A method and system for underwater acoustic communication based on GMSK time domain iterative equalization

Abstract

The application provides a GMSK-based time domain iterative equalization underwater acoustic communication method and system, the method comprises the following steps: adding a training sequence before a transmitting signal sequence, converting 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 transmitting signal, and transmitting the final transmitting signal to an underwater acoustic channel; receiving the transmitting signal in the underwater acoustic channel, carrying out band-pass filtering, time-frequency two-dimensional synchronization, matched filtering, time domain adaptive equalization and symbol detection on the received signal, obtaining a judged symbol, and completing GMSK underwater acoustic communication based on time domain iterative equalization. The application has the advantages that: the complexity of symbol detection of a conventional frequency domain equalizer is reduced, and the detection performance is improved; the ability of adaptive equalization to resist a complex multipath channel is enhanced, and the communication performance is improved.

Description

Technical Field

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

[0002] Underwater acoustic communication channels are highly complex, characterized by frequency-selective fading, limited bandwidth, and time-varying characteristics. 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 cannot track channel variations and suffer performance loss in time-varying channels. Time-domain adaptive equalization can effectively track channel variations through symbol-by-symbol iteration and phase-locked loops, but time-domain equalization for GMSK still has some shortcomings: (1) Symbol detection based on the Viterbi algorithm has extremely high complexity, which increases exponentially with the signal length, making it difficult to use in equalizer design. (2) Although the simplified symbol detection algorithm reduces the complexity, it does not utilize the inherent coding gain of the GMSK signal and still suffers from significant performance loss. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing underwater acoustic communication algorithms, whose complexity increases exponentially with signal length, making them difficult to use in equalizer design.

[0005] To achieve the above objectives, this invention proposes a time-domain iterative equalization underwater acoustic communication method based on GMSK, the method comprising: Step 1: Add the training sequence before 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, time-domain adaptive equalization, and symbol detection on the received signal to obtain the decided symbol, thus completing GMSK underwater acoustic communication based on time-domain iterative equalization.

[0006] As an improvement to the above method, step 1 specifically includes: Step 1-1: Construct the transmitted signal frame; A transmit signal frame is constructed, comprising a training sequence, a sequence of information to be transmitted, and a tail symbol sequence; wherein the phase state of the training sequence starts 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 transmission sequence consisting of the training sequence and the information sequence to be transmitted { a k The transformation yields a bipolar non-return-to-zero sequence. The information-symbol mapping is converted into a GMSK complex baseband signal sequence. b n}:

[0007] 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 complex baseband signal; Based on the Laurent decomposition of the GMSK signal, a phase shaping function is used to analyze the complex baseband GMSK signal sequence. Modulation was performed to obtain the GMSK complex baseband signal. :

[0008] in, t Indicates time; T Indicates the symbol period; For partial impulse response:

[0009]

[0010]

[0011] in, L Indicates the pulse memory length. Indicates frequency shaping pulse The integral function; Represents the independent variable of the integrand;

[0012] in, B Indicates bandwidth; Representing the complementary error function:

[0013]

[0014] in, Represents the independent variable of the integrand; 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; Final transmission signal Represented as:

[0015] in, fc The center frequency of the carrier wave.

[0016] As an improvement to the above method, step 2 specifically includes: Step 2-1: Receive the signal Bandpass filtering is performed, and time-frequency two-dimensional synchronization is achieved using a synchronization sequence; 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: Equalize the complex baseband signal using an adaptive decision feedback equalizer; Steps 2-4: Combining the GMSK's built-in encoding method, use the symbol detector to perform joint probability estimation to obtain the log-likelihood ratio of the equalizer output; Steps 2-5: Based on the calculated log-likelihood ratio, make a decision, solve the difference to obtain the received symbol, and use the received symbol and the corresponding prior information to perform symbol mapping to obtain the input of the feedback filter.

