CN114785644A - Mobile underwater acoustic OTFS communication sparse channel estimation method - Google Patents
Mobile underwater acoustic OTFS communication sparse channel estimation method Download PDFInfo
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Abstract
The invention discloses a mobile underwater sound OTFS communication sparse channel estimation method, which comprises the following steps: at a transmitting end of the underwater acoustic OTFS system, information bit stream a is encoded by a recursive systematic convolutional code with code rate of 1/2 to obtain a coded bit vector b; the coded bit vector b is interleaved by a random interleaver to obtain an interleaved and coded vector c; c, grouping and obtaining a symbol vector X, performing serial-parallel conversion on the X to obtain a two-dimensional data matrix X of K rows and L columns to be modulated, wherein elements in the matrix are represented by X (K, L); and then carrying out inverse octyl Fourier transform on the data matrix to obtain X (n, m), then obtaining a time domain transmitting signal X (t) through Heisenberg transform, finally carrying out modulation to a specified transmitting frequency band, and transmitting out through a underwater acoustic transducer. The invention uses the MMP algorithm to estimate the two-dimensional sparse time delay-Doppler channel parameters, thereby improving the communication performance of the communication system.
Description
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a mobile underwater acoustic OTFS communication sparse channel estimation method.
Background
Orthogonal Frequency Division Multiplexing (OFDM) modulation schemes have been widely used in the field of underwater acoustic communications. However, the underwater acoustic channel faces severe multipath spreading and doppler spreading, and the conventional OFDM technology cannot achieve good communication performance when directly applied to the underwater acoustic communication, mainly because the large doppler shift destroys the orthogonality among the sub-carriers, thereby causing severe Inter-Carrier Interference (ICI). In order to improve the performance of the underwater acoustic communication system under the condition of double-spread channels (i.e., multipath spreading and doppler spreading), the underwater acoustic communication modem technology needs to be innovated.
Aiming at the characteristics of the double-spread underwater acoustic channel, an Orthogonal Time Frequency Space (OTFS) modulation system converts the Time-varying double-spread underwater acoustic channel into a Time-invariant two-dimensional channel in a two-dimensional delay-Doppler domain through a series of two-dimensional transformation, and multiplexes data in the delay-Doppler domain, so that the problem of the Time-varying double-spread channel can be effectively solved. The underwater acoustic channel often has certain sparsity in a time delay-Doppler domain, and in order to better utilize the sparsity, a multipath matching pursuit based algorithm is provided for estimating the sparse two-dimensional time delay-Doppler underwater acoustic channel, and the algorithm can improve the performance of an underwater acoustic OTFS communication system.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to solve the problem of sparse two-dimensional delay-Doppler channel estimation in a mobile underwater acoustic OTFS communication system, and further improve the robustness of the underwater acoustic OTFS system.
The technical scheme is as follows: a mobile underwater acoustic OTFS communication sparse channel estimation method comprises the following steps:
(1) at a transmitting end of the underwater sound OTFS system, information bit stream a is encoded through a recursive system convolutional code with code rate 1/2 to obtain a coded bit vector b; the coded bit vector b is interleaved by a random interleaver to obtain an interleaved and coded vector c; c, grouping and obtaining a symbol vector X, performing serial-parallel conversion on the X to obtain a two-dimensional data matrix X of K rows and L columns to be modulated, wherein elements in the matrix are represented by X (K, L); then, performing Inverse symplectic Fourier Transform (ISFFT) on the data matrix to obtain X (n, m), then obtaining a time domain transmission signal X (t) through Heisenberg Transform (Heisenberg Transform), finally carrying out modulation to a specified transmission frequency band, and transmitting the time domain transmission signal through an underwater acoustic transducer;
(2) transmitting signals reach a receiving hydrophone after passing through a double-spread underwater acoustic channel, receiving passband signals are converted into symbol rate baseband signals Y (t) at a receiving end after frame synchronization, demodulation and down sampling, Y (t) firstly obtains Y (n, m) through Weigner Transform (Wigner Transform), and then obtains Y (k, l) through Simmetic Fine Fourier Transform (SFFT);
(3) extracting a pilot frequency symbol, and estimating a sparse two-dimensional time delay-Doppler channel by a multipath matching tracking algorithm;
(4) equalizing the received signal by using the estimated channel, demodulating and decoding the equalized signal to finally obtain the estimated information bit stream of the transmitted signal
At the transmitting end, placing M × N information data symbols into a delay-doppler domain signal grid, where there are M rows of data in the delay domain and N rows of data in the doppler domain; transforming the delay-Doppler domain signal to the time-frequency domain X (n, m) by ISFFT, i.e.
