CN115208480A - Under-ice underwater acoustic communication method based on joint message transfer - Google Patents

Under-ice underwater acoustic communication method based on joint message transfer Download PDF

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CN115208480A
CN115208480A CN202210770475.3A CN202210770475A CN115208480A CN 115208480 A CN115208480 A CN 115208480A CN 202210770475 A CN202210770475 A CN 202210770475A CN 115208480 A CN115208480 A CN 115208480A
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韩笑
朱广军
殷敬伟
郭龙祥
葛威
李林
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Harbin Xinguang Photoelectric Technology Co ltd
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    • H—ELECTRICITY
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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
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
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    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
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Abstract

本发明提供一种基于联合消息传递的冰下水声通信方法，基于单载波相移键控调制体制，信源比特信息经过信道编码器和随机交织器处理后映射为符号。确定接收端通信信号的解码流程。通信接收机充分利用水声通信信道的稀疏性，采用置信传播求取估计符号的后验概率，以此来降低接收机的计算复杂度。同时，采用期望传播将传递的消息投影到指数分布族中来获取精确的近似后验概率。接收机采用三层迭代结构，第一层为循环置信传播迭代过程，第二层为期望传播迭代过程，第三次为turbo迭代过程。本发明能够在获得与线性最小均方误差均衡器(LMMSE)相当的性能的同时极大地降低计算复杂度。

Figure 202210770475

The invention provides an underwater acoustic communication method based on joint message transfer. Based on a single carrier phase shift keying modulation system, the source bit information is processed by a channel encoder and a random interleaver and then mapped into symbols. Determine the decoding process of the communication signal at the receiving end. The communication receiver makes full use of the sparsity of the underwater acoustic communication channel, and uses belief propagation to obtain the posterior probability of the estimated symbol, thereby reducing the computational complexity of the receiver. At the same time, expected propagation is used to project the delivered message onto a family of exponential distributions to obtain exact approximate posteriors. The receiver adopts a three-layer iterative structure. The first layer is an iterative process of cyclic belief propagation, the second layer is an iterative process of expectation propagation, and the third is a turbo iterative process. The present invention can greatly reduce the computational complexity while obtaining performance comparable to a Linear Minimum Mean Square Error Equalizer (LMMSE).

Figure 202210770475

Description

一种基于联合消息传递的冰下水声通信方法A method for underwater acoustic communication under ice based on joint message passing

技术领域technical field

本发明涉及水声通信领域，更确切地说，涉及一个能够有效降低水声通信接收机复杂度且易于扩展到多阵元系统中方法。The invention relates to the field of underwater acoustic communication, more specifically, to a method that can effectively reduce the complexity of an underwater acoustic communication receiver and is easy to extend to a multi-array element system.

背景技术Background technique

随着全球气候变暖，两极冰盖逐渐消退，北极地区因为其丰富的水下资源和天然的航道优势逐渐人们关注的焦点。而水声通信技术被广泛应用于水下资源勘探、海洋环境监测、数据采集、探测预警等领域。因此，水声通信技术的研究和发展在北极开发的过程中具有重要意义。With global warming, the polar ice caps are gradually receding, and the Arctic region has gradually become the focus of attention because of its rich underwater resources and natural waterway advantages. The underwater acoustic communication technology is widely used in underwater resource exploration, marine environment monitoring, data acquisition, detection and early warning and other fields. Therefore, the research and development of underwater acoustic communication technology is of great significance in the process of Arctic development.

通过对北极实验数据的分析，发现北极冰下水声信道具有丰富的多途时延结构，并且在远距离数据传输时，多途时延能够达到100ms以上。然而，由于北极地区人类活动较少，以及存在冰层覆盖的原因，采集数据的信噪比较高，而且更重要的是其信道的时变较慢。针对北极冰下水声信道的这些特性，传统的高复杂度的自适应信道跟踪的均衡方法不再适用于该场景，而基于信道估计的逐块均衡方法虽然具有相对较低计算复杂度，但是在面临大多途时延的情况，仍然具有较高的计算复杂度。更重要的是，在多阵元系统的情况下，计算复杂度的问题更加尖锐，这使得传统算法在水下通信设备中应用会受到极大的阻碍。Through the analysis of the Arctic experimental data, it is found that the underwater acoustic channel under the Arctic ice has a rich multi-path delay structure, and the multi-path delay can reach more than 100ms during long-distance data transmission. However, due to less human activity in the Arctic and the presence of ice cover, the signal-to-noise ratio of the acquired data is high and, more importantly, the time-varying of the channel is slow. In view of these characteristics of the underwater acoustic channel under the Arctic ice, the traditional high-complexity adaptive channel tracking equalization method is no longer suitable for this scenario, while the block-by-block equalization method based on channel estimation has relatively low computational complexity, but in In the face of large multi-way delay, it still has high computational complexity. More importantly, in the case of a multi-array element system, the problem of computational complexity is more acute, which greatly hinders the application of traditional algorithms in underwater communication equipment.