[0017] As an improvement to the above method, step 2-1 specifically includes: Received signal Represented as:

[0018] in, Indicates the channel response; Indicates acceptance of noise. This indicates the final transmitted signal. The subscript represents the integral. .

[0019] As an improvement to the above method, step 2-2 specifically includes:

[0020] in, n Indicates the number of the output symbol. The signal output by the matched filter:

[0021] This represents the noise output of the matched filter: .

[0022] As an improvement to the above method, steps 2-3 are specifically as follows: Before equalization, the complex baseband signal is first processed. Perform mapping to obtain the mapped signal. :

[0023] complex baseband signal The input is fed into an adaptive decision feedback equalizer to obtain the time-domain equalized soft information. :

[0024] in, and These represent the order of the feedforward filter and the order of the feedback filter, respectively. Represents the input sequence of the feedforward filter elements, Indicates the equalizer coefficient; Represents the input sequence of the feedback filter Element; The phase offset estimate is represented by the following formula;

[0025] in, K 1 , K 2 For different phase-locked loop coefficients; For updated phase offset estimation; For intermediate quantities in the calculation;

[0026] in, This is the output symbol of the feedforward filter; This represents the prior estimation error; This indicates conjugate computation.

[0027] As an improvement to the above method, steps 2-4 are specifically as follows: For equalized soft information Perform separation, define , ; Set information after separation and Follows a Gaussian distribution:

[0028]

[0029] in, To define the probability distribution function; in calculating hour In calculation hour ; and Based on partial training sequences and The obtained variance:

[0030]

[0031] in, and They represent and The training sequence portion; To perform joint probability calculations over the logarithmic field, we define the probability distribution function:

[0032] log-likelihood ratio Represented as:

[0033] The log-likelihood ratio of the estimated symbol is obtained by calculating the joint probability. :

[0034] in, and Representing sequences respectively and sequence The energy; at equilibrium, the probability value for n≤0 is obtained from the soft information of the decided symbols, and the probability value for n>0 is obtained from the soft information obtained in the previous iteration. Make an estimate; 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. .

[0035] As an improvement to the above method, steps 2-5 are specifically as follows: 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. :

[0036] Calculate the input of the feedback filter At that time, first place the already judged symbol Mapped to :

[0037] in, yes The estimated value; yes The estimated value:

[0038] .

[0039] As an improvement to the above method, the iteration is performed as follows: during the first equalization of the received signal, the time-domain equalized soft information is obtained. In subsequent iterations, utilizing Estimate and map the probability values ​​of symbols at different times; that is, calculate the symbol-by-symbol equalization in the second iteration. n The time symbol required n Information after a certain time is provided by Estimated; The iterative calculation steps specifically include: a) Output soft information of the decision feedback equalizer ; b) Utilizing soft information Calculate the probability value , , and ; c) Calculate based on probability values: , and .

[0040] The present invention also provides a time-domain iterative equalization underwater acoustic communication system based on GMSK, the system comprising: The signal transmission module is used to add the training sequence before the transmission signal sequence, perform conversion and information-symbol mapping, and then modulate it according to the GMSK signal modulation method based on Laurent decomposition to obtain the GMSK complex baseband signal. The GMSK complex baseband signal is modulated onto the carrier as the final transmission 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, time-domain adaptive equalization, and symbol detection on the received signals to obtain the decided symbols and complete GMSK underwater acoustic communication based on time-domain iterative equalization.

[0041] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any of the preceding claims.

[0042] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the method described in any of the preceding claims.

[0043] Compared with the prior art, the advantages of the present invention are: 1. In the adaptive 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.

[0044] 2. Compared to simplified symbol detectors, posterior probability-based symbol detectors effectively utilize the encoding gain inherent in GMSK, improving detection performance with a slight increase in complexity.

