CN111884970A - Taylor-FFT demodulation method based on underwater acoustic communication - Google Patents

Taylor-FFT demodulation method based on underwater acoustic communication Download PDF

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CN111884970A
CN111884970A CN202010581747.6A CN202010581747A CN111884970A CN 111884970 A CN111884970 A CN 111884970A CN 202010581747 A CN202010581747 A CN 202010581747A CN 111884970 A CN111884970 A CN 111884970A
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fft
taylor
combiner
underwater acoustic
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王辰
马雪飞
王博
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Harbin Engineering University
Heilongjiang University of Science and Technology
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Heilongjiang University of Science and Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2649—Demodulators
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B13/00—Transmission systems characterised by the medium used for transmission, not provided for in groups H04B3/00 - H04B11/00
    • H04B13/02—Transmission systems in which the medium consists of the earth or a large mass of water thereon, e.g. earth telegraphy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2649—Demodulators
    • H04L27/265—Fourier transform demodulators, e.g. fast Fourier transform [FFT] or discrete Fourier transform [DFT] demodulators

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Abstract

The invention discloses a Taylor-FFT demodulation method based on underwater acoustic communication. Step 1: at a receiving end, firstly, a training sequence is used for synchronization, and an OFDM symbol is obtained after the training sequence and a cyclic prefix are removed; step 2: performing pre-FFT processing on the OFDM symbols; and step 3: then, the result of the pre-FFT processing in the step 2 is processed by FFT to use a combiner to carry out weighted summation of FFT output; and 4, step 4: and (4) carrying out next processing on the weighted and summed OFDM symbols in the step (3). The invention adopts a pre-FFT processing technology, the input of each FFT is the product of an input signal and a Taylor polynomial, and the combiner executes the weighted summation of the FFT output to determine the optimal combiner weight, compensate the non-uniform Doppler frequency offset caused by a time-varying channel, and further improve the communication quality.

