A GMSK underwater acoustic communication method and system based on frequency domain equalization

Abstract

The application provides a GMSK underwater acoustic communication method and system based on frequency domain equalization, and the method comprises the following steps: adding a cyclic prefix before a transmission signal sequence, adding a tail symbol after the transmission signal sequence, carrying out conversion and information-symbol mapping, modulating according to a GMSK signal modulation mode based on Laurent decomposition, obtaining a GMSK complex baseband signal, modulating the GMSK complex baseband signal to a carrier as a final transmission signal, and transmitting the final transmission signal to an underwater acoustic channel; receiving the transmission signal in the underwater acoustic channel, carrying out band-pass filtering, time-frequency two-dimensional synchronization, matched filtering, frequency domain equalization and iterative symbol detection on the received signal, obtaining a judged symbol, and completing the GMSK underwater acoustic communication based on the frequency domain equalization. The application has the advantages that the complexity of the symbol detection of the conventional frequency domain equalizer is reduced, and the communication performance is improved.

Description

Technical Field

[0001] This invention belongs to the field of underwater acoustic communication, specifically relating to a GMSK underwater acoustic communication method and system based on frequency domain equalization. Background Technology

[0002] Underwater acoustic communication channels are highly complex, characterized by frequency-selective fading and limited bandwidth. Gaussian minimum frequency shift keying (GMSK) modulation, due to its continuous phase and constant envelope, offers excellent power and spectral efficiency, effectively improving the effectiveness and reliability of communication systems. However, the complex channel structure in underwater acoustic communication leads to strong inter-symbol interference in the received signal, necessitating the use of appropriate equalization techniques to process the received signal.

[0003] Traditional frequency domain equalization techniques can effectively address interference from multipath channels, but they still have some shortcomings in symbol detection:

[0004] (1) Symbol detection based on the Viterbi algorithm has extremely high complexity, which increases exponentially with the signal length;

[0005] (2) Although the simplified symbol detection algorithm reduces the complexity, it does not utilize the inherent coding gain of the GMSK signal, resulting in a significant performance loss. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing technologies in underwater acoustic signal communication, such as high computational complexity and significant performance loss of detection algorithms.

[0007] To achieve the above objectives, this invention proposes a GMSK underwater acoustic communication method based on frequency domain equalization, the method comprising:

[0008] Step 1: Add the cyclic prefix before the transmitted signal sequence and the tail symbol after the transmitted signal sequence, perform conversion and information-symbol mapping, and then modulate according to the GMSK signal modulation method based on Laurent decomposition to obtain the GMSK complex baseband signal. Modulate the GMSK complex baseband signal onto the carrier as the final transmitted signal and transmit it into the underwater acoustic channel.

[0009] Step 2: The underwater acoustic channel receives the transmitted signal, performs bandpass filtering, time-frequency two-dimensional synchronization, matched filtering, frequency domain equalization, and iterative symbol detection on the received signal to obtain the decided symbol, thus completing GMSK underwater acoustic communication based on frequency domain equalization.

[0010] As an improvement to the above method, step 1 specifically includes:

[0011] Step 1-1: Construct the transmitted signal frame;

[0012] The transmitted signal frame is constructed, and its structure includes a cyclic prefix, a sequence of information to be transmitted, and a tail symbol. The phase state of the cyclic prefix starts from and ends at zero. The tail symbol is used to ensure that the phase state of the sequence of information to be transmitted returns to zero.

[0013] Steps 1-2: Convert the transmitted signal and perform information-symbol mapping;

[0014] For the transmitted signal sequence {a k} is transformed to obtain the bipolar non-return-to-zero sequence {x}. k}, the baseband signal sequence {b} mapped to GMSK via information-symbol mapping. n}:

[0015]

[0016] Among them, b n Represents the GMSK complex baseband signal sequence {b n Elements in}; x k Represents the bipolar non-return-to-zero code signal sequence {x k The elements in}; N represents the data block length; j represents an imaginary number;

[0017] Steps 1-3: Modulate the GMSK signal based on Laurent decomposition to obtain the GMSK complex baseband signal;

[0018] Based on the Laurent decomposition of the GMSK signal, a phase shaping function is used to analyze the GMSK signal sequence {b}. n The signal is modulated to obtain the GMSK complex baseband signal s(t):

[0019]

[0020] Where t represents time; T represents the symbol period; and c(t-nT) is the partial impulse response.