The time-frequency domain signal is converted into a time domain signal x (t) through Heisenberg. After passing through the time-varying double-spread underwater acoustic channel, at the receiving end, the time-domain received signal Y (t) is converted into a time-frequency domain signal Y (n, m) by the Wigner, and data Y (k, l) of the time delay-Doppler domain is obtained through SFFT processing demodulation, namely
The OTFS communication system through the underwater sound channel can obtain the input-output relation of the OTFS system as
Wherein Q is the number of underwater acoustic multi-channel, n is (k-beta)q)NIs represented by (k-beta)q-N) the value of k when evenly divisible by N, m ═ aq)MIs represented by (l-alpha)p-M) the value of l when divisible by M. Wherein tau isq＝αq/MΔf，vq＝βq/NT，τqRepresenting the amount of time delay, vqRepresenting the amount of doppler frequency shift; m is the number of subcarriers, and N is the number of symbols of a frame; g [ k, l]Representing noise in the delay-doppler domain;
representing the channel amplitude gain, channel gain hqIs composed of
Based on the input-output relationship of the underwater acoustic OTFS communication system, the pilot frequency input-output relationship is obtained, namely
yp＝Xphp+gP
Wherein, ypReceiving vector of size M for pilot signalpNp×1；hpIs a channel gain vector of size MpNp×1；gpIs MpNpA noise vector of size x 1; m is a group ofpDiscrete quantity alpha for maximum time delaymax+1，NpDiscrete quantity beta for maximum doppler shiftmax+1, the number of paths Q is less than or equal to MpNp. Due to the difference in resolution, here
So that the doppler shift is a discrete quantity
And time delay dispersion
The number of all combinations is MpNpA part of (a). XpIs a matrix of pilot symbols of size MpNp×MpNpColumn j (j ═ k + N) of the samepl，j＝0,1,…MpNp-1) is represented by
When the number of paths Q is equal to MpNpTime, matrix XpIs a block circulant matrix with circulant blocks, XpHaving MpA cyclic block of size Np×NpAnd forming a block circulant matrix by cyclic shifting.
Denotes the qth element of the ith block, where q is 0,1, … Np-1, and l ═ 0,1, … Mp-1。
In the sparse channel estimation step, the receiving pilot frequency y is utilizedpAnd transmitting pilot XpI.e. using the multi-path matching tracking algorithm pair hpThe estimation is carried out by the following specific steps:
(3.1) initialization of the algorithm, i.e. let k equal 0, r0＝yp，
hp＝0；
(3.2) carrying out iterative loop until the set maximum iterative times K-1 are reached, and then stopping; updating in an iterative process
And
(3.3) find the optimal set of candidate paths with the smallest recovery residual, i.e.
(3.4) solving the optimal solution, i.e.
Has the beneficial effects that: the invention uses MMP sparse algorithm to estimate two-dimensional time delay-Doppler channel, and improves the channel estimation precision of the double-spread underwater acoustic OTFS communication system. Compared with the traditional sparse channel estimation algorithm, the method can effectively utilize the sparse characteristic of the channel to obtain more accurate two-dimensional channel estimation performance, and improve the performance of the underwater acoustic OTFS communication system.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
As shown in fig. 1, the present invention provides a technical solution: a mobile underwater acoustic OTFS communication sparse channel estimation method comprises the following steps:
(1) at a transmitting end of the underwater sound OTFS system, information bit stream a is encoded through a recursive system convolutional code with code rate 1/2 to obtain a coded bit vector b; the coded bit vector b is interleaved by a random interleaver to obtain an interleaved and coded vector c; c, grouping and obtaining a symbol vector X, performing serial-parallel conversion on the X to obtain a two-dimensional data matrix X of K rows and L columns to be modulated, wherein elements in the matrix are represented by X (K, L); then, inverse octyl Fourier transform is carried out on the data matrix to obtain X (n, m), then a time domain transmitting signal X (t) is obtained through Heisenberg transform, and finally the transmitting signal is modulated and moved to a designated transmitting frequency band and is transmitted out through a underwater acoustic transducer;
(2) transmitting signals generated in the step (1) pass through a time-varying double-spread underwater acoustic channel and then reach a receiving hydrophone, and after frame synchronization, demodulation and down-sampling are carried out on the transmitting signals at a receiving end, receiving passband signals are converted into symbol rate baseband signals Y (t), Y (t) firstly obtains Y (n, m) through Weiganer transform, and then Y (k, l) is obtained through sine Fourier transform;
(3) extracting a pilot frequency symbol, and estimating a sparse two-dimensional time delay-Doppler channel by a multipath matching tracking algorithm;
(4) equalizing the received signal by using the estimated channel, demodulating and decoding the equalized signal to finally obtain the estimated information bit stream of the transmitted signal
Specifically, at a transmitting end, placing M multiplied by N information data symbols into a delay-Doppler domain signal grid, wherein M rows of data exist in a delay domain, and N rows of data exist in a Doppler domain; transforming the time delay-Doppler domain signal into the time-frequency domain X (n, m) by inverse symplectic Fourier transform, i.e.
