CN109743118B - OFDM underwater acoustic communication method with high spectrum efficiency under time-varying double-spread channel condition - Google Patents
OFDM underwater acoustic communication method with high spectrum efficiency under time-varying double-spread channel condition Download PDFInfo
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Abstract
An OFDM underwater acoustic communication method with high spectrum efficiency under the condition of time-varying double-spread channel belongs to the technical field of underwater acoustic communication. The invention carries on band-pass filtering to the received signal r (k), converts into the digital signal by ADC, after the front end processing such as down conversion and low-pass filtering, estimates the average Doppler by fuzzy function method, after resampling and frequency correction, carries on the signal multiplexing, time domain equalization and frequency domain equalization, after diversity combination, adopts OMP-DCD algorithm to carry on the residual Doppler compensation, adopts the iterative signal processing technique, through the soft-in soft-out mapping/demapping, interweaver/de-interweaver to cascade LDPC decoder and equalizer, realizes the close information interaction in these two modules, fully utilizes the soft information of the reciprocating transmission, effectively resists the interference of the time-varying double-spread channel. The invention can better compromise the complexity and the performance, efficiently solves the problems of serious intersymbol interference, intercarrier interference, pilot frequency-data interference and the like in the transmission process, and has low complexity which can be realized on a DSP processor in real time.
Description
Technical Field
The invention belongs to the technical field of underwater acoustic communication, and particularly relates to an OFDM (orthogonal frequency division multiplexing) underwater acoustic communication method with high spectral efficiency under the condition of time-varying double-spread channels.
Background
OFDM is a multi-carrier transmission scheme, its main idea is to divide the available bandwidth into a large number of sub-bands with carrier frequency, transform the high-speed serial data to the parallel orthogonal sub-channel for transmission through serial-parallel conversion, effectively counteract the influence of intersymbol interference through increasing the symbol period, and its way of processing signal in frequency domain has reduced the complexity for the realization of receiving end equalizer, have strong anti-multipath ability, high frequency band utilization rate, fast communication speed and low advantage of realizing the complexity, etc. The OFDM underwater acoustic communication system mainly develops research around key technologies such as synchronization, channel estimation, efficient error correction codes, Doppler estimation and compensation and the like so as to give full play to the performance of the OFDM technology.
Early OFDM communication systems were designed by s.coatelan, and w.k.lam et al originally proposed the features and basic structure of a complete OFDM communication system, and since 2005, many scientific research institutes in europe and america have conducted studies on OFDM underwater acoustic communication systems on a large scale and have obtained many research results. Since the transmission signal of the OFDM system is the superposition of the transmission signals on multiple orthogonal subcarriers, this brings the following disadvantages to the OFDM system: (1) susceptible to doppler frequency offset; (2) adding a guard interval to reduce transmission efficiency in exchange for the ability to overcome multipath expansion; (3) too much affected by time-varying systems; (4) the high peak-to-average power ratio poses a great challenge to hardware design.
Disclosure of Invention
The invention aims to solve the problem that the receiver detection performance is lower when an OFDM signal without a guard interval is transmitted in a time-varying double-spread underwater acoustic channel with long multipath spread and large Doppler spread, and provides an OFDM underwater acoustic communication method with high spectrum efficiency under the condition of the time-varying double-spread channel. The invention adopts OFDM signal without guard interval and superimposed pilot signal to realize reliable communication and has high spectrum efficiency, and the proposed receiver can solve the problems of intersymbol interference ISI, intercarrier interference ICI, pilot frequency-data interference, low operation complexity and the like and has higher detection performance.
The purpose of the invention is realized as follows:
an OFDM underwater acoustic communication method with high spectrum efficiency under the condition of time-varying double-spread channel comprises the following steps:
(1) after LDPC coding, interleaving and BPSK mapping are carried out on original data by a sending end, data information is modulated to parallel subcarriers through OFDM modulation, and then a known pilot signal is superposed on the data signal to be sent;
(2) the transmitting signal of the step (1) reaches a communication receiving end after passing through a channel, and a receiving signal r (k) obtains a baseband signal after passing through a front-end processing module
For baseband signal
Average Doppler estimation, resampling and frequency correction are carried out to obtain an oversampling signal
Oversampled signal
Multiplexing to NτA signal, this NτDiversity processing, independent equalization, namely time domain equalization and frequency domain equalization are carried out on the signals, and finally the signals are combined into a signal Z (k); equalizing residual Doppler expansion in the signals by adopting a compressed sensing algorithm for the signals which undergo the double-spread channel;
(3) an iterative signal processing technology is adopted in a receiver, a decoder and an equalizer are cascaded, information interaction is continuously carried out through iterative feedback of the decoder and the equalizer, underwater sound channel fading and interference suppression are resisted, and the reliability of the receiver is improved.