因此，针对北极冰下慢时变信道特征，低复杂度的多阵元系统接收机算法的研究具有重大意义。Therefore, for the slow time-varying channel characteristics under the arctic ice, the research on the receiver algorithm of the low-complexity multi-element system is of great significance.

发明内容SUMMARY OF THE INVENTION

本发明的目的在于提供一种基于联合消息传递的冰区水声通信方法。The purpose of the present invention is to provide an underwater acoustic communication method in ice area based on joint message transfer.

本发明的目的是这样实现的：步骤如下：The purpose of this invention is to realize like this: step is as follows:

(1)接收阵采集的接收符号为y＝[y1，...，ym，...，yM]，其中，M为接收阵元的个数，ym为第m个接收阵元上接收的符号。发射的比特数据序列b＝[b1，...，bn，...，bN]经过卷积编码、交织和映射得到发射符号x＝[x1，...，xn，...，xN]，xn为第n个发射阵元发射的符号。且接收端已知发射信号的导频序列

Figure BDA0003723777620000011
其中，
Figure BDA0003723777620000012
为第n个发射阵元发射的导频序列。根据接收数据设置接收机三层迭代的次数，即BP最大迭代次数U，EP最大迭代次数S，迭代均衡与解码次数T.(1) The received symbols collected by the receiving array are y=[y 1 ,...,y m ,...,y M ], where M is the number of receiving array elements, and y m is the mth receiving array The symbol received on the element. The transmitted bit data sequence b = [ b 1 , . . . , bn , . .., x N ], x n is the symbol transmitted by the nth transmitting array element. And the receiver knows the pilot sequence of the transmitted signal
Figure BDA0003723777620000011
in,
Figure BDA0003723777620000012
It is the pilot sequence transmitted by the nth transmitting array element. Set the number of three-layer iterations of the receiver according to the received data, that is, the maximum iteration number U of BP, the maximum number of iterations S of EP, and the number of iteration equalization and decoding T.

(2)信道估计。在第一次turbo迭代中，使用已知的导频序列

Figure BDA0003723777620000013
和低复杂度稀疏贝叶斯算法估计信道。在后续的turbo迭代中，根据反馈的先验符号
Figure BDA0003723777620000021
进行信道估计。(2) Channel estimation. In the first turbo iteration, a known pilot sequence is used
Figure BDA0003723777620000013
and a low-complexity sparse Bayesian algorithm to estimate the channel. In subsequent turbo iterations, according to the feedback prior sign
Figure BDA0003723777620000021
Perform channel estimation.

(3)计算变量节点传递到均衡节点的消息

Figure BDA0003723777620000022
表示为(3) Calculate the message sent by the variable node to the balance node
Figure BDA0003723777620000022
Expressed as

Figure BDA0003723777620000023
Figure BDA0003723777620000023

其中，

Figure BDA0003723777620000024
表示第k个变量节点传递到第j个均衡节点的消息，
Figure BDA0003723777620000025
和
Figure BDA0003723777620000026
表示该高斯分布消息的均值和方差，k∈[1，K]，j∈[1，J]。in,
Figure BDA0003723777620000024
represents the message delivered by the kth variable node to the jth balance node,
Figure BDA0003723777620000025
and
Figure BDA0003723777620000026
Represents the mean and variance of this Gaussian distributed message, k∈[1,K], j∈[1,J].

(4)计算均衡节点传递到变量节点的消息

Figure BDA0003723777620000027
表示为(4) Calculate the message that the balance node transmits to the variable node
Figure BDA0003723777620000027
Expressed as

Figure BDA0003723777620000028
Figure BDA0003723777620000028

(5)判断：若当前迭代次数u＝U成立，则执行下一步。否则重复进行步骤(3)-(5)BP迭代过程。(5) Judgment: if the current iteration number u=U is established, execute the next step. Otherwise, repeat steps (3)-(5) BP iterative process.

(6)计算变量节点传递到解映射节点的消息

Figure BDA0003723777620000029
表示为(6) Calculate the message that the variable node passes to the demapping node
Figure BDA0003723777620000029
Expressed as

Figure BDA00037237776200000210
Figure BDA00037237776200000210

其中，

Figure BDA00037237776200000211
和
Figure BDA00037237776200000212
表示该消息的均值和方差。in,
Figure BDA00037237776200000211
and
Figure BDA00037237776200000212
Represents the mean and variance of the message.