Description

Taylor-FFT demodulation method based on underwater acoustic communication
Technical Field
The invention relates to the technical field of underwater acoustic communication, in particular to a Taylor-FFT demodulation method based on underwater acoustic communication.
Background
The underwater acoustic communication can ensure the information exchange among submarines, frogmans, torpedoes and command centers. In addition, underwater acoustic communications are also promising for civil use. Orthogonal frequency division multiplexing technology has occupied an important research position in underwater high-speed communication because of the advantages of good noise resistance, high utilization rate of frequency bands, multipath channel interference resistance and the like. The OFDM underwater sound communication technology can not only transmit files such as sound and pictures with small data volume, but also transmit video files with high data volume. Therefore, the technology can further meet the networking work requirement among various types of underwater machines. In addition, OFDM technology has other application advantages, such as providing high data volume delivery with little latency, highly scalable network architecture, and easy implementation of underwater networking communications. These advantages all have a positive impact on the use of OFDM technology in underwater acoustic communications.
Doppler interference between nodes moving relative to each other may occur, which may affect each other from subcarrier to subcarrier, destroy orthogonality of the system, and further affect performance of the communication system. Even if relative motion does not exist among the nodes, namely the nodes are kept relatively static, the orthogonality of the OFDM system can be destroyed by the underwater sound channel which changes along with time, and then mutual interference among subcarriers is caused, and the performance of the OFDM communication system is seriously affected. Therefore, how to reduce the influence of the doppler shift is the key to improve the OFDM underwater acoustic communication quality.
Disclosure of Invention
The invention provides a Taylor-FFT demodulation method based on underwater acoustic communication, which adopts a pre-FFT processing technology, the input of each FFT is the product of an input signal and a Taylor polynomial, and a combiner executes the weighted summation of FFT output to determine the optimal combiner weight, compensate the non-uniform Doppler frequency offset caused by a time-varying channel, and further improve the communication quality.
The invention is realized by the following technical scheme:
a Taylor-FFT demodulation method based on underwater acoustic communication, the demodulation method comprises the following steps:
step 1: at the receiving end, firstly, the training sequence is used for synchronization, the training sequence and the cyclic prefix are removed to obtain OFDM symbols,
using schmidt orthogonalization to obtain a set of orthogonal taylor polynomials,
step 2: performing pre-FFT processing on the OFDM symbols;
and step 3: then, the result of the pre-FFT processing in the step 2 is processed by FFT to use a combiner to carry out weighted summation of FFT output;
and 4, step 4: and 3, performing demapping processing on the weighted and summed OFDM symbols in the step 3.
Further, the pre-FFT processing in step 2 is to use Schmidt orthogonalization to obtain a set of orthogonal Taylor polynomials of,
Figure BDA0002553436550000021
in the formula (I), the compound is shown in the specification,
Figure BDA0002553436550000022
is a coefficient of the taylor polynomial,
Figure BDA0002553436550000023
coefficients of taylor polynomials of different powers.
Further, the step 3 is specifically to input the preprocessed OFDM symbols into an adaptive random gradient algorithm, and obtain the weighting factors of the subcarriers that make the adaptive gradient algorithm converge by using the adaptive gradient algorithm.
Further, the adaptive random gradient algorithm shows the weight of the combiner corresponding to the kth carrier and the kth receiving original
Figure BDA0002553436550000024
Define the corresponding input vector:
Figure BDA0002553436550000025
in the formula (I), the compound is shown in the specification,
Figure BDA0002553436550000026
for the input of the k-1 carrier wave of the m receiving unitInputting a signal;
the output of the combiner is represented as:
Figure BDA0002553436550000027
in the formula (I), the compound is shown in the specification,
Figure BDA0002553436550000028
is the input of the combiner and is the output of the combiner,
Figure BDA0002553436550000029
for the weights obtained by the adaptive random gradient,
Figure BDA00025534365500000210
the length L of the combiner is larger than or equal to I.
Furthermore, in the least mean square algorithm LMS of the adaptive random gradient algorithm, the correlation between the input time vectors is first reduced, and when the vectors are orthogonal to each other, the algorithm will obtain the fastest convergence rate.
The invention has the beneficial effects that:
the method provided by the invention can be used for compensating the non-uniform Doppler frequency offset caused by the variable speed motion of both communication parties and the inherent property of the underwater acoustic channel, so that the performance of the underwater acoustic communication system is improved.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
FIG. 2 is a block diagram of the T-FFT algorithm of the present invention.
FIG. 3 is a block diagram of the combiner of the present invention.
FIG. 4 is a graph of the Taylor orthogonal polynomials of the present invention raised to different powers.
FIG. 5 is a schematic diagram of the adaptive algorithm architecture of the present invention.
FIG. 6 is a graph of performance of the present invention as a function of signal to noise ratio, wherein FIG. 6- (a) is a graph of TFFT error rate performance, and FIG. 6- (b) is a graph of MSE performance as a function of signal to noise ratio.
FIG. 7 is a graph of the performance of the present invention as a function of Doppler shift, wherein FIG. 7- (b) is a graph of TFFT error rate performance as a function of Doppler shift, and FIG. 7- (b) is a graph of MSE performance as a function of Doppler shift.
FIG. 8 is a block diagram of the experimental receiver connection of the present invention.
Fig. 9 is an LFM synchronization diagram of the present invention.
Fig. 10 shows a constellation diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The rest of fig. 1 is not described again for the prior art.
As shown in fig. 1-3, a Taylor-FFT demodulation method based on underwater acoustic communication includes the following steps:
step 1: at a receiving end, as shown in fig. 1, a hydrophone is used to receive a transmission signal (underwater sound signal is converted into an electric signal) and amplify and improve a received signal, and a cooledt software is used to collect the signal through a sound card to operate (such as storage, cutting, filtering and the like) the received signal; firstly, synchronizing by using a training sequence, and removing the training sequence and a cyclic prefix to obtain an OFDM symbol;
step 2: performing pre-FFT processing on the OFDM symbols; since a Linear Frequency Modulation (LFM) signal has a sharp ambiguity function to obtain good synchronization performance, a transmitting end inserts the LFM signal in front of an OFDM symbol for synchronization, a receiving end convolves the received signal with a local LFM signal as shown in fig. 8 to determine a start position of the received signal by taking a maximum value of correlation, and then removes a cyclic prefix for resisting multipath effect from the OFDM symbol with the determined start position for subsequent signal demodulation.
And step 3: then, the result of the pre-FFT processing in the step 2 is processed by FFT to use a combiner to carry out weighted summation of FFT output;
and 4, step 4: and 3, performing demapping processing on the weighted and summed OFDM symbols in the step 3.