[0021]

[0022]

[0023]

[0024] Where L represents the pulse memory length, q(t) represents the integral function of the frequency shaping pulse g(τ), and τ represents the independent variable of the integrand.

[0025]

[0026] Where B represents bandwidth; Q() represents complementary error function:

[0027]

[0028]

[0029] Where ε represents the independent variable of the integrand;

[0030] Steps 1-4: Modulate the GMSK complex baseband signal onto the carrier wave as the final transmit signal and transmit it into the underwater acoustic channel;

[0031] Final transmitted signal s f (t) is represented as:

[0032] s f (t)=s(t)×exp(j×2π×fc×t)

[0033] Where fc is the center frequency of the carrier.

[0034] As an improvement to the above method, step 2 specifically includes:

[0035] Step 2-1: Receive signal r f (t) performs bandpass filtering and uses a synchronization sequence for time-frequency two-dimensional synchronization;

[0036] Step 2-2: Use a low-pass filter and a coherent receiver to process the received signal r. f (t) is subjected to matched filtering to obtain the complex baseband signal r n ;

[0037] Steps 2-3: Utilizing the complex baseband signal r n Channel estimation is performed using the cyclic prefix;

[0038] Steps 2-4: For the complex baseband signal r after removing the cyclic prefix n After performing a Fourier transform, and then combining it with the estimated channel for frequency domain equalization, the equalized soft information is obtained.

[0039] Steps 2-5: Equalization of soft information Perform demapping, separating the mapped information according to the real and imaginary parts;

[0040] Steps 2-6: Calculate the joint probability using the built-in encoding method of GMSK, and perform iterative detection using the obtained log-likelihood ratio;

[0041] Steps 2-7: Utilize the log-likelihood ratio obtained after iterative detection Hard decision is performed, and the received symbols are obtained through differential decoding to complete GMSK underwater acoustic communication based on frequency domain equalization.

[0042] As an improvement to the above method, step 2-1 specifically includes:

[0043] Received signal r f (t) is represented as:

[0044]

[0045] Among them, h η The channel response is represented by z(t); the received noise is represented by s. f () represents the final transmitted signal, and η represents the subscript of the integral, where η = 0, 1, 2, ..., N-1.

[0046] As an improvement to the above method, step 2-2 specifically includes:

[0047]

[0048] Where n represents the number of the output symbol, s n The signal output by the matched filter:

[0049]

[0050] z n This represents the noise output of the matched filter:

[0051]

[0052] As an improvement to the above method, steps 2-3 utilize the complex baseband signal r n The channel estimation method using the cyclic prefix is ​​the orthogonal matching pursuit algorithm.

[0053] As an improvement to the above method, steps 2-4 are specifically as follows:

[0054] Frequency domain of the signal after frequency domain equalization Represented as:

[0055]

[0056] Among them, R k The frequency domain expression representing the output signal of the matched filter; W represents the equalization coefficient of the frequency domain equalizer; k The whitening noise filter is denoted as:

[0057]

[0058] Where C(0,0;k) is obtained by the discrete Fourier transform of c(0,0;l); c(0,0;l) is the correlation coefficient, l is the sign number in the correlation coefficient in the time domain, c(0,0;l) is approximately {1,0.447,0.028,8.4×10-5,0,…,8.4×10-5,0.028,0.447}, and only the first three terms and the last two terms in c(0,0;l) are considered during iterative detection; k is the sign number in the correlation coefficient in the frequency domain;

[0059] Equilibrium Coefficient Represented as:

[0060]

[0061] Among them, H k N represents the estimated channel frequency response; N0 represents the noise power spectrum.

[0062] right Obtain the soft information after equilibrium by performing an inverse Fourier transform.

[0063] As an improvement to the above method, steps 2-5 are specifically as follows:

[0064] Mapped information sequence Represented as:

[0065]

[0066] For sequence Perform separation, define

[0067] Set the information u after separation n and v n After energy normalization, it follows a Gaussian distribution with a mean of 1:

[0068]

[0069]

[0070] Where Pr() is the defined probability distribution function; in calculating Pr(u n When ζ∈{±1}, in calculating Pr(v) n When ξ = ξ), ξ∈{0,±1}; σ u and σ v Based on the cyclic prefix and The obtained variance.

[0071]

[0072]

[0073] in, and They represent u respectively n and v n The cyclic prefix part.

[0074] As an improvement to the above method, steps 2-6 are specifically as follows:

[0075] To perform joint probability calculations over the logarithmic field, we define the probability distribution function:

[0076]

[0077] u n Log-likelihood ratio L(u) n ) is represented as:

[0078]

[0079] The log-likelihood ratio of the estimated symbol is obtained by calculating the joint probability.

