CN117792848A - GMSK-based time domain iterative equalization underwater acoustic communication method and system - Google Patents

Abstract

The invention provides a GMSK-based time domain iterative equalization underwater acoustic communication method and a system, wherein the method comprises the following steps: before a training sequence is added to a transmitting signal sequence, conversion and information-symbol mapping are carried out, modulation is carried out according to a GMSK signal modulation mode based on Laurent decomposition, a GMSK complex baseband signal is obtained, the GMSK complex baseband signal is modulated on a carrier wave to be used as a final transmitting signal, and the final transmitting signal is transmitted into an underwater sound channel; the underwater acoustic channel receives the transmitting signal, carries out band-pass filtering, time-frequency two-dimensional synchronization, matched filtering, time-domain self-adaptive equalization and symbol detection on the received signal to obtain a judged symbol, and completes GMSK underwater acoustic communication based on time-domain iterative equalization. The invention has the advantages that: the complexity of symbol detection of a conventional frequency domain equalization equalizer is reduced, and the detection performance is improved; the adaptive equalization capability against complex multipath channels is enhanced, and the communication performance is improved.

Description

GMSK-based time domain iterative equalization underwater acoustic communication method and system

Technical Field

The invention belongs to the field of underwater acoustic communication, and particularly relates to a GMSK-based time domain iterative equalization underwater acoustic communication method and system.

Background

The underwater acoustic communication channel is quite complex and has the characteristics of frequency selective fading, limited bandwidth, time variation and the like. Gaussian minimum shift keying modulation (GMSK) has good power utilization rate and spectrum utilization rate due to the characteristics of continuous phase and constant envelope, and can effectively improve the effectiveness and reliability of a communication system. However, the channel structure in the underwater acoustic communication is very complex, so that strong intersymbol interference occurs in the received signal, and therefore, the received signal needs to be processed by using a proper equalization technology.

The traditional frequency domain equalization technology can effectively solve the interference of multipath channels, but cannot track the change of the channels, and has performance loss under the time-varying channels. Time-domain adaptive equalization can effectively track channel variation through symbol-by-symbol iteration and phase-locked loop, but there are still some disadvantages to time-domain equalization for GMSK:

(1) Symbol detection based on the Viterbi algorithm has extremely high complexity, and the complexity increases exponentially with the signal length, so that the symbol detection is difficult to be used in equalizer design;

(2) The simplified symbol detection algorithm, while reducing complexity, does not take advantage of the coding gain inherent to GMSK signals, there is still a significant performance penalty.

Disclosure of Invention

The invention aims to overcome the defect that the complexity of the existing algorithm of underwater acoustic communication increases exponentially with the length of a signal and is difficult to be used in equalizer design.

In order to achieve the above object, the present invention provides a time domain iterative equalization underwater acoustic communication method based on GMSK, which includes:

step 1: before a training sequence is added to a transmitting signal sequence, conversion and information-symbol mapping are carried out, modulation is carried out according to a GMSK signal modulation mode based on Laurent decomposition, a GMSK complex baseband signal is obtained, the GMSK complex baseband signal is modulated on a carrier wave to be used as a final transmitting signal, and the final transmitting signal is transmitted into an underwater sound channel;

step 2: the underwater acoustic channel receives the transmitting signal, carries out band-pass filtering, time-frequency two-dimensional synchronization, matched filtering, time-domain self-adaptive equalization and symbol detection on the received signal to obtain a judged symbol, and completes GMSK underwater acoustic communication based on time-domain iterative equalization.

As an improvement of the above method, the step 1 specifically includes:

step 1-1: constructing a transmission signal frame;

constructing a transmitting signal frame, wherein the structure comprises a training sequence, an information sequence to be transmitted and a tail symbol sequence; wherein the phase state of the training sequence is satisfied beginning with a zero state and ending with a zero state; the tail symbol is used for ensuring that the phase state of the information sequence to be transmitted returns to zero;

step 1-2: converting and information-symbol mapping the transmitted signal;

for a transmission sequence { a } consisting of a training sequence and an information sequence to be transmitted _k Conversion is carried out to obtain a bipolar non-return-to-zero sequence { x }, which is a sequence of _k Information-symbol mapped to GMSK complex baseband signal sequence { b } _n }：