As shown in fig. 4, further, the pre-FFT processing in step 2 is to use schmidt orthogonalization to obtain a set of orthogonal taylor polynomials,
Figure BDA0002553436550000031
in the formula (I), the compound is shown in the specification,
Figure BDA0002553436550000032
for coefficients of taylor polynomials of different powers,
Figure BDA0002553436550000033
is a coefficient of the taylor polynomial,
the received OFDM symbols are multiplied by taylor polynomials of different exponentials, respectively.
Further, the step 3 is specifically to input the preprocessed OFDM symbols into an adaptive random gradient algorithm, and obtain the weighting factors of the subcarriers that make the adaptive gradient algorithm converge by using the adaptive gradient algorithm.
As shown in fig. 5, further, the adaptive random gradient algorithm shows weights of the combiner corresponding to the kth carrier and the kth receiving original
Figure BDA0002553436550000041
Define the corresponding input vector:
Figure BDA0002553436550000042
in the formula (I), the compound is shown in the specification,
Figure BDA0002553436550000043
is the input signal of the (k-1) th carrier of the mth receiving unit (I is the output after the ith FFT),
the output of the combiner is represented as:
Figure BDA0002553436550000044
in the formula (I), the compound is shown in the specification,
Figure BDA0002553436550000045
is the input of the combiner and is the output of the combiner,
Figure BDA0002553436550000046
for the weights obtained by the adaptive random gradient,
Figure BDA0002553436550000047
the length L of the combiner is larger than or equal to I for the output of the combiner, when I is equal to 1, the combiner is a traditional FFT demodulation structure, only one demodulator and an equalizer with the size of L are needed, and when L is equal to I, the combiner corresponds to a single-tap demodulator.
Estimation of weighting factors
Requiring weighting factors at the receiving end
Figure BDA0002553436550000048
An estimate is made, which is actually performed after channel equalization, usually by adding a filter to complete the channel equalization,
Figure BDA0002553436550000049
the tap coefficients corresponding to the tap coefficients of each filter are usually adjusted manually if the information of the channel is known at the receiving end, but for the time-varying underwater acoustic channel, the receiving end needs to automatically adjust the tap coefficients.
Adaptive filters can be divided into finite impulse response filters and infinite impulse response filters, with the difference that their impulse response is finite or infinite. The tap coefficient value of the current moment generated by self-adaptive adjustment is used as the weighted value of signal weighted addition of each delay line, the estimation error is used, the absolute value is obtained through the difference between the result of weighted addition and the expected response of the signal, a self-adaptive algorithm is excited, a new tap coefficient is obtained, and the optimal performance of the filter is achieved through an iterative algorithm.
As shown in fig. 5, further, in the least mean square algorithm LMS of the adaptive random gradient algorithm, the correlation between the vectors of each input time is first reduced, and when the vectors are orthogonal to each other, the algorithm will obtain the fastest convergence rate.
As shown in fig. 5, further, the least mean square algorithm LMS specifically includes:
Figure BDA00025534365500000410
Figure BDA0002553436550000051
to further improve the stability and prevent error propagation, a least mean square algorithm LMS is used, wherein a thresholding method is used which keeps the combiner weights constant if the error or gradient exceeds a predetermined level, which prevents sudden changes in the combiner weights that may occur in the event of a decision error.
The LMS algorithm has the advantages that, for example, when the input signal is a stationary signal, the convergence of the algorithm is good; the computational complexity is low compared with other adaptive algorithms; the expected value can be converged to a wiener solution without bias; and stability in algorithm realization by using limited precision and the like.
Principle of T-FFT based demodulation: as shown in figures 6 and 7 of the drawings,
let the received signal of the mth path be modeled as:
Figure BDA0002553436550000061
after removing the cyclic prefix, the signal passing through the mth path can be represented as:
Figure BDA0002553436550000062
wherein the content of the first and second substances,
Figure BDA0002553436550000063
representing the channel coefficient, wm(t) is equivalent noise.
In conventional OFDM systems, the path gain and delay are almost constant over the block duration, i.e. the channel coefficients
Figure BDA0002553436550000064
However, in a fast changing underwater acoustic channel, it is likely that the characteristics of the channel have changed within the duration of the same OFDM symbol, and in this case, if the equalization of all data in one symbol is completed by using the estimation of one channel, a large error occurs, and the channel equalization effect is very poor. A T-FFT based demodulation scheme is therefore proposed herein, in which a taylor polynomial and FFT are used to demodulate OFDM symbols, the principle of which is as follows.
In the conventional OFDM demodulation, FFT is performed on a received signal, and the demodulated signal is:
Figure BDA0002553436550000065
according to the Maximum Likelihood (ML) principle, an optimal receiver is established, and the processing result of a receiving end is as follows:
Figure BDA0002553436550000066
due to the correlation of the multipath fading channel, it can be approximated as a set of smoothly varying channel functions, and the channel coefficients are decomposed into a set of known functions to compute a channel matched filter, expressed as:
Figure BDA0002553436550000067
then equation (4-5) can be expressed as:
Figure BDA0002553436550000068
wherein the content of the first and second substances,
Figure BDA0002553436550000069
generally, when the channel is unknown, the channel needs to be estimated first and then channel equalization is performed to demodulate data. But in the T-FFT based demodulation method, a combiner for adaptively determining weights is implemented without or with only a small amount of channel information. That is, the weight of the combiner corresponding to the kth carrier and the kth receiving original can be expressed by an adaptive algorithm
Figure BDA00025534365500000610
A corresponding input vector may be defined:
Figure BDA0002553436550000071
the output of the combiner is represented as:
Figure BDA0002553436550000072
the length L of the combiner is larger than or equal to I, when I is equal to 1, the combiner is a traditional FFT demodulation structure, only one demodulator and an equalizer with the size of L are needed, and when L is equal to I, the combiner corresponds to a single-tap demodulator.
Demapping and decoding:
the constellation mapping refers to a method of modulating input data in one time, converting single serial data into a complex number with amplitude and phase information, and selecting a QPSK modulation method, wherein the modulation method includes multiple BPSK, QPSK, QAM, and the like. The constellation diagram of the receiving end is shown in fig. 10, and the complex data after signal processing is to be modulated into serial data composed of 0 and 1 in a corresponding demapping process at the receiving end. In order to improve the communication quality, the transmitted signal is convolutionally encoded by grouping the input information bits during the encoding of the convolutional code, the encoded output bits of each block being associated not only with the information bits of the group but also with the information bits of other groups at the previous time. Similarly, in the decoding process of the convolutional code, decoding information is acquired from the packet received at the current moment, and related information is extracted from the packets related before and after, so that the error correction capability can be greatly improved.