The specific operation steps of the step (1) are as follows:
(1.1) carrying out LDPC coding on a data source, namely a binary information stream b to obtain a coded binary bit stream c;
(1.2) interleaving the coded binary bit stream to obtain an interleaved coded binary bit stream d;
(1.3) carrying out BPSK mapping on the binary bit stream obtained in the step (1.2) to obtain a baseband symbol sequence e;
(1.4) performing serial-parallel conversion and IFFT (inverse fast Fourier transform) conversion on the baseband symbol sequence, namely OFDM modulation, to obtain a modulated OFDM symbol sequence s;
(1.5) superposing a known pilot signal on the OFDM symbol sequence obtained in the step (1.4), wherein the superposed pilot signal is used for estimating a time-varying double-spread channel at a receiving end;
(1.6) transmitting a superimposed signal x including data information and pilot information.
The specific operation steps of the step (2) are as follows:
(2.1) a front-end processing module: the received analog signal r (k) is band-pass filtered and analog-to-digital converted by ADCConverting the converted signal into a digital signal, performing down-conversion and low-pass filtering to obtain a baseband signal
(2.2) mean Doppler estimation: firstly, calculating a fuzzy function through a cross-correlation distorted received signal and a pilot signal of a period, finding a peak position and estimating an optimal time-varying Doppler scale factor, and then further optimizing Doppler estimation on the peak by utilizing a parabolic interpolation method;
the average Doppler estimate is obtained by calculating a Doppler cross section with a maximum value through a fuzzy function method, namely:
wherein χ (m:) is the Doppler cross section of the fuzzy function, m is the Doppler position, n is the time delay position,
and
estimated values of m and n, respectively;
obtaining Doppler frequency offset estimation by adopting a parabolic interpolation method
Is composed of
Wherein
k denotes the time kTestObtaining an estimated value;
corresponding estimated scale factor
Is composed of
Wherein f iscFor the centre frequency of the transmitted signal, the time-varying step size is TestLess than one OFDM symbol duration TsI.e. Test＜Ts；
(2.3) resampling and frequency correction: linear interpolation is performed on discrete-time estimates of the scale factor, and the signal is then resampled using the interpolated scale factor
To compensate for the time varying Doppler spread, and to estimate the Doppler shift fd(t) performing frequency correction to obtain a signal
Resampled and frequency corrected signal
Is composed of
Wherein,
is composed of
The continuous-time signal obtained by linear interpolation,
continuous time-varying estimates of Doppler shift, tn＝tn-1+Tr(n)，
Td＝1/(FNτ) F is the transmission signal bandwidth, NτIs an oversampling factor;
(2.4) oversampling Signal
Multiplexing to NτA signal, this NτDiversity processing is carried out on the signals, and the signals are finally combined together after independent equalization, namely time domain equalization and frequency domain equalization;
channel estimation based output x of mth branch of time domain equalizerm(p) is
Wherein,
for the impulse response of the equalizer, TeqFor time delay, LeqIs the length of the equalizer, and N is the number of subcarriers;
frequency domain equalizer output Z of mth branchm(k) Is composed of
Wherein Xm(k) For the discrete fourier transform of the mth branch time domain equalizer output,
for time domain channel estimation, gammaFDIs a regularization parameter;
all diversity frequency domain combined outputs Z (k) are
(2.5) equalizing the residual Doppler spread in the signal by the compressed sensing algorithm for the signal subjected to the double spread channel.
The specific operation steps of the step (3) are as follows:
(3.1) turbo iteration process initialization: setting the prior condition mean value mu of the transmitting symbol to be 0 and the prior condition variance v to be 1, and the initial value of the channel estimation is
(3.2) calculating an ISI mean value from the channel estimate and the calculated prior-conditioned mean value of the transmitted symbols;
(3.3) updating the coefficient vector of the equalizer filter by utilizing an LMMSE (Linear Minimum Mean Square Error) algorithm according to an MSE (Mean-Square Error) criterion, namely a Mean-Square Error rule, and calculating the estimated value of the equalized transmission symbol by utilizing the coefficient vector of the equalizer filter;
(3.4) calculating extrinsic information log-likelihood ratios L of individual coded bits output by the equalizer from the transmitted symbol estimatese(x)；
Wherein,
is the a posteriori probability when the symbol to be estimated is +1,
for the a posteriori probability when the symbol to be estimated is-1,
a priori information input for the equalizer;
(3.5) obtaining the prior information log-likelihood ratio L after the deinterleaving of the external information log-likelihood ratioa(b) As the decoder input, the decoded signal is sent to the decoder for decoding based on the MAP criterion, and the decoder-external information L 'of the next iteration is output'e(b) And a decoding result; if the iteration number does not reach the maximum iteration number gateIf so, carrying out the next step; when the maximum iteration times or the iteration gain-free is reached, ending the iteration process and outputting a decoding result of the decoder;
(3.6) off-decoder information L'e(b) The interleaved data becomes prior information L 'of the next iteration'a(x) Soft mapping is carried out to obtain the prior condition mean mu and the prior condition variance v of the transmitting symbols under BPSK modulation
μ＝tanh(L′a(x)/2)，
v＝1-|μ|2；
(3.7) carrying out channel estimation by using the prior condition mean value mu and the prior condition variance v of the transmitting symbols to obtain a channel estimation value
(3.8) jumping to the step (3.2).
