedback equalizer with time reversal; the input end and the output end of the conventional soft decision feedback equalizer are respectively connected with a component capable of realizing time reversal operation; the method comprises the following steps:
in the bidirectional soft decision feedback equalizer, the received signal and the feedback signal of the received signal are utilized to carry out bidirectional turbo equalization, and the result of the forward turbo equalization and the result of the backward turbo equalization are linearly added to realize the estimation of the signal.
In the above technical solution, the method further comprises:
step 1), dividing an original signal sequence into N paths at a transmitting end, and placing a training sequence at the head and the tail of each path of signal; the training sequence is a group of data known by a receiving end, and the training sequences among all paths of signals are not related to each other;
step 2), after any array element in the receiving end receives a signal which is transmitted by the transmitting end and contains a training sequence, inputting the received signal into a bidirectional soft decision feedback equalizer;
step 3), in the soft decision feedback equalizer with time reversal, time reversal operation is carried out on the received signals, and then the signals after time reversal are copied into N paths; in a conventional soft decision feedback equalizer, directly copying a received signal into N paths;
step 4), in the soft decision feedback equalizer with time reversal and the conventional soft decision feedback equalizer, the filter coefficient is adjusted by using the corresponding training sequence in the N paths of signals obtained by copying, and the transmitted signals of other paths are regarded as 0dB noise;
step 5), summing signals received by each array element of a receiving end;
step 6), performing decision feedback equalization on the N paths of signals obtained after summation and the feedback signal obtained in the previous iteration; wherein, when the feedback equalization is judged for the first time, the feedback signal is 0;
step 7), sequentially performing demapping and parallel-serial conversion on the sequence after the decision feedback equalization, performing time reversal operation on a result after the parallel-serial conversion in the soft decision feedback equalizer with time reversal, and outputting the result; in the conventional soft decision feedback equalizer, the result after parallel-serial conversion is directly output;
step 8), performing bidirectional combination on the output of the soft decision feedback equalizer with time reversal and the output of the conventional soft decision feedback equalizer, and then sequentially performing de-interleaving and decoding on the result of the bidirectional combination;
step 9), judging whether the bidirectional soft decision feedback equalizer is converged, if not, executing the next step, otherwise, outputting a decoding result;
step 10), interweaving decoding results, and then respectively inputting a soft decision feedback equalizer with time reversal and a conventional soft decision feedback equalizer; before inputting the soft decision feedback equalizer with time reversal, time reversal operation is needed to be carried out on the interleaving result;
step 11), performing serial-to-parallel conversion on the signals input into the soft decision feedback equalizer with time reversal and the conventional soft decision feedback equalizer, then dividing the signals into N paths again to obtain feedback signals, wherein corresponding training sequences in the feedback signals are used for adjusting filter coefficients, and then executing step 6).
In the above technical solution, after the first decision feedback equalization, the following steps are further included:
obtaining filter coefficients in a bidirectional soft decision feedback equalizer, setting a threshold according to the average energy of the coefficients, deleting the filter coefficients smaller than the threshold, keeping the coefficients larger than the threshold, and recording the positions of the coefficients.
In the above technical solution, in step 4) and step 11), the filter coefficient is adjusted by using the fastest self-optimization algorithm of the embedded digital phase-locked loop.
In the above technical solution, in step 9), the determining whether the bidirectional soft decision feedback equalizer converges includes: and judging whether the ratio of the bit error rate to the previous bit error rate is reduced, if the bit error rate is not reduced any more, converging the bidirectional soft decision feedback equalizer, and if not, not converging the bidirectional soft decision feedback equalizer.
The invention has the advantages that:
1. the invention provides a direct self-adaptive bidirectional turbo equalization method for an MIMO system, which does not need channel estimation, realizes co-channel interference elimination by using soft information fed back by turbo equalization, avoids multiplication and inversion operations of a large-dimension matrix, greatly reduces algorithm complexity and improves algorithm efficiency, and an equalizer adopted by the method is based on a direct self-adaptive algorithm, is not easily influenced by a time-varying channel and has high algorithm stability.