Claims (5)

1. A Taylor-FFT demodulation method based on underwater acoustic communication is characterized by comprising the following steps:
step 1: at a receiving end, firstly, a training sequence is used for synchronization, and an OFDM symbol is obtained after the training sequence and a cyclic prefix are removed;
step 2: performing pre-FFT processing on the OFDM symbols;
and step 3: then, the result of the pre-FFT processing in the step 2 is processed by FFT to use a combiner to carry out weighted summation of FFT output;
and 4, step 4: and 3, performing demapping processing on the weighted and summed OFDM symbols in the step 3.
2. The Taylor-FFT demodulation method based on underwater acoustic communication in claim 1, wherein the pre-FFT processing in step 2 is performed by using Schmidt orthogonalization to obtain a set of orthogonal Taylor polynomials,
Figure FDA0002553436540000011
in the formula (I), the compound is shown in the specification,
Figure FDA0002553436540000012
for coefficients of taylor polynomials of different powers,
Figure FDA0002553436540000013
are coefficients of taylor polynomials.
3. The Taylor-FFT demodulation method based on underwater acoustic communication according to claim 1, wherein the step 3 is specifically to input the preprocessed OFDM symbols into an adaptive random gradient algorithm, and obtain the weighting factors of the sub-carriers converging the adaptive gradient algorithm by using the adaptive gradient algorithm.
4. The Taylor-FFT demodulation method based on underwater acoustic communication as claimed in claim 3, wherein the adaptive random gradient algorithm represents the weight of the combiner corresponding to the kth carrier and the kth receiving element
Figure FDA0002553436540000014
Define the corresponding input vector:
Figure FDA0002553436540000015
in the formula (I), the compound is shown in the specification,
Figure FDA0002553436540000016
input signals of a k-1 carrier wave of an m receiving unit;
the output of the combiner is represented as:
Figure FDA0002553436540000017
in the formula (I), the compound is shown in the specification,
Figure FDA0002553436540000018
is the input of the combiner and is the output of the combiner,
Figure FDA0002553436540000019
for the weights obtained by the adaptive random gradient,
Figure FDA00025534365400000110
the length L of the combiner is larger than or equal to I.
5. The Taylor-FFT demodulation method based on underwater acoustic communication as claimed in claim 3, wherein the Least Mean Square (LMS) algorithm of the adaptive stochastic gradient algorithm first reduces the correlation between each input time vector, and when the vectors are orthogonal to each other, the algorithm will obtain the fastest convergence rate.
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Application publication date: 20201103