[0080]

[0081] Among them, E(u n ) and E(v n ) respectively represent the sequence {u n} and sequence {v n} energy; A n This indicates the influence of the two symbols before and after the current moment on the current symbol.

[0082] A n =c(0,0;3)×(Pr(u n-2 =1)+Pr(u n+2 =1)-1), during iterative calculation, corresponding to u n The probability is obtained from the log-likelihood ratio.

[0083] As an improvement to the above method, steps 2-7 are specifically as follows:

[0084] The log-likelihood ratio obtained after iterative detection Obtain the estimated symbol by making a hard decision. Differential decoding can be used to obtain the estimated received symbol.

[0085]

[0086] As an improvement to the above method, the iteration is performed as follows: during the first equalization of the received signal, the soft information after frequency domain equalization is obtained. In subsequent iterations, the results obtained from the previous iteration are used. Replace u n , and combined with v n Estimate the probability values ​​of symbols at different times, and then calculate the joint probability to obtain the log-likelihood ratio of the estimated symbols.

[0087] The specific steps of iterative calculation include:

[0088] a) The result of the previous iteration Perform energy normalization and replace u. n The value;

[0089] b) Calculate the probability value Pr(u) after replacement. n =ζ);

[0090] c) Calculate based on probability values:

[0091] The present invention also provides a GMSK underwater acoustic communication system based on frequency domain equalization, the system comprising:

[0092] The signal transmission module is used to add a cyclic prefix to the beginning of the transmitted signal sequence and a tail symbol to the end of the transmitted signal sequence, perform conversion and information-symbol mapping, and then modulate the signal according to the GMSK signal modulation method based on Laurent decomposition to obtain the GMSK complex baseband signal. The GMSK complex baseband signal is then modulated onto the carrier as the final transmitted signal and transmitted to the underwater acoustic channel.

[0093] The signal receiving module is used to receive transmitted signals in the underwater acoustic channel. It performs bandpass filtering, time-frequency two-dimensional synchronization, matched filtering, frequency domain equalization, and iterative symbol detection on the received signals to obtain the decided symbols and complete GMSK underwater acoustic communication based on frequency domain equalization.

[0094] Compared with the prior art, the advantages of the present invention are:

[0095] 1) In frequency domain equalization based on Laurent decomposition, the design of an iterative detector based on posterior probability is used to replace Viterbi decoding, which greatly reduces the complexity of symbol detection in conventional frequency domain equalizers.

[0096] 2) Compared to simplified symbol detectors, posterior probability-based symbol detectors effectively utilize the coding gain inherent in GMSK while slightly increasing complexity, thereby improving communication performance. Attached Figure Description

[0097] Figure 1 The diagram shows a data block structure of the transmitted signal.

[0098] Figure 2 The diagram shown is a schematic of the underwater acoustic signal transceiver structure.

[0099] Figure 3 The following is a flowchart of the signal equalization detection process;

[0100] Figure 4 The image shows the time-domain waveform of the underwater acoustic signal.

[0101] Figure 5 The diagram shown is a measured channel diagram of underwater acoustic communication used in the simulation.

[0102] Figure 6 The figure shown is the time-domain waveform of the received signal after bandpass filtering in the simulation.

[0103] Figure 7 The diagram shown is a constellation diagram after matched filtering.

[0104] Figure 8 The diagram shows the soft information output by the detector after two iterations.

[0105] Figure 9 The image shows histograms of the soft information output after iteration 0 and iteration 2.

[0106] Figure 10 The figure shows the bit error rate curve obtained from the simulation under the sparse channel shown in Table 2. Detailed Implementation

[0107] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0108] To address the inter-symbol interference problem in GMSK underwater acoustic communication caused by complex underwater acoustic channels, this invention proposes an iterative detection technique for GMSK underwater acoustic communication based on frequency domain equalization. Building upon the traditional frequency domain equalization technique based on Laurent decomposition, this invention improves symbol detection by utilizing the equivalent noise variance obtained from the training sequence. Iterative detection effectively leverages the coding gain of GMSK, thereby significantly enhancing the transmission performance of GMSK signals in underwater acoustic channels, while also exhibiting lower complexity compared to Viterbi symbol detection.