Wherein b _n Representing GMSK complex baseband signal sequence { b } _n Elements in }; x is x _k Representing bipolar non-return-to-zero code signal sequence { x } _k Elements in }; n represents the data block length; j represents an imaginary number;

step 1-3: modulating a GMSK signal modulation mode based on Laurent decomposition to obtain a GMSK complex baseband signal;

for the complex baseband GMSK signal sequence { b } using a phase shaping function based on Laurent decomposition of the GMSK signal _n Modulating to obtain GMSK complex baseband signal s (t):

wherein t represents time; t represents a symbol period; c (t-nT) is the partial impulse response:

wherein L represents the pulse memorization length, q (t) represents the integral function of the frequency shaping pulse g (τ); τ represents the argument of the integrated function;

wherein B represents bandwidth; q () represents a complementary error function:

where ε represents the argument of the integrated function;

step 1-4: modulating the GMSK complex baseband signal onto a carrier wave to serve as a final transmitting signal, and transmitting the final transmitting signal into an underwater sound channel;

final transmitted signal s ^f (t) is expressed as:

s ^f (t)＝s(t)×exp(j×2π×fc×t)

where fc is the center frequency of the carrier.

As an improvement of the above method, the step 2 specifically includes:

step 2-1: for the received signal r ^f (t) band-pass filtering, and performing time-frequency two-dimensional synchronization by using a synchronization sequence;

step 2-2: receiving a signal r by means of a low-pass filter and a coherent receiver ^f (t) performing matched filtering to obtain a complex baseband signal r _n ；

Step 2-3: equalizing the complex baseband signal by using an adaptive decision feedback equalizer;

step 2-4: combining a GMSK self-carried coding mode, and carrying out joint probability estimation by using a symbol detector to obtain a log-likelihood ratio output by an equalizer;

step 2-5: and according to the solved log-likelihood ratio, judging and solving the difference to obtain a received symbol, and carrying out symbol mapping by utilizing the received symbol and corresponding prior information to obtain the input of the feedback filter.

As an improvement of the above method, the step 2-1 specifically comprises:

received signal r ^f (t) is expressed as:

wherein h is _η Representing the channel response; z (t) represents the received noise, s ^f () Representing the final transmit signal, η represents the subscript of the integral, η=0, 1, 2.

As an improvement of the above method, the step 2-2 specifically comprises:

wherein n represents the number of the output symbol, s _n For the signal output by the matched filter:

z _n noise representing the matched filter output:

as an improvement of the above method, the step 2-3 specifically comprises:

for complex baseband signal r before equalization _n Mapping to obtain mapped signals

Will complex baseband signal r _n Input to adaptive decision feedback equalizer to obtain soft information after time domain equalization

Wherein K is _F And K _B Respectively representing the feedforward filter order and the feedback filter order;representing the feed forward filter input sequence +.>Lambda of the element (a) _j Representing equalizer coefficients; y is _n-j Representing feedback filter input sequence y _n Elements of }; />Representing a phase offset estimate, represented by;

wherein K is ₁ 、K ₂ Different phase-locked loop coefficients;estimating for the updated phase offset; phi _n Is the calculated intermediate quantity;

wherein,outputting a symbol for the feedforward filter; />Estimating error for a priori; [] ^* Representing conjugate calculations.

As an improvement of the above method, the steps 2-4 specifically include:

for balanced soft informationIsolation, definition->

Setting the separated information u _n And v _n Obeys a gaussian distribution:

wherein Pr () is a defined probability distribution function; in the calculation of Pr (u) _n ζ ε { + -1 }, in calculating Pr (v) _n =ζ), ζ∈ {0, ±2/pi }; sigma (sigma) _u Sum sigma _v Based on part of the training sequenceAnd->The obtained variance:

wherein,and->Respectively represent u _n And v _n Is a training sequence part of (1);

performing joint probability calculation on the logarithmic domain, and defining a probability distribution function:

u _n log-likelihood ratio L (u) _n ) Expressed as:

obtaining log-likelihood ratio of estimated symbol by calculating joint probability

Wherein E (u) _n ) And E (v) _n ) Respectively representing the sequences { u }, respectively _n Sum sequence { v } _n Energy of }; at the time of equalization, the probability value for n.ltoreq.0 is obtained from the soft information of the decided symbol, and the probability value for n > 0 is obtained from the soft information of the last iterationEstimating; a is that _n Representing the influence of two symbols before and after the current moment on the current symbol, A _n ＝c(0,0；3)×(Pr(u _n-2 ＝1)+Pr(u _n+2 =1) -1), corresponding to u at the time of iterative calculation _n The probability of (2) is obtained from the log-likelihood ratio,

as an improvement of the above method, the steps 2-5 specifically include:

log likelihood ratio obtained after iterative detectionMaking hard decisions to obtain estimated symbols->Estimated received symbols +.>