(7)计算解映射节点到变量节点的消息

Figure BDA00037237776200000213
表示为(7) Calculate the message from the demap node to the variable node
Figure BDA00037237776200000213
Expressed as

Figure BDA00037237776200000214
Figure BDA00037237776200000214

其中，上式中高斯分布消息的均值和方差通过矩匹配得到。Among them, the mean and variance of Gaussian distributed messages in the above formula are obtained by moment matching.

(8)判断：若当前迭代次数s＝S成立，则执行下一步。否则，重复进行步骤(3)-(8)EP迭代过程。(8) Judgment: if the current number of iterations s=S is established, execute the next step. Otherwise, repeat steps (3)-(8) EP iteration process.

(9)解映射节点传递到比特节点的消息Le(dk，j)，Le(dk，j)表示对数似然比(LLR)。(9) The message Le (d k,j ) passed by the demapping node to the bit node, Le (d k, j ) represents the log-likelihood ratio (LLR).

(10)根据解映射节点传递到比特节点的消息，进行解交织和信道解码，将信道解码器输出的外信息作为下一次turbo迭代的先验信息。(10) Perform deinterleaving and channel decoding according to the message transmitted by the demap node to the bit node, and use the external information output by the channel decoder as the prior information of the next turbo iteration.

(11)判断：若当前迭代次数t＝T成立，则将信道解码器的输出

Figure BDA00037237776200000215
作为最终的解码结果。否则，重复进行步骤(2)-(11)turbo迭代过程。(11) Judgment: if the current iteration number t=T is established, then the output of the channel decoder is
Figure BDA00037237776200000215
as the final decoding result. Otherwise, repeat the turbo iteration process of steps (2)-(11).

与现有技术相比，本发明的有益效果是：本发明与传统的LMMSE均衡水声通信方法不同之处在于充分利用了水声信道的稀疏性，使用置信传播算法求估计符号的后验概率，该算法的计算复杂度仅与稀疏水声信道的非零系数个数有关，极大地降低了计算复杂度。同时，使用期望传播算法来获得更加精确的近似后验概率。本算法能够简单高效地从SISO系统扩展到SIMO和MIMO或MU系统中，从而有效地降低多阵元系统的计算复杂度。Compared with the prior art, the beneficial effects of the present invention are: the difference between the present invention and the traditional LMMSE equalized underwater acoustic communication method is that the sparsity of the underwater acoustic channel is fully utilized, and the posterior probability of the estimated symbol is obtained by using the belief propagation algorithm. , the computational complexity of the algorithm is only related to the number of non-zero coefficients of the sparse underwater acoustic channel, which greatly reduces the computational complexity. At the same time, the expectation propagation algorithm is used to obtain a more accurate approximate posterior probability. This algorithm can be easily and efficiently extended from SISO system to SIMO and MIMO or MU system, thus effectively reducing the computational complexity of multi-element system.

附图说明Description of drawings

图1是基于联合消息传递的北极冰下水声通信技术流程图；Fig. 1 is the flow chart of the underwater acoustic communication technology under the Arctic ice based on joint messaging;

图2是试验发射接收阵元深度；Figure 2 is the depth of the test transmitting and receiving array elements;

图3(a1)-(b4)为用户5迭代处理结果图；图3(a1)BP-EP第一次迭代均衡器输出星座图，图3(a2)BP-EP第二次迭代均衡器输出星座图，图3(a3)BP-EP第一次迭代解码器输出星座图，图3(a4)BP-EP第二次迭代解码器输出星座图；图3(b1)LMMSE第一次迭代均衡器输出星座图，图3(b2)LMMSE第二次迭代均衡器输出星座图；图3(b3)LMMSE第一次迭代解码器输出星座图，图3(b4)LMMSE第二次迭代解码器输出星座图；Figure 3(a1)-(b4) is the iterative processing result of user 5; Figure 3 (a1) BP-EP first iteration equalizer output constellation, Figure 3 (a2) BP-EP second iteration equalizer output Constellation diagram, Figure 3 (a3) BP-EP first iteration decoder output constellation diagram, Figure 3 (a4) BP-EP second iteration decoder output constellation diagram; Figure 3 (b1) LMMSE first iteration equalization Figure 3 (b2) LMMSE second iteration equalizer output constellation; Figure 3 (b3) LMMSE first iteration decoder output constellation, Figure 3 (b4) LMMSE second iteration decoder output Constellation;

图4是BP-EP方法和LMMSE方法数据处理误码率结果表。Fig. 4 is the result table of data processing bit error rate of BP-EP method and LMMSE method.