[0109] The underwater signal transmission method of the present invention includes the following steps:

[0110] After adding a tail symbol and a cyclic prefix, the transmitted signal sequence is transformed and mapped to information-symbols. Then, it is modulated according to the GMSK signal modulation method based on Laurent decomposition to obtain the GMSK complex baseband signal, which is then modulated onto the carrier as the final transmitted signal and transmitted into the underwater acoustic channel.

[0111] The underwater acoustic channel receives the final transmitted signal, treats it as the received signal, and performs bandpass filtering, time-frequency two-dimensional synchronization, matched filtering, frequency domain equalization, and iterative symbol detection on the received signal to obtain the decided symbol, thus completing the iterative detection of GMSK underwater acoustic communication based on frequency domain equalization.

[0112] 1) Parameter Design

[0113] The parameters involved in this invention include data block length N, information sequence length L, cyclic prefix length G, and tail symbol sequence length S. The cyclic prefix sequence length must be greater than the maximum channel delay.

[0114] 2) Signal frame structure design

[0115] The frame structure of the transmitted signal includes a cyclic prefix, a sequence of information to be transmitted, and a tail symbol. The cyclic prefix can be any sequence unrelated to the information sequence, and its phase state must start and end at zero. The tail symbol ensures that the phase state of the information sequence to be transmitted returns to zero.

[0116] 3) Information-symbol mapping

[0117] First, the emission sequence {a k} is transformed to obtain the bipolar non-return-to-zero sequence {x}. k}

[0118] The GMSK complex baseband signal sequence {bn} can be represented as:

[0119]

[0120] Among them, b n For the GMSK complex baseband signal sequence {b n Elements in}; x k For a bipolar non-return-to-zero code signal sequence {x k The elements in}; j is an imaginary number;

[0121] Based on the Laurent decomposition of the GMSK signal, a phase shaping function is used to analyze the GMSK signal sequence {b}. n The signal is modulated to obtain the GMSK complex baseband signal s(t):

[0122]

[0123] Where t represents time and T represents the symbol period.

[0124] And modulate it onto a carrier wave as the final transmitted signal s f (t), transmitted to the underwater acoustic channel:

[0125] s f(t)=s(t)×exp(j×2π×fc×t) (3)

[0126] Where c(t-nT) is the partial impulse response, j is the imaginary number, and fc is the center frequency of the carrier.

[0127] Let t 1 = t - nT; then c(t) 1 ) = c(t-nT);

[0128]

[0129]

[0130]

[0131] Where q(t) is the integral function of the frequency shaping pulse g(τ); τ represents the independent variable of the integrand.

[0132]

[0133] Where T is the symbol interval, B is the bandwidth, and L is the pulse memory length, set BT = 0.3 and L = 3;

[0134]

[0135] in, or Q(x 1 ) represents the complementary error function; ε represents the independent variable of the integrand.

[0136] The receiving method of the present invention includes the following steps:

[0137] The underwater acoustic channel receives the final transmitted signal, uses it as the received signal, and performs bandpass filtering, time-frequency two-dimensional synchronization, matched filtering, frequency domain equalization, and iterative symbol detection on the received signal to obtain the decided symbol, thus completing the iterative detection of GMSK underwater acoustic communication based on frequency domain equalization.

[0138] Received signal r f (t) can be represented as:

[0139]

[0140] Among them, h η The channel response is z(t); the received noise is z(t); and the channel response is s(t). f () represents the final transmitted signal, η represents the subscript of the integral, η = 0, 1, 2, ..., N-1;

[0141] Its specific process includes:

[0142] 1) For the received signal r f (t) performs bandpass filtering and uses a synchronization sequence for time-frequency two-dimensional synchronization.

[0143] 2) Use a low-pass filter and a coherent receiver to process the received signal r f (t) is subjected to matched filtering to obtain the complex baseband signal r n The coherent receiver is used to match the c(t) pulse.

[0144] r f After passing through a low-pass filter, the signal r(t) is obtained:

[0145]

[0146] Where n represents the number of the output symbol.

[0147]

[0148] Among them, s n The signal output by the matched filter; z n The noise output of the matched filter; η is used to represent the subscript of the integral; η = 0, 1, 2, ..., N-1;

[0149]

[0150]

[0151] 3) Utilizing the complex baseband signal r n Channel estimation is performed using a cyclic prefix, and the methods used include, but are not limited to, the Orthogonal Matching Pursuit (OMP) algorithm.

[0152] 4) For the complex baseband signal r after removing the cyclic prefix n After performing a Fourier transform and then combining it with the estimated channel for frequency domain equalization, the equalized soft information is obtained.

