MIMO-FMT-TR underwater acoustic communication system and method

Abstract

This invention discloses a MIMO-FMT-TR underwater acoustic communication system and method, relating to the field of communication noise suppression technology. The system includes: a transmitter, used to process each user information sequence using error control coding technology, and transmit the processed user information sequence to a receiver via an underwater acoustic channel; and a receiver, used to process the demodulated user information sequence using TR technology to obtain a TR output signal, and to post-process the TR output signal according to adaptive turbo equalization technology; the adaptive turbo equalization technology includes interference cancellation technology, soft-information-based adaptive decision feedback equalization technology, and decoding technology. This invention can achieve the goal of improving interference suppression performance.

Description

Technical Field

[0001] This invention relates to the field of communication noise suppression technology, and in particular to a MIMO-FMT-TR underwater acoustic communication system and method. Background Technology

[0002] In multiple-input multiple-output (MIMO) filtered multitoned (FMT) underwater acoustic communication systems, considering factors such as bandwidth utilization and filter implementation complexity, the FMT sub-band bandwidth is still greater than the correlation bandwidth of the underwater acoustic channel. This results in the fact that although the symbol range affected by multipath spread and channel spatial correlation on each sub-channel is significantly reduced, inter-symbol interference (ISI) caused by multipath spread and co-channel interference (CCI) caused by spatial correlation still exist in MIMO-FMT underwater acoustic communication systems.

[0003] Time reversal (TR) technology is simple to implement and has good space-time compression characteristics, so it is used in the receiver of MIMO-FMT underwater acoustic communication systems for preprocessing CCI and ISI. However, since residual CCI and ISI still exist in the TR output signal, and TR technology cannot suppress noise, appropriate techniques are needed to post-process the noise, residual CCI, and residual ISI in the TR output signal.

[0004] Existing technologies have proposed interference suppression methods that combine continuous interference cancellation and adaptive equalization. Although this method achieves good ISI and CCI suppression performance, it does not consider the problem of noise suppression. Summary of the Invention

[0005] The purpose of this invention is to provide a MIMO-FMT-TR underwater acoustic communication system and method to improve interference suppression performance.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] In a first aspect, the present invention provides a MIMO-FMT-TR underwater acoustic communication system, comprising: a transmitter, configured to process each user information sequence using error control coding technology, and transmit the processed user information sequence to a receiver via an underwater acoustic channel; and a receiver, configured to process the demodulated user information sequence using TR technology to obtain a TR output signal, and to perform post-processing on the TR output signal according to an adaptive turbo equalization technique; wherein the adaptive turbo equalization technique includes interference cancellation technology, soft information-based adaptive decision feedback equalization technology, and decoding technology.

[0008] Secondly, the present invention provides a MIMO-FMT-TR underwater acoustic communication method, comprising: a transmitter processing each user information sequence using error control coding technology, and transmitting the processed user information sequence to a receiver via an underwater acoustic channel; the receiver processing the demodulated user information sequence using TR technology to obtain a TR output signal, and post-processing the TR output signal according to an adaptive turbo equalization technique; the adaptive turbo equalization technique includes interference cancellation technology, soft information-based adaptive decision feedback equalization technology, and decoding technology.

[0009] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0010] This invention jointly suppresses residual CCI, ISI, and noise in the TR output signal after processing in a MIMO-FMT-TR underwater acoustic communication system. Compared with interference suppression techniques that do not employ error control coding (ECC) and interference suppression techniques based on hard decision equalization and decoding, this invention introduces ECC technology and continuously performs soft information interaction during adaptive turbo equalization, resulting in a lower bit error rate and better performance. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a block diagram of the MIMO-FMT-TR underwater acoustic communication system in this embodiment;

[0013] Figure 2 This is a block diagram illustrating the transmission principle of the MIMO-FMT-TR underwater acoustic communication system in this embodiment.

[0014] Figure 3 This is a block diagram illustrating the receiving principle of the MIMO-FMT-TR underwater acoustic communication system in this embodiment;

[0015] Figure 4 This is a schematic diagram of the TR processing principle for the m-th sub-signal sequence group in this embodiment;

[0016] Figure 5 is a schematic diagram of the adaptive Turbo equalization process in this embodiment; Figure 5(a) is a block diagram of the interference canceller; Figure 5(b) is a block diagram of the soft information-based adaptive decision feedback equalizer and decoder.