具体实施方式Detailed ways

下面结合附图与具体实施方式对本发明作进一步详细描述。The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

本发明的具体实现过程如下：The concrete realization process of the present invention is as follows:

(1)搭建多阵元水声通信系统，接收水听器采集的声信号经过采样和解调后得到的基带信号为(1) To build a multi-array element underwater acoustic communication system, the baseband signal obtained after sampling and demodulation of the acoustic signal collected by the receiving hydrophone is:

Figure BDA0003723777620000031
Figure BDA0003723777620000031

其中，hn，m，j，l是信道hn，m在时刻j的第l个系数，第n个发射阵元和第m个接收阵元之间的信道为hn，m＝[hn，m，0，...，hn，m，l，...，hn，m，L-1]，wm，j表示在时刻j时第m个水听器上采集到的均值为0方差为σm，j的高斯白噪声，即

Figure BDA0003723777620000032
将上式表示为矩阵形式Among them, h n, m, j, l is the l th coefficient of the channel h n, m at time j, and the channel between the n th transmitting array element and the m th receiving array element is h n, m = [h n, m, 0 , ..., h n, m, l , ..., h n, m, L-1 ], w m, j represents the data collected on the mth hydrophone at time j Gaussian white noise with mean 0 and variance σ m,j , namely
Figure BDA0003723777620000032
Represent the above formula in matrix form

Figure BDA0003723777620000033
Figure BDA0003723777620000033

其中in

Figure BDA0003723777620000041
Figure BDA0003723777620000041

Figure BDA0003723777620000042
Figure BDA0003723777620000042

Figure BDA0003723777620000043
Figure BDA0003723777620000043

Figure BDA0003723777620000044
Figure BDA0003723777620000044

(2)接收阵采集的接收符号为y＝[y1，...，ym，...，yM]，其中，M为接收阵元的个数，ym为第m个接收阵元上接收的符号。发射的比特数据序列b＝[b1，...，bn，...，bN]经过卷积编码、交织和映射得到发射符号x＝[x1，...，xn，...，xN]，xn为第n个发射阵元发射的符号。且接收端已知发射信号的导频序列

Figure BDA0003723777620000045
其中，
Figure BDA0003723777620000046
为第n个发射阵元发射的导频序列。根据接收数据设置接收机三层迭代的次数，即BP最大迭代次数U，EP最大迭代次数S，迭代均衡与解码次数T.(2) The received symbols collected by the receiving array are y=[y 1 ,...,y m ,...,y M ], where M is the number of receiving array elements, and y m is the mth receiving array The symbol received on the element. The transmitted bit data sequence b = [ b 1 , . . . , bn , . .., x N ], x n is the symbol transmitted by the nth transmitting array element. And the receiver knows the pilot sequence of the transmitted signal
Figure BDA0003723777620000045
in,
Figure BDA0003723777620000046
The pilot sequence transmitted by the nth transmitting array element. Set the number of three-layer iterations of the receiver according to the received data, namely the maximum iteration number U of BP, the maximum number of iterations S of EP, and the number of iteration equalization and decoding T.

(3)信道估计。在第一次turbo迭代中，使用已知的导频序列

Figure BDA0003723777620000047
和低复杂度稀疏贝叶斯算法估计信道。在后续的turbo迭代中，根据反馈的先验符号
Figure BDA0003723777620000048
进行信道估计。(3) Channel estimation. In the first turbo iteration, a known pilot sequence is used
Figure BDA0003723777620000047
and a low-complexity sparse Bayesian algorithm to estimate the channel. In subsequent turbo iterations, according to the feedback prior sign
Figure BDA0003723777620000048
Perform channel estimation.