In the residual doppler equalization described in step (2.5), since the underwater acoustic channel is sparse in the time domain and the frequency domain, the sparsity of the delay-doppler domain is considered, and after the average doppler is compensated, the signal having residual doppler, which has undergone the double-spread channel, is subjected to the delay-doppler-amplitude joint estimation by using the OMP-DCD (Orthogonal Matching Pursuit-Dichotomous coordination algorithm).
The OMP-DCD algorithm specifically comprises the following steps:
(2.5.1) input:
redundant dictionary A
Sparsity K; wherein
Representing a received signal vector of length M, initializing: let residual error epsilon0Y, 1 for the number of iterations q, U0＝φ；
(2.5.2) an identification phase: the residual error epsilon and ajCarrying out inner product:
determining corresponding τ0,q,υ0,qWherein a isjIs column j of A; tau and upsilon represent relative time delay and Doppler scale respectively;
(2.5.3) time delay estimation: constructing a local dictionary Aτ(Ωτ,Ωυ) Wherein
Ωυ＝{υ0,q}；
Determining corresponding τqWherein a isτ,jIs AτColumn j of (1); wherein
An estimate representing the relative time delay τ;
(2.5.4) Doppler estimation: constructing a local dictionary Aυ(Ωτ,Ωυ) Wherein Ω isτ＝{τq}，
Determining a corresponding vqWherein a isυ,jIs AυColumn j of (1); wherein
An estimate representing a doppler scale ν;
(2.5.5) updating the dictionary: u shapeq＝Uq-1∪uυ,q(ii) a Solving the least squares problem using the DCD algorithm: alpha is alphaq＝argminα||y-Uqα||2；
(2.5.6) updating the residual: epsilonq＝y-UqαqThe increment q of the iteration times is q + 1;
(2.5.7) judging whether the condition q is satisfied and is larger than K, if so, stopping the iteration process; if not, returning to (2.5.2) and continuing circulation;
(2.5.8) outputting: alpha is alphaq，Ω＝{{τ1,υ1,α1},...,{τq,υq,αq}}。
The signal model of the received signal vector y based on the redundant dictionary is established as follows:
in order to estimate amplitude attenuation, Doppler scale upsilon and relative time delay tau by using a compressed sensing sparse reconstruction algorithm, discrete parameter grids need to be divided in a parameter space of the Doppler scale and the relative time delay, namely
Wherein,
υn+1＝υn+Δυ,n＝1,...,Dυ-1
τn+1＝τn+Δτ,n＝1,...,Dτ-1
then, a redundant dictionary A is established according to the parameter grid and the time domain waveform s (t) of the transmitting signal, namely
Wherein
Thereby modeling the redundant dictionary, i.e.
y＝Aα+η
Wherein
And then reconstructing a sparse vector alpha by utilizing the compressed sensing sparse reconstruction algorithm, namely an OMP-DCD algorithm according to the known received signal vector y and the redundancy dictionary A, estimating corresponding Doppler scale and relative time delay according to the position of the nonzero element in the alpha, and estimating amplitude attenuation according to the value of the nonzero element in the alpha.
The invention has the beneficial effects that:
the invention uses OFDM signal without guard interval and superimposed pilot signal to communicate in time-varying double-spread underwater acoustic channel, the receiving end estimates and compensates Doppler spread by calculating the mutual fuzzy function of the received signal and the pilot signal and corresponding interpolation technology, designs a practical and feasible receiver which can efficiently detect data signal and can efficiently solve the problems of serious intersymbol interference, intercarrier interference, pilot frequency-data interference and the like in the transmission process. Compared with an ideal receiver which works under the condition of no interference and has perfect channel state information, the receiver has the signal-to-noise ratio loss of less than 3dB in a time-varying double-spread channel. Meanwhile, the receiver has low complexity and can be realized on a DSP (Digital Signal Processing) processor in real time.