[0017] Figure 6 shows the combined channel response diagram after TR processing in this embodiment; Figures 6(a)-(d) respectively show the combined channel response diagrams after TR processing of the four sub-bands of user 1; Figures 6(e)-(h) respectively show the combined channel response diagrams after TR processing of the four sub-bands of user 2.

[0018] Figure 7 is a comparison of the BER of the decoding output of the proposed method under 10dB noise and the BER of the decoding output without using the ECC method in this embodiment; Figure 7(a)-(d) respectively show the comparison of the BER of the decoding output of the proposed method under 10dB noise and the BER of the decoding output without using the ECC method in the first sub-band, the second sub-band, the third sub-band, and the fourth sub-band.

[0019] Figure 8 is a comparison of the SER output of the proposed method without noise and the SER output based on the hard decision method in this embodiment; Figure 8(a)-(d) respectively show the comparison of the SER output of the proposed method without noise and the SER output based on the hard decision method in the first sub-band, the second sub-band, the third sub-band, and the fourth sub-band.

[0020] Figure 9 is a comparison of the SER output of the proposed method under 10dB noise and the SER output based on the hard decision method. Figures 9(a)-(d) show the comparison of the SER output of the proposed method under 10dB noise and the SER output based on the hard decision method in the first sub-band, the second sub-band, the third sub-band, and the fourth sub-band, respectively.

[0021] Figure 10 is a comparison of the BER of the proposed method under 10dB noise and the BER of the hard decision method. Figures 10(a)-(d) show the comparison of the BER of the proposed method under 10dB noise and the BER of the hard decision method in the first sub-band, the second sub-band, the third sub-band, and the fourth sub-band, respectively. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] Example 1

[0025] Theoretically, combining error control coding (ECC) with the above methods can achieve joint suppression of noise, residual ISI, and residual CCI. However, traditional equalization and decoding are often based on hard decision, which can cause information loss of symbols or information bits. Furthermore, when the system's bit error rate is high, interference suppression and decision feedback equalization based on hard decision are prone to error propagation. Therefore, directly combining ECC with the above methods has limited interference suppression performance.

[0026] Based on this, this embodiment adds a new interference suppression technology to a multiple-input multiple-output (MIMO) filtered multitoned (FMT) time reversal (TR) underwater acoustic communication system, forming the MIMO-FMT-TR underwater acoustic communication system provided in this embodiment. In this embodiment, error control coding (ECC) technology is introduced at the transmitting end, and adaptive turbo equalization technology is used at the receiving end to post-process the TR output signal. The adaptive turbo equalization technology mainly includes three parts: interference cancellation technology, soft-information-based adaptive decision feedback equalization (ADFE) technology, and decoding technology.

[0027] In this embodiment, the soft information output by the soft information adaptive decision feedback equalizer is demapped and deinterleaved before being transmitted to the input of the decoder. The soft information output by the decoder is mapped and interleaved before being transmitted to the soft information adaptive decision feedback equalizer and the interference canceller. The soft information exchange and interference suppression processes are performed sequentially and iteratively until the communication performance reaches stability.

[0028] like Figure 1As shown, a MIMO-FMT-TR underwater acoustic communication system includes:

[0029] The transmitter is used to process each user information sequence using error control coding technology and then transmit the processed user information sequence to the receiver via an underwater acoustic channel.

[0030] The receiving end is used to process the demodulated user information sequence using TR technology to obtain the TR output signal, and to perform post-processing on the TR output signal according to the adaptive Turbo equalization technology; the adaptive turbo equalization technology includes interference cancellation technology, soft information-based adaptive decision feedback equalization technology and decoding technology.

[0031] In this embodiment, the transmitting end includes multiple identical transmitting components; the number of transmitting components is the same as the number of received user information sequences, and each transmitting component corresponds one-to-one with a received user information sequence, enabling processing of all received user information sequences; wherein, any transmitting component includes: a serial-to-parallel converter, multiple symbol sequence generators, an FMT modulator, and a transmitting array element.

[0032] Furthermore, each of the transmitting components has the following functions.

[0033] The serial-to-parallel converter is used to convert a received user information sequence into a serial-to-parallel sequence to obtain multiple first sub-information sequence groups.