Calculating the input y of the feedback filter _n When the symbol is judged, the judged symbol is firstly judgedMapping to p _n ：

Wherein,is p _n+1 Is a function of the estimated value of (2); />Is p _n+2 Is a function of the estimated value of (a):

as an improvement of the above method, the iterative manner is: when the received signals are equalized for the first time, soft information after time domain equalization is obtainedIn subsequent iterations, use +.>Estimating probability values of symbols at different times and mapping, i.e. in symbol-by-symbol equalization of the second iteration, information after n time needed for calculating the symbol at n time is calculated by ∈ ->Obtaining estimation;

the iterative computation specifically comprises the following steps:

a) Output soft information of decision feedback equalizer

b) Using soft informationCalculating probability value Pr (u) _n+1 ＝ζ)、Pr(v _n+1 ＝ξ)、Pr(u _n+2 ζ) =ζ)

Pr(v _n+2 ＝ξ)；

c) Calculating according to the probability value:and->

The invention also provides a time domain iterative equalization underwater acoustic communication system based on GMSK, which comprises:

the signal transmitting module is used for adding a training sequence before a transmitting signal sequence, converting and mapping information and symbols, modulating according to a GMSK signal modulation mode based on Laurent decomposition to obtain a GMSK complex baseband signal, modulating the GMSK complex baseband signal onto a carrier wave to serve as a final transmitting signal, and transmitting the final transmitting signal into an underwater sound channel;

the signal receiving module is used for receiving the transmitting signal through the underwater acoustic channel, carrying out band-pass filtering, time-frequency two-dimensional synchronization, matched filtering, time domain self-adaptive equalization and symbol detection on the received signal to obtain a judged symbol, and completing GMSK underwater acoustic communication based on time domain iterative equalization.

The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a method as claimed in any one of the preceding claims when executing the computer program.

The invention also provides a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform a method as claimed in any one of the preceding claims.

Compared with the prior art, the invention has the advantages that:

1. in adaptive equalization based on Laurent decomposition, the designed iterative detector based on posterior probability is used for replacing Viterbi decoding, so that the complexity of symbol detection of a conventional frequency domain equalization equalizer is greatly reduced.

2. Compared with a simplified symbol detector, the symbol detector based on the posterior probability effectively utilizes the coding gain of GMSK under the condition of slightly increasing the complexity, and improves the detection performance.

3. The coding gain obtained by the detector is fed back to the adaptive equalizer, and the equalization coefficient is updated for a plurality of times, so that the capacity of adaptive equalization against complex multipath channels is enhanced, and the communication performance is improved.

Drawings

FIG. 1 shows a data block structure of a transmission signal in an underwater acoustic communication method;

FIG. 2 shows an underwater sound signal receiving and transmitting structure of an underwater sound communication method;

FIG. 3 shows a signal equalization detection flow in one example of a hydroacoustic signal transmission method according to the present invention;

fig. 4 shows a time domain waveform of GMSK underwater acoustic signals after band-pass filtering in an example of an underwater acoustic signal transmission method according to the present invention;

FIG. 5 illustrates measured channels in an offshore acoustic communication test in one example of a method of transmitting an acoustic signal in accordance with the present invention;

FIG. 6 illustrates a time domain waveform after bandpass filtering of a received signal in one example of a method of hydroacoustic signal transmission according to the present invention;

FIG. 7 is a constellation diagram of a matched filtered received signal in one example of a method of underwater acoustic signal transmission in accordance with the present invention;

FIG. 8 is a constellation diagram of soft information output by a detector after initial detection in one example of a method of hydroacoustic signal transmission according to the present invention;

fig. 9 shows a constellation diagram of soft information output by the detector after 2 iterative detections in an example of the method for transmitting an underwater acoustic signal according to the present invention.

Detailed Description

The technical scheme of the invention is described in detail below with reference to the accompanying drawings.

The invention provides a time domain iterative equalization technology for GMSK underwater acoustic communication, which aims to solve the problem of intersymbol interference of a time-varying underwater acoustic channel to the GMSK underwater acoustic communication. On the basis of Laurent decomposition, a time domain self-adaptive decision feedback filter is utilized to perform equalization processing on a received signal, a detection algorithm based on posterior probability is utilized to perform symbol detection, and joint probability output by a detector is fed back to an equalizer to perform iterative equalization.

The algorithm effectively utilizes the coding gain of GMSK and updates the equalizer coefficient for a plurality of times, greatly improves the transmission performance of the GMSK signal under the underwater acoustic time-varying channel, and has lower complexity compared with Viterbi symbol detection.

