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 PDFInfo
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Abstract
The invention provides an under-ice underwater acoustic communication method based on joint message transmission, which is based on a single-carrier phase shift keying modulation system, and information source bit information is mapped into symbols after being processed by a channel encoder and a random interleaver. And determining the decoding flow of the communication signal of the receiving end. The communication receiver fully utilizes the sparsity of the underwater acoustic communication channel, and adopts belief propagation to solve the posterior probability of the estimated symbol, so as to reduce the computational complexity of the receiver. At the same time, the expected propagation is used to project the delivered message into the family of exponential distributions to obtain an accurate approximate posterior probability. The receiver adopts a three-layer iteration structure, wherein the first layer is a cyclic belief propagation iteration process, the second layer is an expected propagation iteration process, and the third layer is a turbo iteration process. The invention can greatly reduce the computational complexity while obtaining performance equivalent to a linear minimum mean square error equalizer (LMMSE).
Description
Technical Field
The present invention relates to the field of underwater acoustic communication, and more particularly, to a method capable of effectively reducing the complexity of an underwater acoustic communication receiver and easily extending to a multi-element system.
Background
With the global warming, the ice covers of the two poles gradually fade, and the arctic region gradually pays attention to people because of abundant underwater resources and natural channel advantages. The underwater acoustic communication technology is widely applied to the fields of underwater resource exploration, marine environment monitoring, data acquisition, detection and early warning and the like. Therefore, research and development of underwater acoustic communication technology have great significance in the process of development in the arctic.
Through analysis of experimental data of the arctic, the arctic under-ice underwater acoustic channel is found to have a rich multi-path time delay structure, and during remote data transmission, the multi-path time delay can reach more than 100 ms. However, due to less human activity in arctic regions and the presence of ice coverage, the signal-to-noise ratio of the collected data is higher and more importantly, the time variation of its channel is slower. For the characteristics of the arctic ice-water acoustic channel, the traditional high-complexity adaptive channel tracking equalization method is not suitable for the scene any more, and the block-by-block equalization method based on channel estimation has relatively low computational complexity, but still has high computational complexity in the case of facing most of the time delay. 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 the conventional algorithm in underwater communication equipment.
Therefore, the research of the low-complexity multi-array element system receiver algorithm is of great significance aiming at the arctic ice-low slow time-varying channel characteristics.
Disclosure of Invention
The invention aims to provide an ice region underwater acoustic communication method based on combined message transmission.
The purpose of the invention is realized as follows: the method comprises the following steps:
(1) The receiving symbol collected by the receiving array is y = [ y = 1 ，...，y m ，...，y M ]Wherein M isNumber of receiving array elements, y m The symbol received on the m-th receive array element. Transmitted 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 The symbol transmitted for the nth transmitting array element. And the receiving end knows the pilot sequence of the transmitted signal
wherein ,
and the pilot sequence transmitted for the nth transmitting array element. And setting the times of three-layer iteration of the receiver, namely the maximum BP iteration time U, the maximum EP iteration time S and the iteration balancing and decoding time T according to the received data.
(2) And (4) channel estimation. In the first turbo iteration, a known pilot sequence is used
And estimating a channel by a low-complexity sparse Bayesian algorithm. In subsequent turbo iterations, a priori symbols from feedback
And performing channel estimation.
(3) Message transmitted by calculation variable node to balance node
Is shown as
wherein ,
a message indicating that the kth variable node passes to the jth equalization node,
and
represents the mean and variance of the Gaussian distribution message, k ∈ [1,K ]]，j∈[1，J]。
(4) Message transmitted by calculation balance node to variable node
Is shown as
(5) And (3) judging: and if the current iteration number U = U is true, executing the next step. Otherwise, repeating the BP iterative process of the steps (3) - (5).
(6) Message passed by compute variable node to demapping node
Is shown as
wherein ,
and
representing the mean and variance of the message.