[0045] 3. The coding gain obtained by the detector is fed back to the adaptive equalizer, and the equalization coefficient is updated multiple times, which enhances the ability of the adaptive equalization to combat complex multipath channels and improves communication performance. Attached Figure Description

[0046] Figure 1 The diagram shows the data block structure for transmitting signals in underwater acoustic communication. Figure 2 The diagram shows the underwater acoustic signal transceiver structure of an underwater acoustic communication method. Figure 3 The diagram shows a signal equalization detection process in an example of the underwater acoustic signal transmission method according to the present invention. Figure 4 The image shows the time-domain waveform of a GMSK underwater acoustic signal after bandpass filtering, in an example of the underwater acoustic signal transmission method according to the present invention. Figure 5 The image shows a measured channel in a marine underwater acoustic communication experiment, as an example of the underwater acoustic signal transmission method according to the present invention. Figure 6 The image shows a time-domain waveform of the received signal after bandpass filtering, as shown in an example of the underwater acoustic signal transmission method according to the present invention. Figure 7 The diagram shown is a constellation diagram of the received signal after matched filtering in an example of the underwater acoustic signal transmission method according to the present invention. Figure 8 The diagram shown is a constellation diagram of the soft information output by the detector after the initial detection in an example of the underwater acoustic signal transmission method according to the present invention. Figure 9 The diagram shown is a constellation diagram of the soft information output by the detector after two iterations of detection in an example of the underwater acoustic signal transmission method according to the present invention. Detailed Implementation

[0047] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0048] To address the inter-symbol interference problem in GMSK underwater acoustic communication caused by time-varying underwater acoustic channels, this invention proposes a time-domain iterative equalization technique for GMSK underwater acoustic communication. Based on Laurent decomposition, a time-domain adaptive decision feedback filter is used to equalize the received signal, and a posterior probability-based detection algorithm is used for symbol detection. The joint probability output by the detector is fed back to the equalizer for iterative equalization.

[0049] This algorithm effectively utilizes the coding gain of GMSK and updates the equalizer coefficients multiple times, significantly improving the transmission performance of GMSK signals in underwater acoustic time-varying channels, while having lower complexity compared to Viterbi symbol detection.

[0050] The present invention discloses a time-domain iterative equalization underwater acoustic communication method based on GMSK, comprising the following steps: The training sequence is added before the transmitted signal sequence for conversion and information-symbol mapping. Then, it is modulated according to the GMSK signal modulation method based on Laurent decomposition to obtain the GMSK complex baseband signal. This signal is then modulated onto the carrier as the final transmitted signal and transmitted to the underwater acoustic channel. The underwater acoustic channel receives the final transmitted signal and uses it as the received signal. The received signal is then subjected to bandpass filtering, two-dimensional time-frequency synchronization, matched filtering, time-domain adaptive equalization, and symbol detection to obtain the decided symbol, thus completing GMSK underwater acoustic communication based on time-domain iterative equalization.

[0051] The signal transmission method of the present invention, which is based on GMSK time-domain iterative equalization underwater acoustic communication method, includes the following steps: 1) Parameter Design The parameters involved in this invention include data block length. N Information sequence length L Training sequence length G .

[0052] 2) Signal frame structure design The transmission frame structure includes a training sequence, a sequence of information to be transmitted, and a tail symbol sequence. The training sequence is used at the detection end to initialize the equalizer coefficients and the detector's equivalent variance. To facilitate subsequent signal detection, the phase state of the training sequence must start and end at zero. The tail symbol is used to ensure that the phase state of the information sequence to be transmitted is zero.

[0053] 3) Information-symbol mapping For the transmission sequence consisting of the training sequence and the information sequence to be transmitted { a k The transformation yields a bipolar non-return-to-zero sequence. .

[0054] GMSK complex baseband signal sequence { b n} can be represented as: (1) in, GMSK complex baseband signal sequence Elements in; A bipolar non-return-to-zero code signal sequence Elements in; j It is an imaginary number; 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 the GMSK complex baseband signal. , (2) in, t Indicates time, T Indicates the symbol period, This is a partial impulse response.