(4)计算变量节点传递到均衡节点的消息

Figure BDA0003723777620000049
表示为(4) Calculate the message sent by the variable node to the balance node
Figure BDA0003723777620000049
Expressed as

Figure BDA00037237776200000410
Figure BDA00037237776200000410

其中，

Figure BDA00037237776200000411
表示第k个变量节点传递到第j个均衡节点的消息，
Figure BDA00037237776200000412
和
Figure BDA00037237776200000413
表示该高斯分布消息的均值和方差，k∈[1，K]，j∈[1，J]。
Figure BDA00037237776200000414
表示第k个解映射节点传递到第k个变量节点的消息，
Figure BDA00037237776200000415
表示第j′个均衡节点传递到第k个变量节点的消息。ne{xk}\{j，k}表示与xk相邻的除去节点{j，k}之外的所有节点。通过高斯分布的乘积性质，得到上式中均值和方差的值为in,
Figure BDA00037237776200000411
represents the message delivered by the kth variable node to the jth balance node,
Figure BDA00037237776200000412
and
Figure BDA00037237776200000413
Represents the mean and variance of this Gaussian distributed message, k∈[1,K], j∈[1,J].
Figure BDA00037237776200000414
represents the message passed by the kth demapping node to the kth variable node,
Figure BDA00037237776200000415
Represents the message delivered by the j'th balancing node to the kth variable node. ne{x k }\{j, k} represents all nodes adjacent to x k except node {j, k}. Through the product property of the Gaussian distribution, the mean and variance in the above formula are obtained as

Figure BDA0003723777620000051
Figure BDA0003723777620000051

Figure BDA0003723777620000052
Figure BDA0003723777620000052

其中，

Figure BDA0003723777620000053
和
Figure BDA0003723777620000054
表示高斯消息
Figure BDA0003723777620000055
的均值和方差，
Figure BDA0003723777620000056
和
Figure BDA0003723777620000057
表示高斯消息
Figure BDA0003723777620000058
的均值和方差。in,
Figure BDA0003723777620000053
and
Figure BDA0003723777620000054
Represents a Gaussian message
Figure BDA0003723777620000055
The mean and variance of ,
Figure BDA0003723777620000056
and
Figure BDA0003723777620000057
Represents a Gaussian message
Figure BDA0003723777620000058
mean and variance.

(5)计算均衡节点传递到变量节点的消息

Figure BDA0003723777620000059
表示为(5) Calculate the message that the balance node transmits to the variable node
Figure BDA0003723777620000059
Expressed as

Figure BDA00037237776200000510
Figure BDA00037237776200000510

其中，fequ(x)表示似然函数，x\xk表示向量[x1，...xk-1，xk+1，...，xk]。通过高斯分布的乘积性质，得到上式的均值和方差为where f equ (x) represents the likelihood function, and x\x k represents the vector [x 1 , . . . x k-1 , x k+1 , . . . , x k ]. Through the product property of Gaussian distribution, the mean and variance of the above formula are obtained as

Figure BDA00037237776200000511
Figure BDA00037237776200000511

Figure BDA00037237776200000512
Figure BDA00037237776200000512

其中，Hj，k表示循环卷积信道矩阵的第(j，k)个值。Wherein, H j, k represents the (j, k)th value of the circular convolution channel matrix.

(6)判断：若当前迭代次数u＝U成立，则执行下一步。否则重复进行步骤(3)-(5)BP迭代过程。(6) Judgment: if the current number of iterations u=U is established, execute the next step. Otherwise, repeat steps (3)-(5) BP iterative process.

(7)计算变量节点传递到解映射节点的消息

Figure BDA00037237776200000513
表示为(7) Calculate the message that the variable node passes to the demapping node
Figure BDA00037237776200000513
Expressed as

Figure BDA00037237776200000514
Figure BDA00037237776200000514

其中，

Figure BDA00037237776200000515
和
Figure BDA00037237776200000516
表示该消息的均值和方差。根据高斯分布乘积的性质，得到in,
Figure BDA00037237776200000515
and
Figure BDA00037237776200000516
Represents the mean and variance of the message. According to the property of the product of Gaussian distribution, we get

Figure BDA00037237776200000517
Figure BDA00037237776200000517

Figure BDA00037237776200000518
Figure BDA00037237776200000518

(8)计算解映射节点到变量节点的消息

Figure BDA00037237776200000519
表示为(8) Calculate the message from the demap node to the variable node
Figure BDA00037237776200000519
Expressed as

Figure BDA0003723777620000061
Figure BDA0003723777620000061

其中，Proj[·]表示投影操作，C表示归一化因子，fdem(xk，dk)表示解映射节点的映射函数，

Figure BDA0003723777620000062
表示第i个比特节点传递到解映射节点的消息。上式中高斯分布消息的均值和方差通过矩匹配得到。Among them, Proj[ ] represents the projection operation, C represents the normalization factor, f dem (x k , d k ) represents the mapping function of the de-mapped node,
Figure BDA0003723777620000062
Represents the message that the i-th bit node delivers to the demapping node. The mean and variance of Gaussian distributed messages in the above formula are obtained by moment matching.