Drawings
FIG. 1 is a complete signal generation flow diagram;
FIG. 2 is a block diagram of the receiver processing of the present invention;
FIG. 3 is a block diagram of receiver front-end processing;
FIG. 4 is a block diagram of an average Doppler estimation process;
FIG. 5 is a block diagram of a time domain equalizer;
fig. 6 is a block diagram of time domain channel estimation.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The first embodiment is as follows:
a high-spectrum-efficiency OFDM underwater acoustic communication method under the condition of time-varying double-spread channels comprises the following steps:
step 1, after LDPC coding, interleaving and BPSK mapping are carried out on original data by a sending end, data information is modulated to parallel subcarriers through OFDM modulation, then a known pilot signal is superposed to a data signal to be sent, and the superposed signal is used for estimating a time-varying double-spread channel at a receiving end;
step 2, the transmitting signal of step 1 reaches the communication receiving end after passing through the channel, and the signal processing is carried out on the receiving signal r (k), and the specific steps are as follows:
step 2.1, the front-end processing module performs band-pass filtering on the received analog signal r (k), converts the analog signal r (k) into a digital signal through an ADC (analog-to-digital converter), and obtains a baseband signal after down-conversion and low-pass filtering
Step 2.2, average Doppler estimation, namely, firstly, calculating a fuzzy function through a received signal with cross-correlation distortion and a pilot signal in a period, finding a peak position and estimating an optimal time-varying Doppler scale factor, and then further optimizing Doppler estimation on the peak value by utilizing a parabolic interpolation method;
the average Doppler estimate is obtained by calculating a Doppler cross section with a maximum value through a fuzzy function method, namely:
wherein χ (m:) is the Doppler cross section of the fuzzy function, m is the Doppler position, n is the time delay position,
and
estimated values of m and n, respectively;
by throwingObtaining Doppler frequency offset estimation after the object line interpolation method
Is composed of
Wherein
k denotes the time kTestObtaining an estimated value;
corresponding estimated scale factor
Is composed of
Wherein f iscFor the centre frequency of the transmitted signal, the time-varying step size is TestLess than one OFDM symbol duration TsI.e. Test＜Ts；
Step 2.3, resampling and frequency correction, linear interpolation is carried out on the discrete time estimation of the scale factor, and then the signal is resampled by using the interpolated scale factor
To compensate for the time varying Doppler spread, and to estimate the Doppler shift fd(t) performing frequency correction to obtain a signal
Resampled and frequency corrected signal
Is composed of
Wherein,
is composed of
The continuous-time signal obtained by linear interpolation,
continuous time-varying estimates of Doppler shift, tn＝tn-1+Tr(n)，
Td＝1/(FNτ) F is the transmission signal bandwidth, NτIs an oversampling factor;
step 2.4, oversampling the signal
Multiplexing to NτA signal, this NτDiversity processing is carried out on the signals, and independent equalization, namely time domain equalization and frequency domain equalization are finally combined together;
channel estimation based output x of mth branch of time domain equalizerm(p) is
Wherein,
for the impulse response of the equalizer, TeqFor time delay, LeqIs the length of the equalizer, and N is the number of subcarriers;
frequency domain equalizer output Z of mth branchm(k) Is composed of
Wherein Xm(k) For the discrete fourier transform of the mth branch time domain equalizer output,
for time domain channel estimation, gammaFDIs a regularization parameter;
all diversity frequency domain combined outputs Z (k) are
Step 2.5, the signal undergoing the double-spread channel adopts a compressed sensing algorithm to balance the residual Doppler spread in the signal;
step 3, in order to effectively and reliably communicate the OFDM signal without the guard interval in the time-varying double-spread underwater acoustic channel, the invention adopts the iterative signal processing technology in the receiver, cascades the decoder and the equalizer, continuously carries out information interaction through the iterative feedback of the decoder and the equalizer, effectively resists the underwater acoustic channel fading and inhibits the interference, and further improves the reliability of the receiver, and the specific steps are as follows:
step 3.1, initializing the turbo iteration process, setting the prior condition mean value mu of the transmission symbol to be 0 and the prior condition variance v to be 1, and the initial value of the channel estimation to be
Step 3.2, calculating ISI mean value according to channel estimation and the calculated prior condition mean value of the transmitted symbol;
step 3.3, updating the coefficient vector of the equalizer filter by utilizing an LMMSE algorithm according to an MSE criterion, and calculating the estimated value of the equalized transmitting symbol by utilizing the coefficient vector of the equalizer filter;
step 3.4, calculating the extrinsic information log-likelihood ratio L of each coded bit output by the equalizer by the transmitted symbol estimatione(x)；
Wherein,
is the a posteriori probability when the symbol to be estimated is +1,
for the a posteriori probability when the symbol to be estimated is-1,
a priori information input for the equalizer;
step 3.5, obtaining prior information log-likelihood ratio L after de-interleaving of external information log-likelihood ratioa(b) As the decoder input, the decoded signal is sent to the decoder for decoding based on the MAP criterion, and the decoder-external information L 'of the next iteration is output'e(b) And a decoding result. And if the iteration times do not reach the maximum iteration time threshold, carrying out the next step. When the maximum iteration times or the iteration gain-free is reached, ending the iteration process and outputting a decoding result of the decoder;
step 3.6, decoder external information L'e(b) The interleaved data becomes prior information L 'of the next iteration'a(x) Soft mapping is carried out to obtain the prior condition mean mu and the prior condition variance v of the transmitting symbols under BPSK modulation
μ＝tanh(L′a ′(x)/2)，
v＝1-|μ|2；
Step 3.7, carrying out channel estimation by using the prior condition mean value mu and the prior condition variance v of the transmitting symbol to obtain a channel estimation value
And 3.8, jumping to the step 3.2.