[0153]

[0154] in, Here is the frequency domain expression of the signal after frequency domain equalization; R k The frequency domain expression for the output signal of the matched filter; W represents the equalization coefficient of the frequency domain equalizer. k The whitening noise filter is denoted as:

[0155]

[0156] in, c(0,0;l) is the correlation coefficient, l is the sign number of the correlation coefficient in the time domain, and k is the sign number of the correlation coefficient in the frequency domain; DFT is the Discrete Fourier Transform.

[0157] Equilibrium Coefficient It can be represented as

[0158]

[0159] Among them, H k Let N0 be the estimated channel frequency domain response and N0 be the noise power spectrum. For the GMSK signal, c(0,0;l) can be approximated as {1,0.447,0.028,8.4×10⁻⁵,0,…,8.4×10⁻⁵,0.028,0.447}. Since c(0,0;l) contains very low energy, only the first three terms and the last two terms of c(0,0;l) are considered during iterative detection.

[0160] Finally, The inverse Fourier transform can be used to obtain the soft information after equilibrium.

[0161] 5) For the soft information after equalization Perform demapping, separate the mapped information according to the real and imaginary parts, and calculate the log-likelihood value.

[0162] Mapped information sequence It can be represented as

[0163]

[0164] For sequence Perform separation, define And calculate u n Log-likelihood ratio L(u) n ),

[0165]

[0166] Wherein, it is assumed that the separated information u n and v n After energy normalization, it follows a Gaussian distribution with a mean of 1, i.e.

[0167]

[0168]

[0169] Where Pr() is the defined probability distribution function; in calculating Pr(u n When ζ∈{±1}, in calculating Pr(v) n When σ = ξ, ξ∈{0,±1}. u and σv Based on the training sequence and The obtained variance.

[0170]

[0171]

[0172] 6) The joint probability is obtained by using the built-in encoding method of GMSK, and the obtained log-likelihood ratio is used to replace L(u) in equation (18) for iterative detection.

[0173] To ensure computational stability, we consider performing joint probability calculations over the logarithm field; therefore, we define the probability distribution function. Then equation (18) can be rewritten as

[0174]

[0175] The log-likelihood ratio of the estimated symbol can be obtained by calculating the joint probability.

[0176]

[0177] Among them, κ is related to the energy and variance of the output prior information. E(u n ) and E(v n ) respectively represent the sequence {u n} and sequence {v n The energy of}.

[0178] A n This represents the influence of the two symbols before and after the current moment on the current symbol. Expanding the first term of equation (24) yields...

[0179]

[0180] Among them, A n =c(0,0;3)×(Pr(u n-2 =1)+Pr(u n+2 =1)-1), during iterative calculation, corresponding to u n The probability can be obtained from the log-likelihood ratio.

[0181] 7) Utilize the log-likelihood ratio obtained after iterative detection Hard decision is performed, and the received symbols are obtained through differential decoding to complete the iterative detection of GMSK underwater acoustic communication based on frequency domain equalization.

[0182] The log-likelihood ratio obtained after iterative detection Obtain the estimated symbol by making a hard decision. Differential decoding can be used to obtain the estimated received symbol.

[0183]

[0184] The above iteration method is as follows: during the first equalization of the received signal, obtain the soft information after frequency domain equalization. In subsequent iterations, the results obtained from the previous iteration are used. Replace u n , and combined with v n Estimate the probability values ​​of symbols at different times, and then calculate the joint probability to obtain the log-likelihood ratio of the estimated symbols.

[0185] The specific steps of iterative calculation include:

[0186] a) Based on the results of the previous iteration Perform energy normalization and replace u. n The value;

[0187] b) Calculate the probability value Pr(u) after replacement. n =ζ);

[0188] c) Calculate based on probability values:

[0189] This invention studies an equalization method for GMSK underwater acoustic communication. Compared with existing technologies, this invention has the following advantages in underwater acoustic multipath channel communication:

[0190] 1) In frequency domain equalization based on Laurent decomposition, the design of an iterative detector based on posterior probability is used to replace Viterbi decoding, which greatly reduces the complexity of symbol detection in conventional frequency domain equalizers.

[0191] 2) Compared to simplified symbol detectors, posterior probability-based symbol detectors effectively utilize the coding gain inherent in GMSK while slightly increasing complexity, thereby improving communication performance.