[0034] The symbol sequence generator is used to encode and interleave each sub-information sequence in a first sub-information sequence group output by the serial-to-parallel converter, and generate a symbol sequence in groups of Q bits. The number of symbol sequence generators is the same as the number of the first sub-information sequence groups, and the symbol sequence generators correspond one-to-one with the first sub-information sequence groups, enabling the processing of all first sub-information sequence groups output by the serial-to-parallel converter.

[0035] The FMT modulator is used to perform FMT modulation on the symbol sequences output by all symbol sequence generators to obtain a modulation sequence.

[0036] The transmitting array element is used to transmit the modulation sequence output by the FMT modulator to the corresponding receiving array element in the receiving end through the underwater acoustic channel; wherein, the modulation sequence is the processed user information sequence.

[0037] The receiving array element is used to receive the modulation sequence transmitted through the underwater acoustic channel; the number of transmitting array elements is the same as the number of receiving array elements.

[0038] In this embodiment, the receiving end includes, in addition to multiple receiving array elements, a:

[0039] An FMT demodulator is used to demodulate the signal received by one receiving array element to obtain M sub-signals. The number of receiving array elements is the same as the number of FMT demodulators, and the receiving array elements and the FMT demodulators correspond one-to-one, enabling FMT demodulation of signals received by all receiving array elements.

[0040] A divider is used to divide the sub-signals output by all FMT demodulators into M second sub-information sequence groups; each sub-signal output by the FMT demodulator is labeled, and the second sub-information sequence group includes the sub-signals output by each FMT demodulator with the same label.

[0041] A TR processor is used to process a second sub-information sequence group using TR technology to obtain a TR output signal; the number of TR processors is the same as the number of second sub-information sequence groups, and the TR processors correspond one-to-one with the second sub-information sequence groups, enabling processing of all second sub-information sequence groups.

[0042] An adaptive turbo equalizer is used to post-process a TR output signal using adaptive turbo equalization technology until the communication performance is stable, thereby obtaining a post-processed signal; the number of adaptive turbo equalizers is the same as the number of TR output signals, and the adaptive turbo equalizers correspond one-to-one with the TR output signals, enabling post-processing of all TR output signals.

[0043] A combiner is used to combine the post-processed signals output by all adaptive turbo equalizers to obtain multiple combined signals; the sub-signals in the post-processed signals output by each adaptive turbo equalizer are labeled, and the combined signals include the sub-signals with the same label in the post-processed signals output by each adaptive turbo equalizer; the number of combined signals is the same as the number of user information sequences.

[0044] A parallel-to-serial converter is used to perform parallel-to-serial conversion on a combined signal; the number of parallel-to-serial converters is the same as the number of combined signals, and the parallel-to-serial converters correspond one-to-one with the combined signals, enabling parallel-to-serial conversion on all combined signals.

[0045] Furthermore, the adaptive turbo equalizer includes:

[0046] The interference canceller is used to estimate the residual CCI in the TR output signal and remove the estimated residual CCI. The soft-information adaptive decision feedback equalizer includes a signal filtering section and an adaptive coefficient adjustment section. The signal filtering section determines the filter coefficients based on the adaptive coefficients output by the adaptive coefficient adjustment section, and filters and removes the residual ISI in the signal output by the interference canceller based on the filter coefficients. The adaptive coefficient adjustment section adjusts the adaptive coefficients using a recursive expectation least squares algorithm and the signal output by the decoder. The decoder performs noise reduction processing on the signal output by the soft-information adaptive decision feedback equalizer using the log-likelihood ratio of the encoded bits.

[0047] Example 2

[0048] This embodiment provides a MIMO-FMT-TR underwater acoustic communication method based on adaptive turbo equalization, comprising: introducing error control coding technology at the transmitting end of the MIMO-FMT-TR underwater acoustic communication system, and using adaptive turbo equalization technology at the receiving end to post-process the TR output signal. The adaptive turbo equalization technology mainly includes three parts: interference cancellation technology, soft-information-based adaptive decision feedback equalization technology, and decoding technology. The detailed process of this method is shown below.

[0049] The transmitter uses error control coding technology to process each user information sequence and then transmits the processed user information sequence to the receiver through an underwater acoustic channel.