(7) Computing unmapped node to variable node messages
Is shown as
Wherein the mean and variance of the gaussian distribution messages in the above equation are obtained by moment matching.
(8) And (3) judging: and if the current iteration number S = S is true, executing the next step. Otherwise, repeating the EP iteration process from the step (3) to the step (8).
(9) Message L delivered by demapping node to bit node e (d k，j )，L e (d k，j ) Representing a log-likelihood ratio (LLR).
(10) And according to the message transmitted to the bit node by the demapping node, performing deinterleaving and channel decoding, and taking the external information output by the channel decoder as prior information of the next turbo iteration.
(11) And (3) judging: if the current iteration time T = T is true, outputting the channel decoder
As a final decoding result. Otherwise, repeating the turbo iteration process from the step (2) to the step (11).
Compared with the prior art, the invention has the beneficial effects that: the difference between the method and the traditional LMMSE (mean square error) balanced underwater acoustic communication method is that the sparsity of an underwater acoustic channel is fully utilized, the posterior probability of an estimated symbol is solved by using a belief propagation algorithm, the calculation complexity of the algorithm is only related to the number of nonzero coefficients of the sparse underwater acoustic channel, and the calculation complexity is greatly reduced. At the same time, a desired propagation algorithm is used to obtain a more accurate approximate posterior probability. The algorithm can be simply and efficiently extended from a SISO system to a SIMO system, a MIMO system or an MU system, thereby effectively reducing the computational complexity of the multi-array element system.
Drawings
FIG. 1 is a flow diagram of an arctic ice-water acoustic communication technique based on federated messaging;
FIG. 2 is a depth of a trial transmit receive array element;
FIGS. 3 (a 1) - (b 4) are graphs of results of the iterative process performed by the user 5; fig. 3 (a 1) shows the output constellation of the BP-EP first iteration equalizer, fig. 3 (a 2) shows the output constellation of the BP-EP second iteration equalizer, fig. 3 (a 3) shows the output constellation of the BP-EP first iteration decoder, and fig. 3 (a 4) shows the output constellation of the BP-EP second iteration decoder; FIG. 3 (b 1) the LMMSE first iteration equalizer outputs a constellation diagram, and FIG. 3 (b 2) the LMMSE second iteration equalizer outputs a constellation diagram; fig. 3 (b 3) LMMSE first iteration decoder outputs a constellation, fig. 3 (b 4) LMMSE second iteration decoder outputs a constellation;
FIG. 4 is a table of the bit error rate results of the data processing of the BP-EP method and the LMMSE method.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
The specific implementation process of the invention is as follows:
(1) A multi-array-element underwater acoustic communication system is set up, and baseband signals obtained by sampling and demodulating acoustic signals collected by a hydrophone are received
wherein ,hn，m，j，l Is channel h n，m The channel between the nth transmitting array element and the mth receiving array element is h at the ith coefficient of time j n，m ＝[h n，m，0 ，...，h n，m，l ，...，h n，m，L-1 ]，w m，j Means that the mean value collected at the mth hydrophone at time j is 0 and the variance is σ m，j White Gaussian noise, i.e.
Expressing the above formula in a matrix form
wherein
(2) The receiving symbol collected by the receiving array is y = [ y 1 ，...，y m ，...，y M ]Wherein M is the number of receiving array elements, y m Is the symbol received on the mth receive array element. Transmitted 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 The symbol transmitted for the nth transmit array element. And the receiving end knows the pilot sequence of the transmitted signal
wherein ,
and the pilot sequence transmitted for the nth transmitting array element. And setting the times of three-layer iteration of the receiver, namely the maximum BP iteration time U, the maximum EP iteration time S and the iteration balancing and decoding time T according to the received data.
(3) And (4) channel estimation. In the first turbo iteration, a known pilot sequence is used
And estimating a channel by a low-complexity sparse Bayesian algorithm. In subsequent turbo iterations, a priori symbols from feedback
And performing channel estimation.