The invention discloses a GMSK-based time domain iterative equalization underwater acoustic communication method which comprises the following steps of:

before a training sequence is added to a transmitting signal sequence, conversion and information-symbol mapping are carried out, modulation is carried out according to a GMSK signal modulation mode based on Laurent decomposition, a GMSK complex baseband signal is obtained, and the GMSK complex baseband signal is modulated on a carrier wave to be used as a final transmitting signal and transmitted into an underwater sound channel;

the underwater sound channel receives the final transmitting signal, takes the final transmitting signal as a receiving signal, carries out band-pass filtering, time-frequency two-dimensional synchronization, matched filtering, time domain self-adaptive equalization and symbol detection on the receiving signal to obtain a symbol after judgment, and completes GMSK underwater sound communication based on time domain iterative equalization.

The invention discloses a signal transmission method of a time domain iterative equalization underwater acoustic communication method based on GMSK, which comprises the following steps:

1) Parameter design

The parameters related to the invention comprise a data block length N, an information sequence length L and a training sequence length G.

2) Signal frame structure design

The transmit frame structure includes a training sequence, an information sequence to be transmitted, and a tail symbol sequence. The training sequence is used for initializing the equivalent variances of the equalizer coefficient and the detector at the detection end, and the phase state of the training sequence needs to be satisfied from the zero state and ended from the zero state in order to facilitate the subsequent signal detection. The tail symbols are used for ensuring that the phase state of the information sequence to be transmitted returns to zero.

3) Information-symbol mapping

For a transmission sequence { a } consisting of a training sequence and an information sequence to be transmitted _k Conversion is carried out to obtain a bipolar non-return-to-zero sequence { x }, which is a sequence of _k }。

GMSK complex baseband signal sequence { b } _n The } can be expressed as:

wherein b _n Is an element in the GMSK complex baseband signal sequence { bn }; x is x _k For bipolar non-return-to-zero code signal sequence { x } _k Elements in }; j is an imaginary number;

based on Laurent decomposition of GMSK signal, a phase shaping function is used to sequence { b } the GMSK signal _n Modulating to obtain GMSK complex baseband signal s (t),

where T represents time, T represents symbol period, and c (T-nT) is a partial impulse response.

And modulates it onto a carrier wave as a final transmitted signal s ^f (t) transmitting into the underwater acoustic channel;

s ^f (t)＝s(t)×exp(j×2π×fc×t) (3)

where j is an imaginary number and fc is the center frequency of the carrier;

let t ¹ =t-nT; then c (t) ¹ )＝c(t-nT)；

Wherein T is the symbol interval, L pulse memory length, q (T) is the integral function of the frequency shaping pulse g (tau); τ represents the argument of the integrated function.

Wherein B is bandwidth, bt=0.3 is set, l=3;

wherein,or->Q(x ¹ ) Epsilon represents the argument of the integrated function, which is the complementary error function.

The invention discloses a signal receiving method of a time domain iterative equalization underwater acoustic communication method based on GMSK, which comprises the following steps:

the resulting transmitted signal is received by the hydrophone via the underwater acoustic channel. The receiver carries out band-pass filtering, time-frequency two-dimensional synchronization, matched filtering and time domain iterative equalization on the received signal to obtain a judged symbol, and completes the time domain iterative equalization of GMSK underwater acoustic communication;

received signal r ^f (t) can be expressed as:

wherein h is _η Is the channel response; z (t) is the received noise, s ^f (t) is the final transmit signal, η represents the subscript of the integral; η=0, 1,2,...

The specific process comprises the following steps:

1) For the received signal r ^f (t) band-pass filtering and time-frequency two-dimensional synchronization using a synchronization sequence:

2) Receiving a signal r by means of a low-pass filter and a coherent receiver ^f (t) performing matched filtering to obtain a complex baseband signal r _n . Wherein the coherent receiver is used to match the c (t) pulse.

r ^f After passing through a low pass filter, obtaining a signal r (t):

for the received signal r ^f (t) performing matched filtering to obtain a complex baseband signal r _n ：

Wherein s is _n For matching the signal output by the filter, n represents the number of the output symbol, z _n Noise output by the matched filter;

3) The complex baseband signal is equalized using an adaptive decision feedback equalizer.