[0050] The receiving end uses TR technology to process the processed user information sequence to obtain the TR output signal, and then performs post-processing on the TR output signal according to the adaptive Turbo equalization technology; the adaptive turbo equalization technology includes interference cancellation technology, soft information-based adaptive decision feedback equalization technology and decoding technology.

[0051] For ease of analysis, we will take a MIMO-FMT-TR underwater acoustic communication system with 2 transmitting array elements transmitting 2 user signals and 2 receiving array elements receiving signals using diversity as an example to illustrate the method provided in this embodiment. In practical applications, this method is applicable to situations with multiple transmitting array elements and multiple receiving array elements. Figure 2 and Figure 3 The transmission principle block diagram and the reception principle block diagram of the MIMO-FMT-TR underwater acoustic communication system are given respectively.

[0052] The processing procedure at the transmitting end is as follows: Figure 2 It can be seen that at the transmitting end of the MIMO-FMT-TR underwater acoustic communication system, the two user information sequences are first converted from serial to parallel to obtain two sub-information sequence groups {a}. α,m (nT b)}, where α = 1, 2 represents the number of user information sequences, m = 0, ..., M-1 represents the number of sub-information sequences obtained after converting each user information sequence into a string and parallel sequence, n represents the number of user information bits, and T b The information bit interval is indicated; subsequently, the M sub-information sequences of each group are encoded and interleaved, and a symbol sequence {s} is generated in groups of Q bits. α,m (nT)},α=1,2,m=0,…M-1,T=QT b This indicates the symbol width of the generated symbol sequence; finally, the two mapped symbol sequences are modulated by FMT and then transmitted to the underwater acoustic channel through two transmit array elements.

[0053] Combination Figure 2 It can be seen that the α-th transmitting element is in kT c The signal transmitted at a given time can be represented as:

[0054]

[0055] Among them, T c =T / K represents the symbol interval after K times interpolation in the FMT modulator, where K represents the interpolation factor, k represents the discrete-time signal, i represents the subcarrier number in the FMT modulator, and g t (kT c ) is the discrete-time response of the transmit filter.

[0056] To balance implementation complexity and spectral suppression, a square root raised cosine filter with a roll-off factor of K / M-1 is chosen as the transmit filter in the FMT modulator.

[0057] The receiver includes: FMT demodulator processing, TR processing, and adaptive Turbo equalization.

[0058] 1) FMT demodulator processing procedure.

[0059] Combination Figure 3 It can be seen that, after transmission through the underwater acoustic channel, the signal received by the β-th receiving element can be expressed as:

[0060]

[0061] in, w represents the discrete-time response of the underwater acoustic channel between the α-th transmitting element and the β-th receiving element. β (kT c ) represents the channel noise received by the β-th receiving element;

[0062] Depend on Figure 3It can be seen that the receiving end first performs FMT demodulation on each received signal, and the β-th received signal y β (kT c After demodulation, M sub-signals are obtained, where the m-th sub-signal can be represented as:

[0063]

[0064] Among them, g r (nT c ) represents the discrete-time response of the receiving filter.

[0065] Formula (3) can be further expressed as:

[0066]

[0067] In this diagram, the first term on the right represents the signal transmitted in the m-th sub-band, the second term represents the inter-carrier interference (ICI) caused by signals transmitted in other sub-bands to the m-th sub-band, and the third term represents noise. FMT modulators have the advantage of being insensitive to frequency offset; when both the transmitter and receiver are fixedly deployed, the impact of ICI on communication performance is very slight and can usually be ignored. The proposed method focuses on the joint suppression of noise, CCI, and ISI; therefore, the following analysis ignores the impact of ICI.

[0068] Ignoring inter-carrier interference, equation (4) can be simplified to:

[0069]

[0070] From formula (5), it can be seen that the discrete-time response of the m-th sub-channel between the α-th transmitting element and the β-th receiving element is:

[0071]

[0072] As can be seen from the above formula, the response of each sub-channel is different, so the signal output by the FMT demodulator needs to be processed by TR separately.

[0073] 2) TR processing procedure.

[0074] TR technology is used to preprocess the sub-signals output by the FMT demodulator. Figure 4 The TR processing procedure for the m-th sub-signal sequence group is given. Figure 4 middle This represents the frequency domain response of the filter used for TR processing. Combined with... Figure 3 and 4 It can be seen that the sub-signal output by the FMT demodulator is divided into M sub-signal sequence groups. Each group of sub-signal sequences undergoes TR processing separately.