2. On the basis of a SISO system, the invention expands a bidirectional SDFE (soft decision feedback equalizer) structure into an MIMO system, utilizes the randomness of error propagation to linearly sum output results of two SDFEs, effectively inhibits the error propagation, improves the detection precision and reduces the bit error rate.
3. In the method, the adjustment of the equalizer coefficient adopts a sparse algorithm, so that the problem of overlarge calculation amount of an MIMO system is solved, and in addition, the adjustment of the equalizer coefficient adopts an FOLMS algorithm embedded with DPLL, so that the iteration step length in the algorithm is adaptively adjusted along with the error, and the detection precision and the algorithm efficiency are further improved.
4. The direct self-adaptive bidirectional turbo equalization method in the MIMO system has the advantages of strong stability, high detection precision, quick algorithm convergence and low complexity, and has certain practical value and application prospect in an actual underwater acoustic communication system.
Drawings
Fig. 1 is a flow chart of a direct adaptive based bi-directional turbo equalization method of the present invention;
FIGS. 2(a) -2 (f) are diagrams of constellations after one, three, five times of iterative equalization and decoding;
FIG. 3 is a EXIT comparison chart of the unidirectional equalization (DA-TEQ) and the bidirectional equalization (DA-BTEQ) under different numbers of receiving array elements.
Detailed Description
The invention will now be further described with reference to the accompanying drawings.
Description of the problem
In the MIMO system, assuming that the number of transmitting array elements is N, the number of receiving array elements is M, the underwater acoustic channel is modeled as finite channel impulse response, and the noise is Additive White Gaussian Noise (AWGN), then the received signal r of the mth array element at the kth time ism,kComprises the following steps:
wherein s isn,kFor the transmitted signal of the nth array element at the kth time, hn,m,l-kFor the channel impulse response between the nth transmitting array element and the mth receiving array element at the l-k time, vm,kThe gaussian white noise is superimposed on the mth receiving array element at the kth moment.
As can be seen from equation (1), in the MIMO system, if each transmitted signal is to be distinguished from the received signal, the co-channel interference needs to be eliminated (the first summation expression in equation (1) represents the co-channel interference) in addition to the inter-symbol interference (the second summation expression in equation (1) represents the inter-symbol interference). Furthermore, the impulse response of the hydro-acoustic channel may last tens or hundreds of symbols long, which undoubtedly increases computational complexity.
The two-way turbo equalization method based on direct self-adaptation of the invention needs to adopt a two-way soft decision feedback equalizer (BiSDFE) when being realized, the two-way soft decision feedback equalizer comprises two Soft Decision Feedback Equalizers (SDFE), wherein one soft decision feedback equalizer is a soft decision feedback equalizer in the prior art, and the other soft decision feedback equalizer is a soft decision feedback equalizer with time reversal. The soft decision feedback equalizer with time reversal is the same as the soft decision feedback equalizer in the prior art in terms of main structure, and only a component capable of realizing time reversal operation is needed at the input end and the output end respectively.