(9)判断：若当前迭代次数s＝S成立，则执行下一步。否则，重复进行步骤(3)-(8)EP迭代过程。(9) Judgment: if the current iteration number s=S is established, execute the next step. Otherwise, repeat steps (3)-(8) EP iteration process.

(10)解映射节点传递到比特节点的消息Le(dk，j)，(10) The message Le (d k, j ) passed by the demapping node to the bit node,

Figure BDA0003723777620000063
Figure BDA0003723777620000063

其中，Le(dk，j)表示对数似然比(LLR)，

Figure BDA0003723777620000064
表示估计符号的方差，
Figure BDA0003723777620000065
表示估计符号，α表示标准星座点，
Figure BDA0003723777620000066
和
Figure BDA0003723777620000067
表示映射和解映射操作，χk表示星座图集，q∈[1，Q]表示一个符号对应的第q个比特，La(dk，q)表示上一次turbo信道解码器反馈的先验信息。where Le (d k, j ) represents the log-likelihood ratio (LLR),
Figure BDA0003723777620000064
represents the variance of the estimated symbols,
Figure BDA0003723777620000065
represents the estimated symbol, α represents the standard constellation point,
Figure BDA0003723777620000066
and
Figure BDA0003723777620000067
represents the mapping and demapping operations, χ k represents the constellation atlas, q∈[1, Q] represents the qth bit corresponding to a symbol, and L a (d k, q ) represents the prior information fed back by the last turbo channel decoder .

(11)根据解映射节点传递到比特节点的消息，进行解交织和信道解码，将信道解码器输出的外信息作为下一次turbo迭代的先验信息。(11) Perform deinterleaving and channel decoding according to the message transmitted by the demap node to the bit node, and use the external information output by the channel decoder as the prior information of the next turbo iteration.

(12)判断：若当前迭代次数t＝T成立，则将信道解码器的输出

Figure BDA0003723777620000068
作为最终的解码结果。否则，重复进行步骤(2)-(11)turbo迭代过程。(12) Judgment: if the current iteration number t=T is established, then the output of the channel decoder is
Figure BDA0003723777620000068
as the final decoding result. Otherwise, repeat the turbo iteration process of steps (2)-(11).

本发明的试验过程如下：The test process of the present invention is as follows:

试验条件：Test conditions:

使用2020年8月在中国第11次北极科学考察中采集的实验数据对提出的接收机算法进行验证。实验位置如图2所示，在北纬85°线以内的高纬度海域进行，试验场地水深约2690m并且有约50cm的冰层覆盖。接收阵的位置为R1并且保持不变，8月24日和25日分别在T1和T2位置进行了通信试验，距离接收阵的位置分别为0.225km和11.112km。图2展示了通信实验过程中接收阵和发射换能器的详细深度信息。试验中发射的多用户数据均为单载波相移键控调制信号，系统采样率为48kHz，载波频率为4kHz，根升余弦滤波器滚降因子为1。发射符号周期为1ms，故带宽为2kHz，使用8个阵元的接收数据进行解码。The proposed receiver algorithm is validated using experimental data collected during China's 11th Arctic scientific expedition in August 2020. The experimental location is shown in Figure 2. It is carried out in the high-latitude sea area within the 85° north latitude line. The water depth of the test site is about 2690m and is covered by an ice layer of about 50cm. The position of the receiving array is R1 and remains unchanged. On August 24th and 25th, the communication test was carried out at the T1 and T2 positions respectively, and the distance from the receiving array was 0.225km and 11.112km, respectively. Figure 2 shows the detailed depth information of the receiving array and transmitting transducer during the communication experiment. The multi-user data transmitted in the test are all single-carrier phase shift keying modulation signals, the system sampling rate is 48kHz, the carrier frequency is 4kHz, and the root raised cosine filter roll-off factor is 1. The transmission symbol period is 1ms, so the bandwidth is 2kHz, and the received data of 8 array elements is used for decoding.

图3(a)和(b)分别是BP-EP算法和LMMSE算法对北极11.112km试验数据的处理结果。星座图结果显示，BP-EP算法和LMMSE算法展现出了相近的性能，而且，经过两次迭代之后的解码器输出结果均能够实现收敛。Figure 3(a) and (b) are the processing results of the BP-EP algorithm and the LMMSE algorithm on the Arctic 11.112km test data, respectively. The constellation diagram results show that the BP-EP algorithm and the LMMSE algorithm show similar performance, and the decoder output results can achieve convergence after two iterations.