[0075] The α-th signal output from the m-th TR process can be represented as:

[0076]

[0077] In formula (7), q (α,γ),(m,m) (lT,nT) and η α,m (nT) represent the combined channel response and noise after TR processing, respectively, and their expressions are as follows:

[0078]

[0079]

[0080] Formula (7) can be further expressed as:

[0081]

[0082] As can be seen from formula (9), in addition to the desired signal component, the signal after TR processing also contains three interference components: CCI, ISI, and noise. In order to obtain good communication performance, these interference components need to be processed.

[0083] 3) Adaptive Turbo Equilibrium Process.

[0084] The proposed method employs adaptive Turbo equalization technology at the receiving end to post-process the TR output signal, and the signal processing process is shown in Figure 5.

[0085] As can be seen from Figure 5, the interference canceller and the soft-information-based adaptive decision feedback equalizer can utilize the symbol probability p in (s γ,m (nT)), γ≠α and p in (s α,m (nT)) To eliminate the effects of residual CCI and ISI, the decoder utilizes the log-likelihood ratio (LLR)Λ of the encoded bits. in (c α,m (nT b To suppress noise, the demapper and deinterleaver can reduce the symbol probability p. out (s α,m (nT)) converted into encoded bits Mappers and interleavers can encode bits in LLCΛ out (c α,m (nT b Converting )) to symbolic probability p in (s α,m (nT)), and to ensure that only external information is passed to the decoder, the encoded bits Subtract the LLRΛ from the decoder's previous output out(c α,m (nT b Only after that is it passed to the input of the decoder.

[0086] In an adaptive Turbo equalizer, soft information exchange and interference suppression are performed sequentially and iteratively until communication performance stabilizes. In the following analysis, for ease of expression, the symbol interval T and bit interval T will be used interchangeably. b Omitted.

[0087] Interference Canceller: In the interference cancellation process, it is first necessary to estimate the residual CCI in the signal. Ignoring channel estimation errors, the estimated CCI can be expressed as:

[0088]

[0089] In the above formula, q (α,γ),(m,m) (l,n) represents the combined channel response shown in formula (8). Symbol s γ,m The decision value of (n). In the first iteration, Set to zero. In subsequent iterations, According to the formula Perform the calculation, where s represents 2 Q A symbol set consisting of 1 symbol Complex values ​​within, p in [s γ,m [n] = s] indicates that when s γ,m Input prior information when (n) = s.

[0090] The estimated CCI is derived from r α,m The output signal after removing interference from (n) is:

[0091]

[0092] The soft-information-based adaptive decision feedback equalizer (hereinafter referred to as soft-information-based ADFE): As shown in Figure 5(b), the signal processing of the soft-information-based ADFE consists of signal filtering (represented by the solid line) and adaptive coefficient adjustment (represented by the dashed line).

[0093] definition

[0094] Where N f and N b These represent the lengths of the feedforward filter and the feedback filter, respectively. This represents the input to the feedback filter. In the first iteration, The value of is equal to the hard decision of the filtered output signal, and in subsequent iterations,

[0095] The input vector for signal filtering is defined as:

[0096]

[0097] The filtered output signal can be expressed as:

[0098]

[0099] Where f α,m (n) is the filter coefficient vector, which consists of the forward filter coefficients. and feedback filter coefficients constitute.

[0100] In the proposed method, the recursive expected least squares (RELS) algorithm is used for adaptive coefficient adjustment, and the update steps are as follows.

[0101] 1) Calculate the Kalman gain

[0102]

[0103] J α,m (n-1) represents the inverse covariance matrix, and λ is the forgetting factor.

[0104] 2) Update the coefficient vector

[0105]

[0106] in This is the decision value used for adaptive coefficient adjustment. When ADFE is working in training mode, equals s α,m (n), when ADFE is working in decision mode Calculate according to the following formula:

[0107]

[0108] In the formula In the first iteration, all symbols are assumed to be equally probable, i.e., p in [s α,m (n) = s] = 1 / 2 Q .