Referring to fig. 1, the direct adaptive based bidirectional turbo equalization method of the present invention includes:
step 1), dividing an original signal sequence into N paths at a transmitting end, and placing a training sequence at the head and the tail of each path of signal; the training sequence is a group of data known by a receiving end, and the training sequences among all paths of signals are not related to each other, so that all paths of signals are distinguished according to the training sequences;
step 2), after any array element in the receiving end receives the signal which is transmitted by the transmitting end and contains the training sequence, the received signal is input into a bidirectional soft decision feedback equalizer, namely the received signal is respectively input into a conventional soft decision feedback equalizer and a soft decision feedback equalizer with time reversal;
step 3), in the soft decision feedback equalizer with time reversal, time reversal operation is carried out on the received signals, and then the signals after time reversal are copied into N paths; in a conventional soft decision feedback equalizer, directly copying a received signal into N paths;
wherein, the time reversal operation refers to the sequence inversion, for example, the sequence [ 1234567 ] is changed to [ 7654321 ] through the time reversal operation;
step 4), in the soft decision feedback equalizer with time reversal and the conventional soft decision feedback equalizer, adjusting filter coefficients (f and f' shown in fig. 1) by using corresponding training sequences in the copied N paths of signals to eliminate intersymbol interference, wherein the transmitted signals of other paths are regarded as 0dB noise;
in the step, because the intensity of 0dB noise is too large, the equalization effect is not too good at this time, and loop iteration is needed in the subsequent steps;
step 5), summing all paths of signals received by all array elements of a receiving end, wherein because each path of signal is copied into N paths before, N paths of results are obtained after summing in a soft decision feedback equalizer with time reversal or a conventional soft decision feedback equalizer;
step 6), performing decision feedback equalization on the N paths of signals obtained after summation and the feedback signal obtained in the previous iteration; wherein, when the first iterative computation is carried out, the feedback signal is 0;
step 7), sequentially performing demapping and parallel-serial conversion on the sequence (including the training sequence and the actual information sequence) after the decision feedback equalization, performing time reversal operation on a result after the parallel-serial conversion in the soft decision feedback equalizer with time reversal, and then outputting the result; in the conventional soft decision feedback equalizer, the result after parallel-serial conversion is directly output;
step 8), performing bidirectional combination on the output of the soft decision feedback equalizer with time reversal and the output of the conventional soft decision feedback equalizer, and then sequentially performing de-interleaving and decoding on the result of the bidirectional combination;
step 9), judging whether the bidirectional soft decision feedback equalizer is converged, if not, executing the next step, otherwise, outputting a decoding result;
in this step, determining whether the bi-directional soft decision feedback equalizer converges comprises: judging whether the ratio of the bit error rate to the previous bit error rate is reduced, if the bit error rate is not reduced any more, then the bidirectional soft decision feedback equalizer is converged, otherwise, the bidirectional soft decision feedback equalizer is not converged;
step 10), interweaving decoding results, and then respectively inputting a soft decision feedback equalizer with time reversal and a conventional soft decision feedback equalizer; before inputting the soft decision feedback equalizer with time reversal, time reversal operation is needed to be carried out on the interleaving result;
step 11), performing serial-to-parallel conversion on the signals input into the soft decision feedback equalizer with time reversal and the conventional soft decision feedback equalizer, and then dividing the signals into N paths again to obtain feedback signals, wherein corresponding training sequences in the feedback signals are used for adjusting filter coefficients (such as b and b' in fig. 1), and then the feedback signals can be further applied to the decision feedback equalization operation in step 6).
The above is one implementation of the direct adaptive based bidirectional turbo equalization method of the present invention. In another embodiment, as a preferred implementation, a step of reducing the computational complexity by using a coefficient sparsification algorithm is further included.
Because the MIMO system is adopted for communication in the invention, the number of the filters in the equalizer is N multiplied by M times of that of the SISO system, which will undoubtedly increase the calculation amount, thereby influencing the real-time processing of the signals. Therefore, the coefficient sparsification algorithm is adopted to reduce the calculation complexity, and the algorithm is described as follows: after the first equalization, the coefficients of the filter in the equalizer are obtained, a threshold is set based on the average energy of the coefficients, coefficients smaller than the threshold are deleted, coefficients larger than the threshold are retained, and the positions of the coefficients are recorded.
Because the smaller coefficients contribute little to the filtering result, the influence on the final result after deletion is not great, and in the subsequent iteration process, the retained coefficients only need to be adjusted, so that the calculation complexity is reduced.
In another embodiment, in step 4) and step 11) of the method of the present invention, a steepest self-optimization algorithm (foms) of an embedded digital phase-locked loop (DPLL) is further used to adjust the filter coefficients, so that the method of the present invention converges quickly.
The above is a description of the method of the present invention. As can be seen from the description of the steps of the method of the present invention, the method of the present invention utilizes the feedback signal to eliminate the intersymbol interference and the co-channel interference. In view of the disadvantages of error propagation associated with the combination of SDFE and turbo equalization, the method of the present invention also takes advantage of the diversity of bi-directional equalization to eliminate error propagation. This is because error propagation has randomness and the position where an error occurs in forward equalization does not necessarily occur in reverse equalization, and therefore, the results of forward equalization and reverse equalization are linearly added, and error propagation can be effectively eliminated.