For complex baseband signal r before equalization _n Mapping to obtain mapped signals

Will complex baseband signal r _n Input to adaptive decision feedback equalizer to obtain soft information after time domain equalization

Wherein K is _F And K _B The feedforward filter order and the feedback filter order are respectively;for the sequence->Element of (2), feedforward filter input, < +.>Estimating for phase offset; lambda (lambda) _j Equalizer coefficients; y is _n-j For sequence { y } _n -an element, a feedback filter input;

wherein K is ₁ 、K ₂ Different phase-locked loop coefficients;estimating for the updated phase offset; phi _n Is the calculated intermediate quantity;

wherein,outputting a symbol for the feedforward filter; />Estimating error for a priori; j is an imaginary number; [] ^* Representing conjugate calculations.

4) And combining a GMSK self-carried coding mode, and carrying out joint probability estimation by using a symbol detector to obtain the log-likelihood ratio of the equalizer output.

For balanced soft informationIsolation, definition->And calculate u _n Log-likelihood ratio L (u) _n )：

Wherein, the separated information u is assumed _n And v _n Obeying Gaussian distribution, i.e.

Wherein Pr () is a defined probability distribution function; in the calculation of Pr (u) _n ζ ε { + -1 }, calculate Pr (v) _n =ζ) ∈ {0, ±2/pi }. Sigma (sigma) _u Sum sigma _v Based on part of the training sequenceAnd->The variance obtained. />And->Respectively represent u _n And v _n Is used for the training sequence part of the system.

For computational stability, we consider joint probability computation over the logarithmic domain, thus defining a probability distribution functionThen equation (18) may be rewritten as:

the log-likelihood ratio of the estimated symbol can be obtained by calculating the joint probability

Where k is related to the energy and variance of the output a priori information,E(u _n ) And E (v) _n ) Respectively representing the sequences { u }, respectively _n Sum sequence { v } _n Energy of }. At the time of equalization, the probability value for n.ltoreq.0 can be obtained from the soft information of the decided symbol, and the probability value for n > 0 can be obtained from the soft information of the last iteration +.>An estimation is made.

A _n Representing the effect of two symbols before and after the current time on the current symbol, the first term expansion of equation (24) can be obtained,

wherein A is _n ＝c(0,0；3)×(Pr(u _n-2 ＝1)+Pr(u _n+2 =1) -1), corresponding to u at the time of iterative calculation _n The probability of (2) may be obtained from a log-likelihood ratio,

5) And according to the solved log-likelihood ratio, judging and solving the difference to obtain the received information, and carrying out symbol mapping by utilizing the received information and the corresponding prior information to obtain the input of the feedback filter.

Log likelihood ratio obtained after iterative detectionMaking hard decisions to obtain estimated symbols->Estimated received symbols +.>

Calculating the input y of the feedback filter _n When the symbol is judged, the judged symbol is firstly judgedMapping to p _n ，

Calculating y using soft information and decided information obtained from the last iteration _n ：

Wherein,is p _n+1 Is a function of the estimated value of (2); />Is p _n+2 Is a function of the estimated value of (a):

iterative squareThe formula is: when the received signals are equalized for the first time, soft information after time domain equalization is obtainedIn subsequent iterations, use +.>Estimating probability values of symbols at different times and mapping, i.e. in symbol-by-symbol equalization of the second iteration, information after n time needed for calculating the symbol at n time is calculated by ∈ ->Obtaining estimation;

the iterative computation comprises the following steps of;

a) Output soft information of decision feedback equalizer

b) Using soft informationCalculating probability value Pr (u) _n+1 ＝ζ)、Pr(v _n+1 ＝ξ)、Pr(u _n+2 ζ=ζ) and Pr (v) _n+2 ＝ξ)；

c) Calculating according to the probability value:and->/>

Compared with the prior art, the invention has the following beneficial effects that the communication under the time-varying underwater sound multipath channel:

in adaptive equalization based on Laurent decomposition, the designed iterative detector based on posterior probability is used for replacing Viterbi decoding, so that the complexity of symbol detection of a conventional frequency domain equalization equalizer is greatly reduced.

Compared with a simplified symbol detector, the symbol detector based on the posterior probability effectively utilizes the coding gain of GMSK under the condition of slightly increasing the complexity, and improves the detection performance.

The coding gain obtained by the detector is fed back to the adaptive equalizer, and the equalization coefficient is updated for a plurality of times, so that the capacity of adaptive equalization against complex multipath channels is enhanced, and the communication performance is improved.

Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

In an embodiment of the present invention, the use of the method of the present invention for GMSK signal underwater information transmission is discussed. The bandwidth of a signal transmitting system of the underwater acoustic transducer matched with the power amplifier is 4-8kHz, the center frequency is 6kHz, the code element period is set to be 0.5ms when GMSK modulation is carried out, and original information 1038bits is transmitted. The transmit signal parameter settings are shown in the following table:

table 1 parameter settings of the transmitted signals

Transmitting end flow referring to fig. 2, firstly, frame structure design is performed, and a training sequence with length of 256bits is added, wherein the training sequence is composed of special words. The transmitted signal full frame structure is shown in fig. 1. Mapping and modulating are carried out according to Laurent decomposition of GMSK signals, and the time domain waveform of the modulated transmitting signals is shown in figure 4.

The receiving end process refers to fig. 2, time-frequency two-dimensional synchronization is performed by using a synchronization sequence, and band-pass filtering is performed on a received signal, and the filtered time domain waveform is shown in fig. 6. The band-pass filtered signal is matched and filtered through a filter (11), and a complex baseband signal { r } _n And its constellation is shown in figure 7. Reference is made to fig. 3 for complex baseband signals { r } _n Equalization and demodulation. Will complex baseband signal { r _n The mapped signal is input into an adaptive decision feedback filter, and the tap coefficients of the filter are calculated by using a training sequence. Outputting a filter by separating real and imaginary partsIs decomposed into { u } _n Sum { v } _n And joint probability estimation is performed. For initial estimation, symbol-by-symbol pair { u } _n Estimate to obtain log-likelihood ratio L (u) _n ) Then use L (u) _n ) Calculating probability values of other states (the state which cannot be calculated is set to 0), and then carrying out joint probability estimation to obtain estimated log likelihood ratio +.>According to->And acquiring and mapping the fed-back soft information, and inputting the soft information into a feedback filter to update the tap coefficients of the filter. In a subsequent iteration, will +.>Substituting for calculation, the gain obtained after 2 iterations is significantly reduced due to the limited coding gain contained in the GMSK signal, and fig. 8 and 9 are constellation diagrams of the output signal after 0 iterations and 2 iterations, respectively. Post-iteration pair->Making hard decisions to obtain an estimated symbol sequence +.>After 2 iterations, obtaining complete output information after differential decoding according to the formula (26)>And 1038bits in total, and the whole communication process is completed.

The invention also provides a time domain iterative equalization underwater acoustic communication system based on GMSK, which comprises:

the signal transmitting module is used for adding a training sequence before a transmitting signal sequence, converting and mapping information and symbols, modulating according to a GMSK signal modulation mode based on Laurent decomposition to obtain a GMSK complex baseband signal, modulating the GMSK complex baseband signal onto a carrier wave to serve as a final transmitting signal, and transmitting the final transmitting signal into an underwater sound channel;

the signal receiving module is used for receiving the transmitting signal through the underwater acoustic channel, carrying out band-pass filtering, time-frequency two-dimensional synchronization, matched filtering, time domain self-adaptive equalization and symbol detection on the received signal to obtain a judged symbol, and completing GMSK underwater acoustic communication based on time domain iterative equalization.

The present invention may also provide a computer apparatus comprising: at least one processor, memory, at least one network interface, and a user interface. The various components in the device are coupled together by a bus system. It will be appreciated that a bus system is used to enable connected communications between these components. The bus system includes a power bus, a control bus, and a status signal bus in addition to the data bus.

The user interface may include, among other things, a display, a keyboard, or a pointing device (e.g., a mouse, track ball, touch pad, or touch screen, etc.).

It is to be understood that the memory in the embodiments disclosed herein may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (Double Data Rate SDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (SLDRAM), and Direct RAM (DRRAM). The memory described herein is intended to comprise, without being limited to, these and any other suitable types of memory.

In some implementations, the memory stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system and application programs.

The operating system includes various system programs, such as a framework layer, a core library layer, a driving layer, and the like, and is used for realizing various basic services and processing hardware-based tasks. Applications, including various applications such as Media Player (Media Player), browser (Browser), etc., are used to implement various application services. The program implementing the method of the embodiment of the present disclosure may be contained in an application program.

In the above embodiment, the processor may be further configured to call a program or an instruction stored in the memory, specifically, may be a program or an instruction stored in an application program:

the steps of the above method are performed.

The method described above may be applied in a processor or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The processor may be a general purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The methods, steps and logic blocks disclosed above may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method as disclosed above may be embodied directly in hardware for execution by a decoding processor, or in a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.

It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more application specific integrated circuits (Application Specific Integrated Circuits, ASIC), digital signal processors (Digital Signal Processing, DSP), digital signal processing devices (DSP devices, DSPD), programmable logic devices (Programmable Logic Device, PLD), field programmable gate arrays (Field-Programmable Gate Array, FPGA), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units configured to perform the functions described herein, or a combination thereof.