[0109] 3) Update the inverse matrix

[0110]

[0111] As can be seen from equation (17), in order to calculate Need to Choose an appropriate model. Research shows that ignoring... The included s α,m The information (n) will only add some approximately Gaussian distributed noise to the system, therefore It can be used Approximation is performed.

[0112] In equation (17) use By making the substitution, we can obtain

[0113]

[0114] in Assuming a Gaussian distribution with mean s and variance calculated according to the reference (Yellepeddi A, Preisig J C. Design of decision device for the adaptation of decision directed equalizers [C] / / IEEE.IEEE, 2013.).

[0115] Decoder: Equalized output signal It is mapped to the output probability. For each symbol set... For the value s, its output probability is:

[0116]

[0117] After the output probabilities are demapped and deinterleaved, the LLR of the encoded bits can be obtained.

[0118]

[0119] To ensure that only external information is passed to the decoder, the encoded bits... Subtract the LLRΛ from the decoder's previous output out (c α,m (n) is then passed to the input of the decoder. In the first iteration, Λ out (c α,m The initial value of (n) is set to 0.

[0120] The underwater acoustic communication method provided in this embodiment is verified using a set of underwater acoustic channels measured in the channel pool of Harbin Engineering University. A specific calculation example is given below to illustrate the effectiveness of the underwater acoustic communication method (hereinafter referred to as the proposed method) provided in this embodiment.

[0121] During underwater acoustic channel testing, the two transmitting elements were positioned 1.5m and 2m above the water surface, and the two receiving elements were positioned 0.7m and 0.9m above the water surface. The communication distance between the transmitter and receiver was 15m, and the received signal-to-noise ratio was approximately 24dB. The signals transmitted by the two transmitting elements consisted of an 8-16kHz Hamming windowed linear frequency modulated (LFM) signal, a guard time interval, and an information signal. The communication band of the information signal was 8-16kHz, divided into four sub-bands based on the FMT principle. The roll-off factor of the transmit and receive filters in each band was set to 0.5. The channel estimation algorithm used was the recursive least squares (RLS) algorithm, with a forgetting factor set to 0.999. Figure 6 shows the combined channel response after TR processing obtained in the experiment.

[0122] Figure 6(a)-(d) represents the combined channel response after TR processing of the four sub-bands for user 1, and Figure 6(e)-(h) represents the combined channel response after TR processing of the four sub-bands for user 2. The dashed line in Figure 6 represents the combined channel response q. (α,α),(m,m) (t), which reflects the suppression of ISI by TR, and the dotted line represents the combined channel response q. (α,γ),(m,m) (t), α≠γ, which reflects the inhibition of CCI by TR, and q (α,α),(m,m) The magnitude of q is normalized relative to its maximum value. (α,γ),(m,m) (t), α≠γ relative to q (α,α),(m,m) The maximum value of (t) was normalized. From Figure 6, q... (α,α),(m,m) The waveform (t) shows that uncompressed sidelobes still exist after TR processing, which leads to ISI, caused by q (α,γ),(m,m) As can be seen from the waveform of (t), α≠γ, its amplitude is not compressed to 0, which will lead to CCI.

[0123] The proposed method was verified by simulation based on the measured channel obtained from the pool experiment. For comparison, two other methods were also simulated and analyzed: 1) the interference suppression method without ECC (hereinafter referred to as the method without ECC) and 2) the interference suppression method based on hard decision equalization and decoding (hereinafter referred to as the hard decision method).

[0124] In the simulation, the communication frequency band, carrier modulation mode, number of sub-bands, roll-off factor, and symbol rate on each sub-band are the same as in the actual experiment. Other parameter settings are shown in the table below.

[0125] Table 1 shows the parameter settings for the proposed method and the two comparative methods.

[0126]

[0127]

[0128] The performance comparison between the proposed method and the method without ECC is as follows: the output bit error rate (BER) of the decoder in each sub-band using the proposed method is compared with the output BER of the equalizer in each sub-band using the method without ECC. Simulation results without noise are shown in Table 2, and the results of obtaining a 10dB input signal-to-noise ratio by adding Gaussian white noise are shown in Figure 7. As can be seen from Table 2, after three iterations, the BER for both users in the proposed method reaches zero. However, in the method without ECC, the BER remains at 10 in the second sub-band for both users and the fourth sub-band for user 2. -4 The difference is on the order of magnitude. This indicates that when residual ISI and CCI are the main factors affecting communication performance, the proposed method outperforms the other method due to its superior residual ISI and CCI suppression performance. As shown in Figure 7, when the input signal-to-noise ratio is 10 dB and the performance of both methods has stabilized, the BER of the proposed method is significantly lower than that of the method without ECC. This demonstrates that when noise, residual ISI, and CCI are all major factors affecting communication performance, the proposed method's advantage is more pronounced due to the inclusion of ECC.