Performance analysis
To verify the validity of the algorithm, the applicant performed lake test experiments. The experimental time was 11 months of 2015, and the place was thousand island lake. A2 x 4 MIMO system is adopted for communication, the transmitting transducer and the receiving transducer are placed at 15m under water, the interval between every two transducers is 1m, and the communication distance is 2000 m. The transmitting signal adopts QPSK modulation and RSC coding, the generator polynomial of an encoder is [5,7], the signal center frequency is 12KHz, the bandwidth of the transducer is 9-15 KHz, the sampling rate is 96KHz, the symbol rate of single-array element transmission is 6KHz, and the symbol rate of double-array element transmission is 12 KHz. The transmitting signals are synchronized by hyperbolic frequency modulation signals, the guard interval is 2048 sampling points, the number of training symbols is 200, and the number of information symbols is 1936. We use the two-way turbo equalization algorithm for signal detection and give a constellation and an outer information transfer graph (EXIT).
Fig. 2(a) -fig. 2(f) show the constellation after one, three, five times of iterative equalization and decoding. As shown in the figure, the constellation diagrams after five times of iterative equalization do not significantly converge, the bit error rate is only 0.0102, the bit error rate cannot be further reduced by increasing the number of iterations, and the constellation diagrams after decoding obviously converge to four quadrants, and the bit error rate is 0. This shows that zero bit error transmission can be realized under 2000m channel by adopting the bidirectional turbo method in the MIMO system provided by the invention. In order to compare the method provided by the invention with other methods in the prior art, the applicant also provides an EXIT graph comparison of unidirectional equalization (DA-TEQ) and bidirectional equalization (DA-BTEQ) under the condition of different numbers of receiving array elements. As shown in fig. 3, the larger the number of receiving array elements, the farther the EXIT curve is propagated, which means the lower bit error rate. For 2 × 4 uni-directional turbo equalization, the extrinsic information at the final convergence is 0.84, and the extrinsic information at the convergence of the bi-directional turbo equalization is 1, which indicates that the bi-directional equalization performance is better. Under the condition of other numbers of receiving array elements, the performance of the bidirectional equalization is superior to that of the unidirectional equalization, which indicates that error propagation may exist in the unidirectional equalization, so that the equalizer fails, and the bidirectional equalization can effectively eliminate the error propagation and reduce the bit error rate. In conclusion, the bidirectional turbo equalization method provided by the invention successfully inhibits error propagation, obtains diversity gain of multi-array element reception, and can effectively realize signal detection.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (5)
1. A two-way turbo equalization method based on direct self-adaptation in a multiple-input multiple-output system is applied to a two-way soft decision feedback equalizer, wherein the two-way soft decision feedback equalizer comprises a conventional soft decision feedback equalizer and a soft decision feedback equalizer with time reversal; the input end and the output end of the conventional soft decision feedback equalizer are respectively connected with a component capable of realizing time reversal operation; the method comprises the following steps:
in the bidirectional soft decision feedback equalizer, the received signal and the feedback signal of the received signal are utilized to carry out bidirectional turbo equalization, and the result of the forward turbo equalization and the result of the backward turbo equalization are linearly added to realize the estimation of the signal.