The second embodiment is as follows: the embodiment is illustrated in fig. 1, and the specific operation steps of step 1 of the embodiment are as follows:
step 1.1, performing LDPC coding on a data source, namely a binary information stream b to obtain a coded binary bit stream c;
step 1.2, interleaving the coded binary bit stream to obtain an interleaved coded binary bit stream d;
step 1.3, carrying out BPSK mapping on the binary bit stream obtained in the step 1.2 to obtain a baseband symbol sequence e;
step 1.4, performing serial-to-parallel conversion and IFFT (inverse Fast Fourier transform) conversion on the baseband symbol sequence, namely OFDM modulation, and obtaining a modulated OFDM symbol sequence s;
step 1.5, superposing a known pilot signal on the OFDM symbol sequence obtained in step 1.4, wherein the superposed pilot signal is used for estimating a time-varying double-spread channel at a receiving end;
and step 1.6, sending the superposed signal x comprising the data information and the pilot frequency information.
Other steps are the same as those in the first embodiment.
The third concrete implementation mode: in step 2.5 of this embodiment, it is considered that the underwater acoustic channel may be considered to be sparse in the time domain and the frequency domain, so that the sparsity of the delay-doppler domain is considered, and after the average doppler is compensated, the time delay-doppler-amplitude joint estimation is performed on the signal having residual doppler, which has undergone the double-spread channel, by using an OMP-DCD (Orthogonal Matching Pursuit-Dichotomous correlation determination) algorithm.
The Orthogonal Matching Pursuit (OMP) algorithm is a greedy algorithm, and the basic idea is as follows: the observation signal is regarded as the linear combination of certain atoms in the redundant original word library, and an optimal solution or a local optimal solution is sought through a certain number of iterative processes, so that the residual error between the estimation signal and the original signal is smaller and smaller, and the approximation to the original signal is finally realized. The core idea of the OMP algorithm is as follows: and (3) realizing the reconstruction of the sparse signal alpha by using an iterative method. And selecting the atom with the maximum correlation with the current residual error in each iteration, then subtracting the correlation part from the observation vector, and repeating the process until the iteration number reaches the sparsity K. Because the least square problem needs to be solved in each iteration, the operation amount of the OMP algorithm is large, the OMP algorithm is combined with the binary coordinate reduction DCD algorithm, the OMP-DCD algorithm is adopted, and the DCD algorithm based on the binary search idea greatly reduces the complexity of solving the sparse reconstruction problem.
The specific implementation steps of the algorithm are shown in table 1. Table 1 double-spread hydroacoustic channel to be estimated
Is a sparse vector, D is the channel length, and its sparsity is K.
Representing a received signal vector of length M. Tau and υ in table 1 represent the relative delay and doppler scales respectively,
and
representing estimates of τ and υ, respectively. In order to estimate amplitude attenuation, Doppler scale and relative time delay by using a compressed sensing sparse reconstruction algorithm, discrete parameter grids need to be divided in a parameter space of the Doppler scale and the relative time delay firstly, namely
Wherein,
υn+1＝υn+Δυ,n＝1,...,Dυ-1
τn+1＝τn+Δτ,n＝1,...,Dτ-1
then, a redundant dictionary A is established according to the parameter grid and the time domain waveform s (t) of the transmitting signal, namely
Wherein
Thereby modeling the redundant dictionary, i.e.
y＝Aα+η
Wherein
And then reconstructing a sparse vector alpha by using a compressed sensing sparse reconstruction algorithm according to a known received signal vector y and a redundancy dictionary A, estimating corresponding Doppler scale and relative time delay according to the position of a nonzero element in the alpha, and estimating amplitude attenuation according to the value of the nonzero element in the alpha. The dictionary is divided into a global dictionary and a local dictionary, and the global dictionary A ═ a in the table 1j1 ≦ j ≦ D column vector ajAre all unit vectors. In table 1, U represents a matrix formed by all column vectors in a whose indexes belong to a support set, a set formed by indexes of non-zero elements in α is called a support set, and U represents a column vector in a whose inner product with the residual epsilon is the largest.