图4的表给出了BP-EP算法和LMMSE算法对北极11.112km试验数据的处理的误码率结果。数据结果显示，在第一次迭代过程的结果中，两种算法的性能相近。在第二次迭代中的均衡器输出结果中，BP-EP算法展现出了更佳的误码率性能，并且解码器的输出均为零。The table in Fig. 4 shows the bit error rate results of the BP-EP algorithm and the LMMSE algorithm for the processing of the Arctic 11.112km test data. The data results show that the performance of the two algorithms is similar in the results of the first iteration process. In the equalizer output results in the second iteration, the BP-EP algorithm exhibits better bit error rate performance, and the output of the decoder is all zero.

综上，本发明公开了一种基于联合消息传递的冰区水声通信方法，属于水声通信技术领域。本发明通过下述技术方案予以实现：基于单载波相移键控调制体制，信源比特信息经过信道编码器和随机交织器处理后映射为符号。确定接收端通信信号的解码流程。通信接收机充分利用水声通信信道的稀疏性，采用置信传播(BP)求取估计符号的后验概率，以此来降低接收机的计算复杂度。同时，采用期望传播(EP)将传递的消息投影到指数分布族中来获取精确的近似后验概率。接收机采用三层迭代结构，第一层为循环置信传播迭代过程，第二层为期望传播迭代过程，第三次为turbo迭代过程。最后，将BP-EP联合消息传递算法推广到多阵元系统中，以此来降低多阵元水声通信系统的计算复杂度。本发明的优点在于(1)能够在获得与线性最小均方误差均衡器(LMMSE)相当的性能的同时极大地降低计算复杂度；(2)算法适用于单输入单输出(SISO)、单输入多输出(SIMO)、多输入多输出(MIMO)或者多用户(MU)系统；(3)可用来实现水下无线网络之间的数据传输。To sum up, the present invention discloses an underwater acoustic communication method in ice area based on joint message transmission, which belongs to the technical field of underwater acoustic communication. The invention is realized by the following technical scheme: based on the single-carrier phase shift keying modulation system, the source bit information is processed by a channel encoder and a random interleaver and then mapped into symbols. Determine the decoding process of the communication signal at the receiving end. The communication receiver makes full use of the sparsity of the underwater acoustic communication channel, and uses belief propagation (BP) to obtain the posterior probability of the estimated symbol, thereby reducing the computational complexity of the receiver. At the same time, expected propagation (EP) is employed to project the delivered messages into a family of exponential distributions to obtain accurate approximate posterior probabilities. The receiver adopts a three-layer iterative structure. The first layer is an iterative process of cyclic belief propagation, the second layer is an iterative process of expectation propagation, and the third is a turbo iterative process. Finally, the BP-EP joint message passing algorithm is extended to the multi-element system to reduce the computational complexity of the multi-element underwater acoustic communication system. The advantages of the present invention are that (1) the computational complexity can be greatly reduced while the performance comparable to the Linear Minimum Mean Square Error Equalizer (LMMSE) can be obtained; (2) the algorithm is suitable for single-input single-output (SISO), single-input Multiple Output (SIMO), Multiple Input Multiple Output (MIMO) or Multiple User (MU) systems; (3) can be used to implement data transmission between underwater wireless networks.

Claims (7)