2. The direct-adaptation-based bidirectional turbo equalization method in the mimo system of claim 1, further comprising:
step 1), dividing an original signal sequence into N paths at a transmitting end, and placing a training sequence at the head and the tail of each path of signal; the training sequence is a group of data known by a receiving end, and the training sequences among all paths of signals are not related to each other;
step 2), after any array element in the receiving end receives a signal which is transmitted by the transmitting end and contains a training sequence, inputting the received signal into a bidirectional soft decision feedback equalizer;
step 3), in the soft decision feedback equalizer with time reversal, time reversal operation is carried out on the received signals, and then the signals after time reversal are copied into N paths; in a conventional soft decision feedback equalizer, directly copying a received signal into N paths;
step 4), in the soft decision feedback equalizer with time reversal and the conventional soft decision feedback equalizer, the filter coefficient is adjusted by using the corresponding training sequence in the N paths of signals obtained by copying, and the transmitted signals of other paths are regarded as 0dB noise;
step 5), summing signals received by each array element of a receiving end;
step 6), performing decision feedback equalization on the N paths of signals obtained after summation and the feedback signal obtained in the previous iteration; wherein, when the feedback equalization is judged for the first time, the feedback signal is 0;
step 7), sequentially performing demapping and parallel-serial conversion on the sequence after the decision feedback equalization, performing time reversal operation on a result after the parallel-serial conversion in the soft decision feedback equalizer with time reversal, and outputting the result; in the conventional soft decision feedback equalizer, the result after parallel-serial conversion is directly output;
step 8), performing bidirectional combination on the output of the soft decision feedback equalizer with time reversal and the output of the conventional soft decision feedback equalizer, and then sequentially performing de-interleaving and decoding on the result of the bidirectional combination;
step 9), judging whether the bidirectional soft decision feedback equalizer is converged, if not, executing the next step, otherwise, outputting a decoding result;
step 10), interweaving decoding results, and then respectively inputting a soft decision feedback equalizer with time reversal and a conventional soft decision feedback equalizer; before inputting the soft decision feedback equalizer with time reversal, time reversal operation is needed to be carried out on the interleaving result;
step 11), performing serial-to-parallel conversion on the signals input into the soft decision feedback equalizer with time reversal and the conventional soft decision feedback equalizer, then dividing the signals into N paths again to obtain feedback signals, wherein corresponding training sequences in the feedback signals are used for adjusting filter coefficients, and then executing step 6).
3. The direct-adaptation-based bidirectional turbo equalization method in the mimo system according to claim 2, further comprising the following steps after the first decision feedback equalization:
obtaining filter coefficients in a bidirectional soft decision feedback equalizer, setting a threshold according to the average energy of the coefficients, deleting the filter coefficients smaller than the threshold, keeping the coefficients larger than the threshold, and recording the positions of the coefficients.
4. The direct-adaptation-based bidirectional turbo equalization method in the mimo system according to claim 2 or 3, wherein the filter coefficients are adjusted in step 4) and step 11) by using a fastest self-optimization algorithm with an embedded digital phase-locked loop.
5. The direct-adaptation-based bi-directional turbo equalization method in the mimo system according to claim 2 or 3, wherein the determining whether the bi-directional soft decision feedback equalizer converges in step 9) comprises: and judging whether the ratio of the bit error rate to the previous bit error rate is reduced, if the bit error rate is not reduced any more, converging the bidirectional soft decision feedback equalizer, and if not, not converging the bidirectional soft decision feedback equalizer.
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| CN109981501B (en) * | 2019-03-08 | 2023-03-31 | 哈尔滨工程大学 | Underwater sound direct self-adaptive MIMO communication method |
| CN109995474A (en) * | 2019-03-29 | 2019-07-09 | 舟山美通信息技术有限责任公司 | A kind of SISO communication equipment implementation based on SDFE and Turbo code iterative equalization and decoding |
| CN111147157B (en) * | 2019-12-30 | 2021-04-23 | 浙江大学 | Soft interference elimination Turbo equalization method for orthogonal signal division multiplexing on underwater acoustic channel |
| CN112202456B (en) * | 2020-10-24 | 2022-04-29 | 青岛鼎信通讯股份有限公司 | Turbo decoding method for broadband power line carrier communication |
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| CN104603853A (en) * | 2012-05-04 | 2015-05-06 | 李尔登公司 | System and methods for coping with doppler effects in distributed-input distributed-output wireless systems |
| CN105323203A (en) * | 2015-11-12 | 2016-02-10 | 哈尔滨工程大学 | Anti-multipath underwater acoustic communication based on orthogonal carrier sweep expansion technology |
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