Table 1: dual-spread underwater acoustic channel estimation algorithm based on OMP-DCD algorithm
The other steps are the same as those in the second embodiment.
Claims (6)
1. An OFDM underwater acoustic communication method with high spectrum efficiency under the condition of time-varying double-spread channel is characterized by comprising the following steps:
(1) after LDPC coding, interleaving and BPSK mapping are carried out on original data by a sending end, data information is modulated to parallel subcarriers through OFDM modulation, and then a known pilot signal is superposed on the data signal to be sent;
(2) the transmitting signal of the step (1) reaches a communication receiving end after passing through a channel, and a receiving signal r (k) obtains a baseband signal after passing through a front-end processing module
For baseband signal
Average Doppler estimation, resampling and frequency correction are carried out to obtain an oversampling signal
Oversampled signal
Multiplexing to NτA signal, this NτDiversity processing, independent equalization, namely time domain equalization and frequency domain equalization are carried out on the signals, and finally the signals are combined into a signal Z (k); equalizing residual Doppler expansion in the signals by adopting a compressed sensing algorithm for the signals which undergo the double-spread channel;
(2.1) a front-end processing module: the received analog signal r (k) is subjected to band-pass filtering, converted into a digital signal after ADC analog-to-digital conversion, down-converted and low-pass filtered to obtain a baseband signal
(2.2) mean Doppler estimation: firstly, calculating a fuzzy function through a cross-correlation distorted received signal and a pilot signal of a period, finding a peak position and estimating an optimal time-varying Doppler scale factor, and then further optimizing Doppler estimation on the peak by utilizing a parabolic interpolation method;
the average Doppler estimate is obtained by calculating a Doppler cross section with a maximum value through a fuzzy function method, namely:
wherein χ (m:) is the Doppler cross section of the fuzzy function, m is the Doppler position, n is the time delay position,
and
estimated values of m and n, respectively;
obtaining Doppler frequency offset estimation by adopting a parabolic interpolation method
Is composed of
Wherein
k denotes the time kTestObtaining an estimated value;
corresponding estimated scale factor
Is composed of
Wherein f iscFor the centre frequency of the transmitted signal, the time-varying step size is TestLess than one OFDM symbol duration TsI.e. Test＜Ts；
(2.3) resampling and frequency correction: linear interpolation is performed on discrete-time estimates of the scale factor, and the signal is then resampled using the interpolated scale factor
To compensate for the time varying Doppler spread, and to estimate the Doppler shift fd(t) performing frequency correction to obtain a signal
Resampled and frequency corrected signal
Is composed of
Wherein,
is composed of
The continuous-time signal obtained by linear interpolation,
continuous time-varying estimates of Doppler shift, tn＝tn-1+Tr(n)，
Td＝1/(FNτ) F is the transmission signal bandwidth, NτIs an oversampling factor;
(2.4) oversampling Signal
Multiplexing to NτA signal, this NτDiversity processing is carried out on the signals, and the signals are finally combined together after independent equalization, namely time domain equalization and frequency domain equalization;
channel estimation based output x of mth branch of time domain equalizerm(p) is
Wherein,
for the impulse response of the equalizer, TeqFor time delay, LeqIs the length of the equalizer, and N is the number of subcarriers;
frequency domain equalizer output Z of mth branchm(k) Is composed of
Wherein Xm(k) For the discrete fourier transform of the mth branch time domain equalizer output,
for time domain channel estimation, gammaFDIs a regularization parameter;
all diversity frequency domain combined outputs Z (k) are
(2.5) equalizing the residual Doppler spread in the signal by adopting a compressed sensing algorithm for the signal subjected to the double-spread channel;
(3) an iterative signal processing technology is adopted in a receiver, a decoder and an equalizer are cascaded, information interaction is continuously carried out through iterative feedback of the decoder and the equalizer, underwater sound channel fading and interference suppression are resisted, and the reliability of the receiver is improved.
2. The method for OFDM underwater acoustic communication with high spectral efficiency under the condition of time-varying double-spread channel as claimed in claim 1, wherein the specific operation steps of the step (1) are as follows:
(1.1) carrying out LDPC coding on a data source, namely a binary information stream b to obtain a coded binary bit stream c;
(1.2) interleaving the coded binary bit stream to obtain an interleaved coded binary bit stream d;
(1.3) carrying out BPSK mapping on the binary bit stream obtained in the step (1.2) to obtain a baseband symbol sequence e;
(1.4) performing serial-parallel conversion and IFFT (inverse fast Fourier transform) conversion on the baseband symbol sequence, namely OFDM modulation, to obtain a modulated OFDM symbol sequence s;
(1.5) superposing a known pilot signal on the OFDM symbol sequence obtained in the step (1.4), wherein the superposed pilot signal is used for estimating a time-varying double-spread channel at a receiving end;
(1.6) transmitting a superimposed signal x including data information and pilot information.