[0055] And modulate it onto a carrier wave as the final transmitted signal. Transmitted into the underwater acoustic channel; (3) in, j It is an imaginary number. fc The center frequency of the carrier wave; make ;but ; (4) (5) (6) in, T For symbol intervals, L Pulse memory length, Frequency shaping pulse The integral function; This represents the independent variable of the integrand.

[0056] (7) in, B Configure bandwidth BT =0.3, L =3; (8) in, or ; For complementary error functions, This represents the independent variable of the integrand.

[0057] The signal receiving method of the time-domain iterative equalization underwater acoustic communication method based on GMSK of the present invention includes the following steps: The final transmitted signal is received by a hydrophone through an underwater acoustic channel. The receiver performs bandpass filtering, two-dimensional time-frequency synchronization, matched filtering, and time-domain iterative equalization on the received signal to obtain the decided symbol, thus completing the time-domain iterative equalization of GMSK underwater acoustic communication. Received signal It can be represented as: (9) in, For channel response; To accept noise, For the final transmitted signal, The subscript representing the integral; .

[0058] Its specific process includes: 1) Regarding the received signal Bandpass filtering is performed, and time-frequency two-dimensional synchronization is achieved using a synchronization sequence: 2) Use a low-pass filter and a coherent receiver to process the received signal. Perform matched filtering to obtain the complex baseband signal. The coherent receiver is used to match the c(t) pulse.

[0059] After passing through a low-pass filter, the signal is obtained. : (10) For the received signal Perform matched filtering to obtain the complex baseband signal. : (11) in, The signal output by the matched filter, n Indicates the number of the output symbol. The noise output of the matched filter; (12) (13) 3) Use an adaptive decision feedback equalizer to equalize the complex baseband signal.

[0060] Before equalization, the complex baseband signal is first processed. Perform mapping to obtain the mapped signal. : (14) complex baseband signal The input is fed into an adaptive decision feedback equalizer to obtain the time-domain equalized soft information. , (15) in, and These are the orders of the feedforward filter and the feedback filter, respectively; For sequence The elements, the feedforward filter input, For phase offset estimation; Equalizer coefficients; For sequence The elements are used as inputs to the feedback filter; (16) in, K 1 , K 2 For different phase-locked loop coefficients; For updated phase offset estimation; For intermediate quantities in the calculation; (17) in, This is the output symbol of the feedforward filter; This represents the prior estimation error; j It is an imaginary number; This indicates conjugate computation.

[0061] 4) Combining the GMSK's built-in encoding method, a symbol detector is used to perform joint probability estimation to obtain the log-likelihood ratio of the equalizer output.

[0062] For equalized soft information Perform separation, define , and calculate log-likelihood ratio : (18) Among them, it is assumed that the information after separation and It follows a Gaussian distribution, that is... (19) (20) in, To define the probability distribution function; in calculating hour In calculation hour . and Based on partial training sequences and The obtained variance. and They represent and The training sequence portion.

[0063] (twenty one) (twenty two) 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: (twenty three) The log-likelihood ratio of the estimated symbol can be obtained by calculating the joint probability. : (twenty four) Among them, κ is related to the energy and variance of the output prior information. , and Representing sequences respectively and sequence The energy. At equilibrium, the probability value for n≤0 can be obtained from the soft information of the decided symbols, and the probability value for n>0 can be obtained from the soft information obtained in the previous iteration. Make an estimate.

[0064] 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... (25) in, During iterative calculation, the corresponding The probability can be obtained from the log-likelihood ratio. .

[0065] 5) Based on the solved log-likelihood ratio, the decision and solution difference are used to obtain the received information, and the received message and the corresponding prior information are used to perform symbol mapping to obtain the input of the feedback filter.