(4) Compute variable nodeMessages delivered to balancing nodes
Is shown as
wherein ,
a message indicating that the kth variable node passes to the jth equalization node,
and
represents the mean and variance of the Gaussian distribution message, k ∈ [1,K]，j∈[1，J]。
A message indicating that the kth demapping node passes to the kth variable node,
indicating the message passed by the jth' equalization node to the kth variable node. ne { x k Denotes with x k All nodes except for the nodes j, k that are adjacent. The product property of Gaussian distribution is used to obtain the value of the mean value and the variance in the formula as
wherein ,
and
representing Gaussian messages
The mean and the variance of (a) is,
and
representing Gaussian messages
Mean and variance of.
(5) Message transmitted by calculation balance node to variable node
Is shown as
wherein ,fequ (x) Representing a likelihood function, x \ x k Represents a vector [ x 1 ，...x k-1 ，x k+1 ，...，x k ]. By the product property of Gaussian distribution, the mean and variance of the formula are obtained
wherein ,Hj，k The (j, k) th value of the cyclic convolution channel matrix is represented.
(6) And (3) judging: and if the current iteration number U = U is true, executing the next step. Otherwise, repeating the BP iteration process of the steps (3) to (5).
(7) Message passed by compute variable node to demapping node
Is shown as
wherein ,
and
representing the mean and variance of the message. According to the nature of the Gaussian distribution product, obtaining
(8) Computing unmapped node to variable node messages
Is shown as
Wherein, proj [ ·]Representing the projection operation, C representing a normalization factor, f dem (x k ，d k ) A mapping function representing a demapping node,
representing the message that the ith bit node passes to the demapping node. On the upper partThe mean and variance of the gaussian distribution message in the formula are obtained by moment matching.
(9) And (3) judging: and if the current iteration number S = S is true, executing the next step. Otherwise, repeating the EP iteration process from the step (3) to the step (8).
(10) Message L delivered by demapping node to bit node e (d k，j )，
wherein ,Le (d k，j ) Represents a log-likelihood ratio (LLR),
which represents the variance of the estimated symbols,
representing the estimated symbols, alpha representing the standard constellation point,
and
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.
(11) And according to the message transmitted to the bit node by the demapping node, performing deinterleaving and channel decoding, and taking the external information output by the channel decoder as prior information of the next turbo iteration.
(12) And (3) judging: if the current iteration time T = T is true, outputting the channel decoder
As a final decoding result. Otherwise, repeating the turbo iteration process of steps (2) - (11).
The test process of the invention is as follows:
the test conditions are as follows:
the proposed receiver algorithm was validated using experimental data collected in 11 th north pole scientific investigation in china, 8 months 2020. The experimental site was carried out at a high altitude sea area within the 85 ° north latitude line, as shown in fig. 2, and the experimental site had a water depth of about 2690m and an ice layer covering about 50 cm. The position of the receiving array is R1 and is kept unchanged, communication tests are carried out at the positions T1 and T2 respectively on 24 th and 25 th days in 8 months, and the positions from the receiving array are 0.225km and 11.112km respectively. Fig. 2 shows detailed depth information of the receiving array and the transmitting transducer during the communication experiment. The multi-user data transmitted in the test are all single-carrier phase shift keying modulation signals, the sampling rate of the system is 48kHz, the carrier frequency is 4kHz, and the roll-off factor of the root-raised cosine filter is 1. The transmission symbol period is 1ms, so the bandwidth is 2kHz, and 8 array elements of received data are used for decoding.
FIGS. 3 (a) and (b) are the results of processing the test data for the North Pole 11.112km by the BP-EP algorithm and the LMMSE algorithm, respectively. The constellation diagram result shows that the BP-EP algorithm and the LMMSE algorithm show similar performance, and the output result of the decoder after two iterations can realize convergence.