For a software implementation, the inventive techniques may be implemented with functional modules (e.g., procedures, functions, and so on) that perform the inventive functions. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.

The present invention may also provide a non-volatile storage medium for storing a computer program. The steps of the above-described method embodiments may be implemented when the computer program is executed by a processor.

Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and are not limiting. Although the present invention has been described in detail with reference to the embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the appended claims.

Claims (10)

1. A GMSK-based time domain iterative equalization underwater acoustic communication method, the method comprising:

step 1: before a training sequence is added to a transmitting signal sequence, conversion and information-symbol mapping are carried out, modulation is carried out according to a GMSK signal modulation mode based on Laurent decomposition, a GMSK complex baseband signal is obtained, the GMSK complex baseband signal is modulated on a carrier wave to be used as a final transmitting signal, and the final transmitting signal is transmitted into an underwater sound channel;

step 2: the underwater acoustic channel receives the transmitting signal, carries out band-pass filtering, time-frequency two-dimensional synchronization, matched filtering, time-domain self-adaptive equalization and symbol detection on the received signal to obtain a judged symbol, and completes GMSK underwater acoustic communication based on time-domain iterative equalization.

2. The GMSK-based time domain iterative equalization underwater acoustic communication method of claim 1, wherein the step 1 specifically comprises:

step 1-1: constructing a transmission signal frame;

constructing a transmitting signal frame, wherein the structure comprises a training sequence, an information sequence to be transmitted and a tail symbol sequence; wherein the phase state of the training sequence is satisfied beginning with a zero state and ending with a zero state; the tail symbol is used for ensuring that the phase state of the information sequence to be transmitted returns to zero;

step 1-2: converting and information-symbol mapping the transmitted signal;

for a transmission sequence { a } consisting of a training sequence and an information sequence to be transmitted _k Conversion is carried out to obtain a bipolar non-return-to-zero sequence { x }, which is a sequence of _k Information-symbol mapped to GMSK complex baseband signal sequence { b } _n }：

Wherein b _n Representing GMSK complex baseband signal sequence { b } _n Elements in }; x is x _k Representing bipolar non-return-to-zero code signal sequence { x } _k Elements in }; n represents the data block length; j represents an imaginary number;

step 1-3: modulating a GMSK signal modulation mode based on Laurent decomposition to obtain a GMSK complex baseband signal;

for the complex baseband GMSK signal sequence { b } using a phase shaping function based on Laurent decomposition of the GMSK signal _n Modulating to obtain GMSK complex baseband signal s (t):

wherein t represents time; t represents a symbol period; c (t-nT) is the partial impulse response:

wherein L represents the pulse memorization length, q (t) represents the integral function of the frequency shaping pulse g (τ); τ represents the argument of the integrated function;

wherein B represents bandwidth; q () represents a complementary error function:

where ε represents the argument of the integrated function;

step 1-4: modulating the GMSK complex baseband signal onto a carrier wave to serve as a final transmitting signal, and transmitting the final transmitting signal into an underwater sound channel;

final transmitted signal s ^f (t) is expressed as:

s ^f (t)＝s(t)×exp(j×2π×fc×t)

where fc is the center frequency of the carrier.

3. The GMSK-based time domain iterative equalization underwater acoustic communication method of claim 2, wherein said step 2 specifically comprises:

step 2-1: for the received signal r ^f (t) band-pass filtering, and performing time-frequency two-dimensional synchronization by using a synchronization sequence;

step 2-2: receiving a signal r by means of a low-pass filter and a coherent receiver ^f (t) performing matched filtering to obtain a complex baseband signal r _n；

Step 2-3: equalizing the complex baseband signal by using an adaptive decision feedback equalizer;

step 2-4: combining a GMSK self-carried coding mode, and carrying out joint probability estimation by using a symbol detector to obtain a log-likelihood ratio output by an equalizer;

step 2-5: and according to the solved log-likelihood ratio, judging and solving the difference to obtain a received symbol, and carrying out symbol mapping by utilizing the received symbol and corresponding prior information to obtain the input of the feedback filter.

4. The GMSK-based time domain iterative equalization underwater acoustic communication method of claim 3, wherein the step 2-1 is specifically:

received signal r ^f (t) is expressed as:

wherein h is _η Representing the channel response; z (t) represents the received noise, s ^f () Representing the mostFinal transmit signal, η represents the subscript of the integral, η=0, 1,2,...