[0192] In this embodiment of the invention, the application of the method of the invention in underwater GMSK signal transmission is discussed using simulation. The signal transmission system bandwidth is set to 4-8 kHz, the center frequency to 6 kHz, the symbol period for GMSK modulation to be 0.5 ms, and the original information to be transmitted to be 2048 bits. The transmission signal parameters are set as shown in the table below:

[0193] Table 1. Parameter settings for transmitted signals

[0194]

[0195]

[0196] Transmitter process reference Figure 2 First, the frame structure is designed by adding a 1024-bit cyclic prefix, which is composed of special words. The complete frame structure of the transmitted signal is as follows: Figure 1 As shown. Mapping and modulation are performed based on the Laurent decomposition of the GMSK signal, and the actual transmitted signal is as follows. Figure 4 As shown, the signal is transmitted through... Figure 5 The measured underwater acoustic channel received the signal, with the in-band signal-to-noise ratio set to 15dB.

[0197] Receiver process reference Figure 2 The time-frequency two-dimensional synchronization is performed using a synchronization sequence, and the received signal is bandpass filtered. The filtered time-domain waveform is as follows: Figure 6 As shown. The complex baseband signal r is obtained by performing matched filtering on the bandpass-filtered signal using Equation 11. n At this time, the constellation chart ( Figure 7 (As shown) Symbol decision cannot be performed due to multipath interference. (Reference) Figure 3 For complex baseband signals {r n Equalization and demodulation are performed. First, the channel is estimated using a cyclic prefix algorithm. And for the deprefixed complex baseband signal {r n Frequency domain equalization is performed to obtain the frequency domain equalization output soft information. The equilibrium coefficient is shown in equation (16). For Mapping is performed to obtain soft information The mapping method is shown in equation (17). By separating the real and imaginary parts, Decomposed into {u n} and {v n} and perform joint probability estimation. In the initial estimation, only {u n The log-likelihood ratio L(u) is estimated by performing the estimation. n ), and then use L(u n Calculate the probability values ​​for other state representations and perform joint probability estimation to obtain the estimated log-likelihood ratio. Can replace {u n Iterative calculations were performed, but due to the limited coding gain contained in the GMSK signal, the gain obtained after two iterations decreased significantly. After iteration... Figure 8 As shown Perform hard decision to obtain the estimated symbol sequence. After differential decoding according to equation (26), the complete output information is obtained. A total of 2048 bits were used to complete the entire communication process. Figure 9The distribution of the detector output soft information is shown in the graphs after 0 iterations and 2 iterations. It can be seen that the detector output soft information is more concentrated after 2 iterations, and has better performance.

[0198] Bit error rate simulation was performed using the sparse channel shown in Table 2, and the bit error rate curves are as follows. Figure 10 As shown, when the bit error rate is 10⁻⁶, compared with the detection scheme with 0 iterations, the performance is improved by 2dB with 1 iteration, 2.7dB with 2 iterations, and 3.1dB with 3 iterations.

[0199] Table 2 shows the simulated bit error rate curves for sparse channels.

[0200]

[0201] The present invention also provides a GMSK underwater acoustic communication system based on frequency domain equalization, the system comprising:

[0202] The signal transmission module is used to add a cyclic prefix to the beginning of the transmitted signal sequence and a tail symbol to the end of the transmitted signal sequence, perform conversion and information-symbol mapping, and then modulate the signal according to the GMSK signal modulation method based on Laurent decomposition to obtain the GMSK complex baseband signal. The GMSK complex baseband signal is then modulated onto the carrier as the final transmitted signal and transmitted to the underwater acoustic channel.

[0203] The signal receiving module is used to receive transmitted signals in the underwater acoustic channel. It performs bandpass filtering, time-frequency two-dimensional synchronization, matched filtering, frequency domain equalization, and iterative symbol detection on the received signals to obtain the decided symbols and complete GMSK underwater acoustic communication based on frequency domain equalization.

[0204] The present invention also provides a computer device comprising: at least one processor, a memory, at least one network interface, and a user interface. The various components of this device are coupled together via a bus system. It is understood that the bus system is used to enable communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus.

[0205] The user interface may include a display, keyboard, or clicking device (e.g., mouse, trackball, touchpad, or touchscreen).

[0206] It is understood that the memory in the embodiments disclosed in this application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate Synchronous DRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memory.

[0207] In some implementations, the memory stores elements such as executable modules or data structures, or subsets thereof, or extended sets thereof: operating systems and applications.

[0208] The operating system includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application programs include various applications, such as media players and browsers, used to implement various application functions. Programs implementing the methods of the embodiments of this disclosure can be included in the application programs.