3. The OFDM underwater acoustic communication method with high spectrum efficiency under the condition of time-varying double-spread channel as claimed in claim 1, wherein the specific operation steps of the step (3) are as follows:
(3.1) turbo iteration process initialization: setting the prior condition mean value mu of the transmitting symbol to be 0 and the prior condition variance v to be 1, and the initial value of the channel estimation is
(3.2) calculating an ISI mean value from the channel estimate and the calculated prior-conditioned mean value of the transmitted symbols;
(3.3) updating the coefficient vector of the equalizer filter by utilizing an LMMSE (Linear Minimum Mean Square Error) algorithm according to an MSE (Mean-Square Error) criterion, namely a Mean-Square Error rule, and calculating the estimated value of the equalized transmission symbol by utilizing the coefficient vector of the equalizer filter;
(3.4) calculating extrinsic information log-likelihood ratios L of individual coded bits output by the equalizer from the transmitted symbol estimatese(x)；
Wherein,
is the a posteriori probability when the symbol to be estimated is +1,
for the a posteriori probability when the symbol to be estimated is-1,
a priori information input for the equalizer;
(3.5) obtaining the prior information log-likelihood ratio L after the deinterleaving of the external information log-likelihood ratioa(b) As the decoder input, the decoded signal is sent to the decoder for decoding based on the MAP criterion, and the decoder-external information L 'of the next iteration is output'e(b) And a decoding result; if the iteration times do not reach the maximum iteration time threshold, the next step is carried out; when the maximum iteration times or the iteration gain-free is reached, ending the iteration process and outputting a decoding result of the decoder;
(3.6) off-decoder information L'e(b) The interleaved data becomes prior information L 'of the next iteration'a(x) Soft mapping is carried out to obtain the prior condition mean mu and the prior condition variance v of the transmitting symbols under BPSK modulation
μ＝tanh(L′a(x)/2)，
v＝1-|μ|2；
(3.7) carrying out channel estimation by using the prior condition mean value mu and the prior condition variance v of the transmitting symbols to obtain a channel estimation value
(3.8) jumping to the step (3.2).
4. The method of claim 3 for high spectral efficiency OFDM underwater acoustic communication under time-varying double-spread channel conditions, wherein: in the residual doppler equalization described in step (2.5), since the underwater acoustic channel is sparse in the time domain and the frequency domain, the sparsity of the delay-doppler domain is considered, and after the average doppler is compensated, the signal having residual doppler, which has undergone the double-spread channel, is subjected to the delay-doppler-amplitude joint estimation by using the OMP-DCD (Orthogonal Matching Pursuit-Dichotomous coordination algorithm).
5. The OFDM underwater acoustic communication method with high spectral efficiency under the condition of time-varying double-spread channel according to claim 4, wherein the OMP-DCD algorithm specifically comprises the following steps:
(2.5.1) input:
redundant dictionary
Sparsity K; wherein
Representing a received signal vector of length M, initializing: let residual error epsilon0Y, 1 for the number of iterations q, U0＝φ；
(2.5.2) an identification phase: the residual error epsilon and ajCarrying out inner product:
determining corresponding τ0,q,υ0,qWherein a isjIs column j of A; tau and upsilon represent relative time delay and Doppler scale respectively;
(2.5.3) time delay estimation: constructing a local dictionary Aτ(Ωτ,Ωυ) Wherein
Ωυ＝{υ0,q}；
Determining corresponding τqWherein a isτ,jIs AτColumn j of (1); wherein
An estimate representing the relative time delay τ;
(2.5.4) Doppler estimation: constructing a local dictionary Aυ(Ωτ,Ωυ) Wherein
Determining a corresponding vqWherein a isυ,jIs AυColumn j of (1); wherein
An estimate representing a doppler scale ν;
(2.5.5) updating the dictionary: u shapeq＝Uq-1∪uυ,q(ii) a Solving the least squares problem using the DCD algorithm:
αq＝arg minα||y-Uqα||2；
(2.5.6) updating the residual: epsilonq＝y-UqαqThe increment q of the iteration times is q + 1;
(2.5.7) judging whether the condition q is satisfied and is larger than K, if so, stopping the iteration process; if not, returning to (2.5.2) and continuing circulation;
(2.5.8) outputting: alpha is alphaq，Ω＝{{τ1,υ1,α1},...,{τq,υq,αq}}。
6. The method of claim 5, wherein the received signal vector y is based on a redundant dictionary signal model, and the method comprises:
in order to estimate amplitude attenuation, Doppler scale upsilon and relative time delay tau by using a compressed sensing sparse reconstruction algorithm, discrete parameter grids need to be divided in a parameter space of the Doppler scale and the relative time delay, namely
Wherein,
υn+1＝υn+Δυ,n＝1,...,Dυ-1
τn+1＝τn+Δτ,n＝1,...,Dτ-1
then, a redundant dictionary A is established according to the parameter grid and the time domain waveform s (t) of the transmitting signal, namely
Wherein
Thereby modeling the redundant dictionary, i.e.