[0066] 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. : (26) Calculate the input of the feedback filter At that time, first place the already judged symbol Mapped to , (27) Calculate using the soft information obtained from the previous iteration and the already decided information : (28) in, yes The estimated value; yes The estimated value: (29) (30) The iterative method is as follows: during the first equalization of the received signal, obtain the soft information after time-domain equalization. In subsequent iterations, utilizing Estimate and map the probability values ​​of symbols at different times; that is, calculate the symbol-by-symbol equalization in the second iteration. n The time symbol required n Information after a certain time is provided by Estimated; The specific steps of iterative calculation include: a) Output soft information of the decision feedback equalizer ; b) Utilizing soft information Calculate the probability value , , and ; c) Calculate based on probability values: , and .

[0067] This invention studies an equalization method for GMSK underwater acoustic communication. Compared with existing technologies, this invention has the following advantages in communication under time-varying underwater acoustic multipath channels: In Laurent decomposition-based adaptive equalization, a designed 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.

[0068] Compared to simplified symbol detectors, posterior probability-based symbol detectors effectively utilize the encoding gain inherent in GMSK, improving detection performance with a slight increase in complexity.

[0069] The coding gain obtained by the detector is fed back to the adaptive equalizer, and the equalization coefficients are updated multiple times, which enhances the ability of the adaptive equalization to combat complex multipath channels and improves communication performance.

[0070] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0071] In this embodiment of the invention, the application of the method of the invention for underwater GMSK signal transmission is discussed. The signal transmission system after matching the hydroacoustic transducer with the power amplifier has a bandwidth of 4-8kHz, a center frequency of 6kHz, and a symbol period of 0.5ms during GMSK modulation, transmitting 1038 bits of original information. The transmission signal parameters are set as shown in the table below: Table 1. Parameter settings for transmitted signals

[0072] Transmitter process reference Figure 2 First, the frame structure is designed by adding a 256-bit training sequence, which consists 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. The time-domain waveform of the modulated transmitted signal is shown below. Figure 4 As shown.

[0073] 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 is obtained by performing matched filtering on the bandpass filtered signal using equation (11). Its constellation chart is as follows Figure 7 As shown. Reference Figure 3 For complex baseband signals Equalization and demodulation are performed on the complex baseband signal. The mapped input is then fed into an adaptive decision feedback filter, and the filter tap coefficients are calculated using the training sequence. The filter output is obtained by separating the real and imaginary parts. Decomposed into and And perform joint probability estimation. In the initial estimation, symbol-by-symbol pair... The log-likelihood ratio is obtained by estimation. Then use Calculate the probability values ​​for other state representations (set the values ​​to 0 for states that cannot be calculated), and then perform joint probability estimation to obtain the estimated log-likelihood ratio. ,according to The soft information from the feedback is acquired and mapped, and then input into the feedback filter to update the filter tap coefficients. In subsequent iterations, the soft information is... Substituting the values ​​into the calculation, it becomes clear that the gain obtained after two iterations is significantly reduced due to the limited coding gain contained in the GMSK signal. Figure 8 and Figure 9 These are constellation diagrams of the output signals after iteration 0 and iteration 2, respectively. After iteration... Perform hard decision to obtain the estimated symbol sequence. After two iterations, differential decoding is performed according to equation (26) to obtain the complete output information. A total of 1038 bits are used to complete the entire communication process.

[0074] The present invention also provides a time-domain iterative equalization underwater acoustic communication system based on GMSK, the system comprising: The signal transmission module is used to add the training sequence before the transmission signal sequence, perform conversion and information-symbol mapping, and then modulate it according to the GMSK signal modulation method based on Laurent decomposition to obtain the GMSK complex baseband signal. The GMSK complex baseband signal is modulated onto the carrier as the final transmission 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, time-domain adaptive equalization, and symbol detection on the received signals to obtain the decided symbols and complete GMSK underwater acoustic communication based on time-domain iterative equalization.

[0075] 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.

[0076] The user interface may include a display, keyboard, or clicking device (e.g., mouse, trackball, touchpad, or touchscreen).