[0209] In the above embodiments, the processor can also invoke programs or instructions stored in memory, specifically programs or instructions stored in an application program, for the following purposes:

[0210] Follow the steps described above.

[0211] The above methods can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above methods can be completed by integrated logic circuits in the processor's hardware or by software instructions. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the disclosed methods, steps, and logic block diagrams. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the disclosed methods can be directly implemented by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above methods.

[0212] It is understood that the embodiments described in this invention can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in this application, or combinations thereof.

[0213] For software implementation, the technology of this invention can be achieved by executing the functional modules (e.g., procedures, functions, etc.) of this invention. The software code can be stored in memory and executed by a processor. The memory can be implemented in the processor or externally.

[0214] The present invention may also provide a non-volatile storage medium for storing a computer program. When the computer program is executed by a processor, it can implement the steps in the above method embodiments.

[0215] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A GMSK underwater acoustic communication method based on frequency domain equalization, the method comprising: Step 1: Add the cyclic prefix before the transmitted signal sequence and the tail symbol after the transmitted signal sequence, perform conversion and information-symbol mapping, and then modulate according to the GMSK signal modulation method based on Laurent decomposition to obtain the GMSK complex baseband signal. Modulate the GMSK complex baseband signal onto the carrier as the final transmitted signal and transmit it into the underwater acoustic channel. Step 2: The underwater acoustic channel receives the transmitted signal, performs bandpass filtering, time-frequency two-dimensional synchronization, matched filtering, frequency domain equalization and iterative symbol detection on the received signal to obtain the decided symbol, and completes GMSK underwater acoustic communication based on frequency domain equalization. Step 2 specifically includes: Step 2-1: Receive the signal Bandpass filtering is performed using a synchronization sequence. Two-dimensional synchronization; Step 2-2: Use a low-pass filter and a coherent receiver to process the received signal. Perform matched filtering to obtain the complex baseband signal. ; Steps 2-3: Utilizing complex baseband signals Channel estimation is performed using the cyclic prefix; Steps 2-4: Process the complex baseband signal after removing the cyclic prefix. After performing a Fourier transform, frequency domain equalization is then performed in conjunction with the estimated channel to obtain the equalized result. ; Steps 2-5: After equilibration Perform demapping, separating the mapped information according to the real and imaginary parts; Steps 2-6: Calculate the joint probability using the built-in encoding method of GMSK, and perform iterative detection using the obtained log-likelihood ratio; Steps 2-7: Utilize the logarithm obtained after iterative detection Hard decision is performed, and the received symbols are obtained through differential decoding to complete GMSK underwater acoustic communication based on frequency domain equalization.

2. The GMSK underwater acoustic communication method based on frequency domain equalization according to claim 1, characterized in that, Step 1 specifically includes: Step 1-1: Construct the transmitted signal frame; The transmitted signal frame is constructed, and its structure includes a cyclic prefix, a sequence of information to be transmitted, and a tail symbol. The phase state of the cyclic prefix starts from and ends at zero. The tail symbol is used to ensure that the phase state of the sequence of information to be transmitted returns to zero. Steps 1-2: Convert the transmitted signal and perform information-symbol mapping; For the transmitted signal sequence { a k The transformation yields a bipolar non-return-to-zero sequence. The baseband signal sequence of GMSK, mapped to information-symbols { b n }: ； in, Represents the GMSK complex baseband signal sequence Elements in; Represents a bipolar non-return-to-zero code signal sequence Elements in; N Indicates the length of the data block; j represents an imaginary number; Steps 1-3: Modulate the GMSK signal based on Laurent decomposition to obtain the GMSK signal. Baseband signal; Based on the Laurent decomposition of the GMSK signal, a phase shaping function is applied to this GMSK signal sequence. Modulation was performed to obtain GMSK. Baseband signal : ； in, t Indicates time; T Indicates the symbol period; for Impulse response: ； ； ； Where L represents the pulse memory length, Indicates frequency shaping pulse The integral function; Represents the independent variable of the integrand; ； in, B Indicates bandwidth; express function: ； ； in, Represents the independent variable of the integrand; Steps 1-4: GMSK The baseband signal is modulated onto the carrier wave as the final transmitted signal and transmitted into the underwater acoustic channel; Final transmission signal Represented as: ； in, fc The center frequency of the carrier wave.