[0129] Table 2. Performance comparison of the proposed method without noise and the method without ECC.

[0130]

[0131] The performance comparison between the proposed method and the hard-decision method is as follows: the output symbol error rate (SER) of each equalizer and the output BER of each decoder are used to compare the performance of the proposed method and the hard-decision method.

[0132] Table 3 compares the BER of the proposed method with that of the hard-decision method without noise.

[0133]

[0134] The results shown in Figures 8-10 and Table 3 demonstrate that when the performance of both methods stabilizes, the proposed method outperforms the hard-decision-based method under different noise intensities, with the performance advantage becoming more pronounced when the input signal-to-noise ratio is 10 dB. This is because the proposed method utilizes soft information in signal processing to improve the performance of equalization and decoding.

[0135] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0136] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A MIMO-FMT-TR underwater acoustic communication system, characterized in that, include: The transmitter is used to process each user information sequence using error control coding technology and transmit the processed user information sequence to the receiver via an underwater acoustic channel; the transmitter includes multiple identical transmitting components; The number of transmitting components is the same as the number of received user information sequences, enabling processing of all received user information sequences; wherein each transmitting component includes: a serial-to-parallel converter, multiple symbol sequence generators, an FMT modulator, and a transmitting array element; The receiving end is used to process the demodulated user information sequence using TR technology to obtain the TR output signal, and to perform post-processing on the TR output signal according to adaptive turbo equalization technology; the adaptive turbo equalization technology includes interference cancellation technology, soft information-based adaptive decision feedback equalization technology, and decoding technology; the receiving end includes multiple receiving array elements, and further includes: An FMT demodulator is used to demodulate the signal received by one receiving array element to obtain M sub-signals. The number of receiving array elements is the same as the number of FMT demodulators, and the receiving array elements and the FMT demodulators correspond one-to-one, enabling FMT demodulation of signals received by all receiving array elements. A divider is used to divide the sub-signals output by all FMT demodulators into M second sub-information sequence groups; each sub-signal output by the FMT demodulator is labeled, and the second sub-information sequence group includes the sub-signals output by each FMT demodulator with the same label; A TR processor is used to process a second sub-information sequence group using TR technology to obtain a TR output signal; the number of TR processors is the same as the number of second sub-information sequence groups, and the TR processors correspond one-to-one with the second sub-information sequence groups, enabling processing of all second sub-information sequence groups; An adaptive turbo equalizer is used to post-process a TR output signal using adaptive turbo equalization technology until the communication performance is stable, thereby obtaining a post-processed signal; the number of adaptive turbo equalizers is the same as the number of TR output signals, and the adaptive turbo equalizers correspond one-to-one with the TR output signals, enabling post-processing of all TR output signals; A combiner is used to combine the post-processed signals output by all adaptive turbo equalizers to obtain multiple combined signals; the sub-signals in the post-processed signals output by each adaptive turbo equalizer are labeled, and the combined signals include the sub-signals with the same label in the post-processed signals output by each adaptive turbo equalizer; the number of combined signals is the same as the number of user information sequences. A parallel-to-serial converter is used to perform parallel-to-serial conversion on a combined signal; the number of parallel-to-serial converters is the same as the number of combined signals, and the parallel-to-serial converters correspond one-to-one with the combined signals, enabling parallel-to-serial conversion on all combined signals.