[0077] 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 SDRAM (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.

[0078] 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.

[0079] 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.

[0080] 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: Follow the steps described above.

[0081] 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 implemented 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.

[0082] 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.

[0083] For software implementation, the technology of this invention can be implemented 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.

[0084] 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.

[0085] 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 time-domain iterative equalization underwater acoustic communication method based on GMSK, the method comprising: Step 1: Add the training sequence before 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, time-domain adaptive equalization, and symbol detection on the received signal to obtain the decided symbol, thus completing GMSK underwater acoustic communication based on time-domain iterative equalization. Step 1 specifically includes: Step 1-1: Construct the transmitted signal frame; A transmit signal frame is constructed, comprising a training sequence, a sequence of information to be transmitted, and a tail symbol sequence; wherein the phase state of the training sequence starts 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 transmission sequence consisting of the training sequence and the information sequence to be transmitted { a k The transformation yields a bipolar non-return-to-zero sequence. The information-symbol mapping is converted into a GMSK complex baseband signal sequence. 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 complex baseband signal; Based on the Laurent decomposition of the GMSK signal, a phase shaping function is used to analyze the GMSK complex baseband signal sequence. Modulation was performed to obtain the GMSK complex baseband signal. : in, t Indicates time; T Indicates the symbol period; For partial impulse response: in, L Indicates the pulse memory length. Indicates frequency shaping pulse The integral function; Represents the independent variable of the integrand; in, B Indicates bandwidth; Representing the complementary error function: in, Represents the independent variable of the integrand; 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; Final transmission signal Represented as: in, fc The center frequency of the carrier wave.

2. The time-domain iterative equalization underwater acoustic communication method based on GMSK according to claim 1, characterized in that, Step 2 specifically includes: Step 2-1: Receive the signal Bandpass filtering is performed, and time-frequency two-dimensional synchronization is achieved using a synchronization sequence; 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: Equalize the complex baseband signal using an adaptive decision feedback equalizer; Steps 2-4: Combining the GMSK's built-in encoding method, use the symbol detector to perform joint probability estimation to obtain the log-likelihood ratio of the equalizer output; Steps 2-5: Based on the calculated log-likelihood ratio, make a decision, solve the difference to obtain the received symbol, and use the received symbol and the corresponding prior information to perform symbol mapping to obtain the input of the feedback filter.

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

4. The time-domain iterative equalization underwater acoustic communication method based on GMSK according to claim 3, characterized in that, Step 2-2 specifically involves: 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 time-domain iterative equalization underwater acoustic communication method based on GMSK according to claim 4, characterized in that, Steps 2-3 specifically refer to: Before equalization, the complex baseband signal is first processed. Perform mapping to obtain the mapped signal. : complex baseband signal The input is fed into an adaptive decision feedback equalizer to obtain the time-domain equalized soft information. : in, and These represent the order of the feedforward filter and the order of the feedback filter, respectively. Represents the input sequence of the feedforward filter elements, Indicates the equalizer coefficient; Represents the input sequence of the feedback filter Element; The phase offset estimate is represented by the following formula; in, K 1 , K 2 For different phase-locked loop coefficients; For updated phase offset estimation; For intermediate quantities in the calculation; in, This is the output symbol of the feedforward filter; This represents the prior estimation error; This indicates conjugate computation.

6. A time-domain iterative equalization underwater acoustic communication system based on GMSK, used to implement the method of any one of claims 1-5, the system comprising: The signal transmission module is used to add the training sequence before the transmission signal sequence, perform conversion and information-symbol mapping, and then modulate it according to the GMSK signal modulation method based on Laurent decomposition to obtain the GMSK complex baseband signal. The GMSK complex baseband signal is modulated onto the carrier as the final transmission 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, time-domain adaptive equalization, and symbol detection on the received signals to obtain the decided symbols and complete GMSK underwater acoustic communication based on time-domain iterative equalization.

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