3. The GMSK underwater acoustic communication method based on frequency domain equalization according to claim 2, characterized in that, Step 2-1 specifically includes: Received signal Represented as: ； in, Indicates the channel response; Indicates acceptance of noise. This indicates the final transmitted signal. The subscript representing the integral. .

4. The GMSK underwater acoustic communication method based on frequency domain equalization according to claim 3, characterized in that, Step 2-2 specifically includes: ； in, n Indicates the number of the output symbol. The signal output by the matched filter: ； This represents the noise output of the matched filter: 。 5. The GMSK underwater acoustic communication method based on frequency domain equalization according to claim 4, characterized in that, Steps 2-3 utilize Baseband signal The channel estimation method using the cyclic prefix is ​​the orthogonal matching pursuit algorithm.

6. The GMSK underwater acoustic communication method based on frequency domain equalization according to claim 5, characterized in that, Steps 2-4 specifically refer to: Frequency domain of the signal after frequency domain equalization Represented as: ； in, The frequency domain expression representing the output signal of the matched filter; This represents the equalization coefficient of the frequency domain equalizer; Indicates whitening noise , denoted as: ； in, for Obtained through Discrete Fourier Transform; The correlation coefficient, l Let c(0,0) be the sign of the correlation coefficient in the time domain. l The approximation is {1, 0.447, 0.028, 8.4×10⁻⁵, 0, …, 8.4×10⁻⁵, 0.028, 0.447}. During iterative detection, only c(0,0;) is considered. l The first three items and the last two items in the list; k This refers to the symbol number in the correlation coefficient in the frequency domain; Equilibrium Coefficient Represented as: ； in, This represents the estimated channel frequency domain response; Represents the noise power spectrum; right Doing the anti-Fourier Achieving equilibrium information .

7. The GMSK underwater acoustic communication method based on frequency domain equalization according to claim 6, characterized in that, Steps 2-5 specifically refer to: Mapped information sequence Represented as: ； For sequence Perform separation, define , ; Set information after separation and After energy normalization, it follows a Gaussian distribution with a mean of 1: ； ； in, This defines the probability distribution function; in the calculation hour In calculation hour ; and Based on the cyclic prefix and The obtained variance; ； ； in, and They represent and The cyclic prefix part.

8. The GMSK underwater acoustic communication method based on frequency domain equalization according to claim 7, characterized in that, Steps 2-6 specifically refer to: To perform joint probability calculations over the logarithmic field, we define the probability distribution function: ； log-likelihood ratio Represented as: ； The log-likelihood ratio of the estimated symbol is obtained by calculating the joint probability. : ； in, and Representing sequences respectively and sequence Energy; This indicates the influence of the two symbols before and after the current moment on the current symbol. During iterative calculation, the corresponding The probability is obtained from the log-likelihood ratio. .

9. The GMSK underwater acoustic communication method based on frequency domain equalization according to claim 8, characterized in that, Steps 2-7 specifically refer to: The log-likelihood ratio obtained after iterative detection Obtain the estimated symbol by making a hard decision. Differential decoding can be used to obtain the estimated received symbol. : 。 10. The GMSK underwater acoustic communication method based on frequency domain equalization according to claim 9, characterized in that, The iteration method is as follows: during the first equalization of the received signal, after obtaining frequency domain equalization... In subsequent iterations, the results obtained from the previous iteration are used. replace and combined Estimate the probability values ​​of symbols at different times, and then calculate the joint probability to obtain the log-likelihood ratio of the estimated symbols. ; The specific steps of iterative calculation include: a) The result of the previous iteration Perform energy normalization and replace The value; b) Calculate the probability value after replacement. ; c) Calculate based on probability values: .

11. A GMSK underwater acoustic communication system based on frequency domain equalization, implemented according to the method of any one of claims 1-10, characterized in that, The system includes: The signal transmission module is used to add a cyclic prefix to the beginning of the transmitted signal sequence and a tail symbol to the end of the transmitted signal sequence, perform conversion and information-symbol mapping, and then modulate the signal according to the GMSK signal modulation method based on Laurent decomposition to obtain the GMSK complex baseband signal. The GMSK complex baseband signal is then modulated onto the carrier as the final transmitted signal and transmitted to the underwater acoustic channel. The signal receiving module is used to receive transmitted signals in the underwater acoustic channel. It performs bandpass filtering, time-frequency two-dimensional synchronization, matched filtering, frequency domain equalization, and iterative symbol detection on the received signals to obtain the decided symbols and complete GMSK underwater acoustic communication based on frequency domain equalization.

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