y＝Aα+η
Wherein
And then reconstructing a sparse vector alpha by utilizing the compressed sensing sparse reconstruction algorithm, namely an OMP-DCD algorithm according to the known received signal vector y and the redundancy dictionary A, estimating corresponding Doppler scale and relative time delay according to the position of the nonzero element in the alpha, and estimating amplitude attenuation according to the value of the nonzero element in the alpha.
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| CN115883298B (en) * | 2022-11-11 | 2024-08-27 | 中国船舶重工集团公司第七一五研究所 | Underwater acoustic communication method based on Haar distributed domain coding diversity |
| CN116484180B (en) * | 2023-06-21 | 2023-09-22 | 中国人民解放军国防科技大学 | System and method for extracting communication signal gene |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20090083003A (en) * | 2008-01-29 | 2009-08-03 | 삼성전자주식회사 | Apparatus and method for estimating doppler spread in wireless communication system |
| CN101631099A (en) * | 2009-08-05 | 2010-01-20 | 华南理工大学 | Multi-input and multi-output selectivity OFDM underwater sound communication system and method |
| CN102065046A (en) * | 2009-11-11 | 2011-05-18 | 北京泰美世纪科技有限公司 | Information transmission method and signal generation device of OFDM (Orthogonal Frequency Division Multiplexing) system |
| CN102868659A (en) * | 2012-08-31 | 2013-01-09 | 中国船舶重工集团公司第七一五研究所 | Symbol synchronization and Doppler compensation method for mobile orthogonal frequency division multiplexing (OFDM) underwater sound communication signal |
| CN103491046A (en) * | 2013-09-12 | 2014-01-01 | 江苏科技大学 | Method for processing Doppler expansion of underwater sound high-speed OFDM communication |
| CN107426120A (en) * | 2017-07-24 | 2017-12-01 | 哈尔滨工程大学 | A kind of underwater sound OFDM MFSK channel equalization methods based on least mean-square error |
| CN107911133A (en) * | 2017-11-17 | 2018-04-13 | 厦门大学 | The Doppler factor estimation and compensation method of a kind of mobile underwater sound communication |
| CN108092723A (en) * | 2017-12-28 | 2018-05-29 | 浙江大学 | Independent pathway Doppler effect correction and equalization methods under the double extended channel environment of the underwater sound based on compressed sensing algorithm |
-
2018
- 2018-12-24 CN CN201811584348.4A patent/CN109743118B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20090083003A (en) * | 2008-01-29 | 2009-08-03 | 삼성전자주식회사 | Apparatus and method for estimating doppler spread in wireless communication system |
| CN101631099A (en) * | 2009-08-05 | 2010-01-20 | 华南理工大学 | Multi-input and multi-output selectivity OFDM underwater sound communication system and method |
| CN102065046A (en) * | 2009-11-11 | 2011-05-18 | 北京泰美世纪科技有限公司 | Information transmission method and signal generation device of OFDM (Orthogonal Frequency Division Multiplexing) system |
| CN102868659A (en) * | 2012-08-31 | 2013-01-09 | 中国船舶重工集团公司第七一五研究所 | Symbol synchronization and Doppler compensation method for mobile orthogonal frequency division multiplexing (OFDM) underwater sound communication signal |
| CN103491046A (en) * | 2013-09-12 | 2014-01-01 | 江苏科技大学 | Method for processing Doppler expansion of underwater sound high-speed OFDM communication |
| CN107426120A (en) * | 2017-07-24 | 2017-12-01 | 哈尔滨工程大学 | A kind of underwater sound OFDM MFSK channel equalization methods based on least mean-square error |
| CN107911133A (en) * | 2017-11-17 | 2018-04-13 | 厦门大学 | The Doppler factor estimation and compensation method of a kind of mobile underwater sound communication |
| CN108092723A (en) * | 2017-12-28 | 2018-05-29 | 浙江大学 | Independent pathway Doppler effect correction and equalization methods under the double extended channel environment of the underwater sound based on compressed sensing algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 基于压缩感知的MIMO_OFDM冰下水声通信技术研究;周焕玲;《中国优秀硕士学位论文全文数据库信息科技辑》;20180415;第3章3.3节 * |
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