1. An under-ice underwater acoustic communication method based on joint message transmission is characterized by comprising the following steps:
the method comprises the following steps: setting the number of three-layer iteration of the receiver according to the received data, wherein the number of three-layer iteration of the receiver comprises the maximum number of BP iteration U, EP maximum iteration number S, iteration balance and decoding number T;
step two: channel estimation, in the first turbo iteration, using a known pilot sequence
Figure FDA0003723777610000011
Estimating a channel by using a low-complexity sparse Bayesian algorithm; in subsequent turbo iterations, a priori symbols from feedback
Figure FDA0003723777610000012
Performing channel estimation;
step three: message transmitted by variable calculation node to balance node
Figure FDA0003723777610000013
Step four: message transmitted by calculation balance node to variable node
Figure FDA0003723777610000014
Step five: and (3) judging: if the current iteration times U = U is true, executing the next step; otherwise, repeating the BP iteration process of the third step to the fifth step;
step six: message passed by compute variable node to demapping node
Figure FDA0003723777610000015
Step seven: computing unmapped node to variable node messages
Figure FDA0003723777610000016
Step eight: and (3) judging: if the current iteration times S = S is true, executing the next step; otherwise, repeating the EP iteration process of the third step to the eighth step;
step nine: message L delivered by demapping node to bit node e (d k,j )，L e (d k,j ) Representing a log-likelihood ratio (LLR);
step ten: de-interleaving and channel decoding are carried out according to the information transmitted to the bit nodes by the de-mapping nodes, and the external information output by the channel decoder is used as the prior information of the next turbo iteration;
step eleven: and (3) judging: if the current iteration time T = T is true, outputting the channel decoder
Figure FDA0003723777610000017
As a final decoding result, communication is effected; otherwise, the turbo iteration process of steps two-eleven is repeated.
2. The method for underwater acoustic communication based on combined messaging according to claim 1, wherein the receiving data in the first step comprises: the receiving array collects the receiving symbols y = [ y 1 ,...,y m ,...,y M ]Wherein M is the number of receiving array elements, y m For m receiving array element up connectionA received symbol; transmission bit data sequence b = [ b ] 1 ,...,b n ,...,b N ]Obtaining a transmission symbol x = [ x ] through convolutional coding, interleaving and mapping 1 ,...,x n ,...,x N ]，x n A symbol transmitted for the nth transmit array element; and the receiving end knows the pilot sequence of the transmitted signal
Figure FDA0003723777610000018
wherein ,
Figure FDA0003723777610000019
and the pilot sequence transmitted for the nth transmitting array element.
3. The method of claim 1, wherein the step three comprises an underwater acoustic communication method based on joint message delivery
Figure FDA0003723777610000021
Comprises the following steps:
Figure FDA0003723777610000022
wherein ,
Figure FDA0003723777610000023
a message indicating that the kth variable node passes to the jth equalization node,
Figure FDA0003723777610000024
and
Figure FDA0003723777610000025
represents the mean and variance of the Gaussian distribution message, k ∈ [1,K]，j∈[1,J]；
Figure FDA0003723777610000026
A message indicating that the kth demapping node passes to the kth variable node,
Figure FDA0003723777610000027
a message indicating that the jth equalizing node passes to the kth variable node; ne { x k Denotes with x } \ j, k k All neighboring nodes except for node { j, k }; by the product property of the gaussian distribution, the values of the mean and variance in the above formula are obtained as:
Figure FDA0003723777610000028
Figure FDA0003723777610000029
wherein ,
Figure FDA00037237776100000210
and
Figure FDA00037237776100000211
representing Gaussian messages
Figure FDA00037237776100000212
The mean and the variance of (a) is,
Figure FDA00037237776100000213
and
Figure FDA00037237776100000214
representing Gaussian messages
Figure FDA00037237776100000215
Mean and variance of (c).
4. The method for underwater acoustic communication based on combined message passing as claimed in claim 1, wherein the step four is
Figure FDA00037237776100000216
Comprises the following steps:
Figure FDA00037237776100000217
wherein ,fequ (x) Representing a likelihood function, x \ x k Represents a vector [ x 1 ,...x k-1 ,x k+1 ,…,x K ](ii) a By the product property of the gaussian distribution, the mean and variance of the above formula are obtained:
Figure FDA00037237776100000218
Figure FDA00037237776100000219
wherein ,Hj,k The (j, k) th value of the cyclic convolution channel matrix is represented.
5. The method for underwater acoustic communication based on united message passing as claimed in claim 1, wherein the sixth step is
Figure FDA00037237776100000220
Comprises the following steps:
Figure FDA0003723777610000031
wherein ,
Figure FDA0003723777610000032
and
Figure FDA0003723777610000033
means and variance representing the message; according to the Gaussian distribution productOf obtaining
Figure FDA0003723777610000034
6. The method for underwater acoustic communication based on combined message passing as claimed in claim 1, wherein step seven is performed
Figure FDA0003723777610000035
Comprises the following steps:
Figure FDA0003723777610000036
wherein, proj [ ·]Representing projection operations, C representing a normalization factor, f dem (x k ,d k ) A mapping function representing a demapping node,
Figure FDA0003723777610000037
a message indicating that the ith bit node passes to the demapping node; the mean and variance of the gaussian distribution message in the above equation are obtained by moment matching.
7. The method for underwater acoustic communication based on combined message passing as claimed in claim 1, wherein L of the ninth step e (d k,j ) Comprises the following steps:
Figure FDA0003723777610000038
wherein ,Le (d k,j ) Represents a log-likelihood ratio (LLR),
Figure FDA0003723777610000039
which represents the variance of the estimated symbols,
Figure FDA00037237776100000310
representing the estimated symbols, alpha representing the standard constellation point,
Figure FDA00037237776100000311
and
Figure FDA00037237776100000312
representing mapping and demapping operations, χ k Represents a constellation set, q ∈ [1,Q ]]Denotes the q-th bit, L, corresponding to a symbol a (d k,q ) Representing a priori information fed back by a last turbo channel decoder.
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