5. The GMSK-based time-domain iterative equalization underwater acoustic communication method of claim 4, wherein said step 2-2 is specifically:

wherein n represents the number of the output symbol, s _n For the signal output by the matched filter:

z _n noise representing the matched filter output:

6. the GMSK-based time-domain iterative equalization underwater acoustic communication method of claim 5, wherein said step 2-3 is specifically:

for complex baseband signal r before equalization _n Mapping to obtain mapped signals

Will complex baseband signal r _n Input to adaptive decision feedback equalizer to obtain soft information after time domain equalization

Wherein K is _F And K _B Respectively representing the feedforward filter order and the feedback filter order;representing the feed forward filter input sequence +.>Lambda of the element (a) _j Representing equalizer coefficients; y is _n-j Representing feedback filter input sequence y _n Elements of }; />Representing a phase offset estimate, represented by;

wherein K is ₁ 、K ₂ Different phase-locked loop coefficients;estimating for the updated phase offset; phi _n Is the calculated intermediate quantity;

wherein,outputting a symbol for the feedforward filter; />Estimating error for a priori; [] ^* Representing conjugate calculations.

7. The GMSK-based time domain iterative equalization underwater acoustic communication method of claim 6, wherein the steps 2-4 specifically are:

for balanced soft informationIsolation, definition->

Setting the separated information u _n And v _n Obeys a gaussian distribution:

wherein Pr () is a defined probability distribution function; in the calculation of Pr (u) _n ζ ε { + -1 }, in calculating Pr (v) _n =ζ), ζ∈ {0, ±2/pi }; sigma (sigma) _u Sum sigma _v Based on part of the training sequenceAnd->The obtained variance:

wherein,and->Respectively represent u _n And v _n Is a training sequence part of (1);

performing joint probability calculation on the logarithmic domain, and defining a probability distribution function:

u _n log-likelihood ratio L (u) _n ) Expressed as:

obtaining log-likelihood ratio of estimated symbol by calculating joint probability

Wherein E (u) _n ) And E (v) _n ) Respectively representing the sequences { u }, respectively _n Sum sequence { v } _n Energy of }; at the time of equalization, the probability value for n.ltoreq.0 is obtained from the soft information of the decided symbol, and the probability value for n > 0 is obtained from the soft information of the last iterationProceeding withEstimating; a is that _n Representing the influence of two symbols before and after the current moment on the current symbol, A _n ＝c(0,0；3)×(Pr(u _n-2 ＝1)+Pr(u _n+2 =1) -1), corresponding to u at the time of iterative calculation _n Is obtained by log likelihood ratio, +.>

8. The GMSK-based time domain iterative equalization underwater acoustic communication method of claim 7, wherein the steps 2-5 specifically are:

log likelihood ratio obtained after iterative detectionMaking hard decisions to obtain estimated symbols->Estimated received symbols +.>

Calculating the input y of the feedback filter _n When the symbol is judged, the judged symbol is firstly judgedMapping to p _n ：

Wherein,is p _n+1 Is a function of the estimated value of (2); />Is p _n+2 Is a function of the estimated value of (a):

9. a GMSK based time domain iterative balanced underwater acoustic communication method according to claim 3, wherein the iterative manner is: when the received signals are equalized for the first time, soft information after time domain equalization is obtainedIn subsequent iterations, use +.>Estimating probability values of symbols at different times and mapping, i.e. in symbol-by-symbol equalization of the second iteration, information after n time needed for calculating the symbol at n time is calculated by ∈ ->Obtaining estimation;

the iterative computation specifically comprises the following steps:

a) Output soft information of decision feedback equalizer

b) Using soft informationCalculating probability value Pr (u) _n+1 ＝ζ)、Pr(v _n+1 ＝ξ)、Pr(u _n+2 ζ) =ζ)

Pr(v _n+2 ＝ξ)；

c) Calculating according to the probability value:and->

10. A GMSK-based time domain iterative equalization underwater acoustic communication system, the system comprising:

the signal transmitting module is used for adding a training sequence before a transmitting signal sequence, converting and mapping information and symbols, modulating according to a GMSK signal modulation mode based on Laurent decomposition to obtain a GMSK complex baseband signal, modulating the GMSK complex baseband signal onto a carrier wave to serve as a final transmitting signal, and transmitting the final transmitting signal into an underwater sound channel;

the signal receiving module is used for receiving the transmitting signal through the underwater acoustic channel, carrying out band-pass filtering, time-frequency two-dimensional synchronization, matched filtering, time domain self-adaptive equalization and symbol detection on the received signal to obtain a judged symbol, and completing GMSK underwater acoustic communication based on time domain iterative equalization.