2. The MIMO-FMT-TR underwater acoustic communication system according to claim 1, characterized in that, The serial-to-parallel converter is used to convert a received user information sequence into a serial-to-parallel sequence to obtain multiple first sub-information sequence groups. The symbol sequence generator is used to encode and interleave each sub-information sequence in a first sub-information sequence group output by the serial-to-parallel converter, and generate a symbol sequence in groups of Q bits; the number of symbol sequence generators is the same as the number of the first sub-information sequence groups, and the symbol sequence generators correspond one-to-one with the first sub-information sequence groups, and can process all the first sub-information sequence groups output by the serial-to-parallel converter; The FMT modulator is used to perform FMT modulation on the symbol sequences output by all symbol sequence generators to obtain a modulation sequence. The transmitting array element is used to transmit the modulation sequence output by the FMT modulator to the corresponding receiving array element in the receiving end through the underwater acoustic channel; wherein, the modulation sequence is the processed user information sequence.

3. The MIMO-FMT-TR underwater acoustic communication system according to claim 2, characterized in that, The number of transmitting array elements is the same as the number of receiving array elements, and the transmitting array elements correspond one-to-one with the receiving array elements.

4. The MIMO-FMT-TR underwater acoustic communication system according to claim 3, characterized in that, The receiving array element is used to receive the modulation sequence transmitted through the underwater acoustic channel.

5. A MIMO-FMT-TR underwater acoustic communication system according to claim 4, characterized in that, The adaptive turbo equalizer includes: An interference canceller is used to estimate the residual CCI in the TR output signal and remove the estimated residual CCI. The soft-information adaptive decision feedback equalizer includes a signal filtering section and an adaptive coefficient adjustment section. The signal filtering section is used to determine the filter coefficients based on the adaptive coefficients output by the adaptive coefficient adjustment section, and to filter and remove residual ISI in the signal output by the interference canceller based on the filter coefficients. The adaptive coefficient adjustment section is used to adjust the adaptive coefficients using a recursive expectation least squares algorithm and the signal output by the decoder. The decoder is used to perform noise reduction on the signal output by the soft-information adaptive decision feedback equalizer by utilizing the log-likelihood ratio of the encoded bits.

6. A MIMO-FMT-TR underwater acoustic communication method, characterized in that, include: The transmitter uses error control coding technology to process each user information sequence and transmits the processed user information sequence to the receiver through an underwater acoustic channel; the transmitter includes multiple identical transmitting components; The number of transmitting components is the same as the number of received user information sequences, enabling processing of all received user information sequences; wherein each transmitting component includes: a serial-to-parallel converter, multiple symbol sequence generators, an FMT modulator, and a transmitting array element; The receiver uses TR technology to process the demodulated user information sequence to obtain the TR output signal, and then performs post-processing on the TR output signal according to adaptive turbo equalization technology. The adaptive turbo equalization technology includes interference cancellation technology, soft-information-based adaptive decision feedback equalization technology, and decoding technology. The receiver includes multiple receiving array elements and further includes: An FMT demodulator is used to demodulate the signal received by one receiving array element to obtain M sub-signals. The number of receiving array elements is the same as the number of FMT demodulators, and the receiving array elements and the FMT demodulators correspond one-to-one, enabling FMT demodulation of signals received by all receiving array elements. A divider is used to divide the sub-signals output by all FMT demodulators into M second sub-information sequence groups; each sub-signal output by the FMT demodulator is labeled, and the second sub-information sequence group includes the sub-signals output by each FMT demodulator with the same label; A TR processor is used to process a second sub-information sequence group using TR technology to obtain a TR output signal; the number of TR processors is the same as the number of second sub-information sequence groups, and the TR processors correspond one-to-one with the second sub-information sequence groups, enabling processing of all second sub-information sequence groups; An adaptive turbo equalizer is used to post-process a TR output signal using adaptive turbo equalization technology until the communication performance is stable, thereby obtaining a post-processed signal; the number of adaptive turbo equalizers is the same as the number of TR output signals, and the adaptive turbo equalizers correspond one-to-one with the TR output signals, enabling post-processing of all TR output signals; A combiner is used to combine the post-processed signals output by all adaptive turbo equalizers to obtain multiple combined signals; the sub-signals in the post-processed signals output by each adaptive turbo equalizer are labeled, and the combined signals include the sub-signals with the same label in the post-processed signals output by each adaptive turbo equalizer; the number of combined signals is the same as the number of user information sequences. A parallel-to-serial converter is used to perform parallel-to-serial conversion on a combined signal; the number of parallel-to-serial converters is the same as the number of combined signals, and the parallel-to-serial converters correspond one-to-one with the combined signals, enabling parallel-to-serial conversion on all combined signals.

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