The table of FIG. 4 shows the bit error rate results of the BP-EP algorithm and the LMMSE algorithm for the processing of the test data of the north pole 11.112km. 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 result in the second iteration, the BP-EP algorithm exhibits better bit error rate performance, and the output of the decoder is zero.
In summary, the invention discloses an ice region underwater acoustic communication method based on joint message transmission, and belongs to the technical field of underwater acoustic communication. The invention is realized by the following technical scheme: based on a single-carrier phase shift keying modulation system, information source bit information is mapped into symbols after being processed by a channel encoder and a random interleaver. And determining the decoding flow of the communication signal of the receiving end. The communication receiver fully utilizes the sparsity of the underwater acoustic communication channel, and adopts Belief Propagation (BP) to obtain the posterior probability of an estimated symbol, so as to reduce the computational complexity of the receiver. At the same time, the exact approximate posterior probability is obtained by projecting the delivered message into a family of exponential distributions using Expectation Propagation (EP). The receiver adopts a three-layer iteration structure, wherein the first layer is a cyclic belief propagation iteration process, the second layer is an expected propagation iteration process, and the third layer is a turbo iteration process. And finally, the BP-EP combined message transfer algorithm is popularized to a multi-array element system, so that the computational complexity of the multi-array element underwater acoustic communication system is reduced. The invention has the advantages that (1) the computational complexity can be greatly reduced while the performance equivalent to a linear minimum mean square error equalizer (LMMSE) is obtained; (2) The algorithm is applicable to single-input single-output (SISO), single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multi-user (MU) systems; (3) The method can be used for realizing 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
Estimating a channel by using a low-complexity sparse Bayesian algorithm; in subsequent turbo iterations, a priori symbols from feedback
Performing channel estimation;
step three: message transmitted by variable calculation node to balance node
Step four: message transmitted by calculation balance node to variable node
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
Step seven: computing unmapped node to variable node messages
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
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
wherein ,
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
Comprises the following steps:
wherein ,
a message indicating that the kth variable node passes to the jth equalization node,
and
represents the mean and variance of the Gaussian distribution message, k ∈ [1,K]，j∈[1,J]；
A message indicating that the kth demapping node passes to the kth variable node,
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:
wherein ,
and
representing Gaussian messages
The mean and the variance of (a) is,
and
representing Gaussian messages
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
Comprises the following steps:
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:
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
Comprises the following steps:
wherein ,
and
means and variance representing the message; according to the Gaussian distribution productOf obtaining
6. The method for underwater acoustic communication based on combined message passing as claimed in claim 1, wherein step seven is performed
Comprises the following steps:
wherein, proj [ ·]Representing projection operations, C representing a normalization factor, f dem (x k ,d k ) A mapping function representing a demapping node,
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:
wherein ,Le (d k,j ) Represents a log-likelihood ratio (LLR),
which represents the variance of the estimated symbols,
representing the estimated symbols, alpha representing the standard constellation point,
and
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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| CN109246039A (en) * | 2018-08-09 | 2019-01-18 | 华南理工大学 | A kind of Soft Inform ation iteration receiving method based on two-way time domain equalization |
| CN109743118A (en) * | 2018-12-24 | 2019-05-10 | 哈尔滨工程大学 | A kind of double OFDM underwater acoustic communication methods for expanding the spectral efficient under channel condition of time-varying |
| CN112953653A (en) * | 2021-03-01 | 2021-06-11 | 哈尔滨工程大学 | Single-carrier multi-user underwater acoustic communication method |
| CN114401172A (en) * | 2021-10-26 | 2022-04-26 | 郑州大学 | Joint estimation and detection method based on Turbo equalization framework and VAMP |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116016061A (en) * | 2022-12-16 | 2023-04-25 | 重庆邮电大学 | Short wave double-selection channel double-iteration Turbo equalization method for high maneuvering platform |
| CN116016061B (en) * | 2022-12-16 | 2024-05-07 | 重庆邮电大学 | Short wave double-selection channel double-iteration Turbo equalization method for high maneuvering platform |
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