The time-frequency domain signal is converted into a time domain signal x (t) through Heisebauer transformation.
Specifically, after the transmission signal passes through the time-varying double-spread underwater acoustic channel, at the receiving end, the time-domain received signal Y (t) is converted into a time-frequency domain signal Y (n, m) by wigner transform, and data Y (k, l) of the time delay-doppler domain is obtained through the demodulation of the sine fourier transform, that is, the data Y (k, l) of the time delay-doppler domain is obtained, that is, the transmission signal passes through the time-varying double-spread underwater acoustic channel
The OTFS communication system modulates and demodulates the related transformation to obtain the OTFS system input-output relation through the underwater sound channel
Wherein Q is the number of the underwater acoustic multi-path channels, M is the number of the subcarriers, and N is the number of one frame of symbols; n ═ k-betaq)NRepresents (k-beta)q-N) the value of k when evenly divisible by N, m ═ aq)MRepresents (l-alpha)p-M) the value of l when it can be divided exactly by M, g [ k, l]Representing the noise in the delay-doppler domain,
representing the channel amplitude gain; channel gain hqIs composed of
Wherein tau isq＝αq/MΔf，vq＝βq/NT，τqRepresenting the amount of time delay, vqRepresenting the amount of doppler frequency shift; based on the input-output relationship of the underwater acoustic OTFS communication system, the pilot frequency input-output relationship is obtained, namely
yp＝Xphp+gP
Wherein, ypFor pilot signal reception vector of size MpNp×1；hpIs a channel gain vector of size MpNp×1；gpIs MpNpA noise vector of size x 1; m is a group ofpDiscrete quantity alpha for maximum time delaymax+1，NpFor maximum Doppler shift by discrete amounts betamax+1, the number of paths Q is less than or equal to MpNp(ii) a Due to the difference in resolution, here
Discrete amount of Doppler shift
And time delay dispersion
The number of all combinations is MpNpA part of (a); xpIs a pilot symbol matrix of size MpNp×MpNp。
XpJ (k + N) th column (j ═ k + N)pl，j＝0,1,…MpNp-1) is represented by
When the number of paths Q is equal to MpNpTime, matrix XpIs a block circulant matrix with circulant blocks, XpHaving MpA cyclic block, each cyclic block having a size of Np×NpAnd forming a block circulant matrix by cyclic shifting,
denotes the qth element of the ith block, where q is 0,1, … Np-1, and l ═ 0,1, … Mp-1。
In the step (3), in the sparse channel estimation step, the received pilot frequency y is utilizedpAnd transmit pilot XpNamely, a multi-path matching tracking algorithm can be used to hpThe estimation is carried out by the following specific steps:
(3.1) algorithm initialization, i.e. let k equal 0, r0＝yp，
hp＝0；
(3.2) carrying out iteration circulation until the set maximum iteration number K-1 is reached, and then stopping; updating in an iterative process
And
(3.3) find the optimal set of candidate paths with the smallest recovery residual, i.e.
(3.4) solving the optimal solution, i.e.
Claims (5)
1. A mobile underwater sound OTFS communication sparse channel estimation method is characterized by comprising the following steps:
(1) at a transmitting end of the underwater acoustic OTFS system, information bit stream a is encoded by a recursive systematic convolutional code with code rate of 1/2 to obtain a coded bit vector b; the coded bit vector b is interleaved by a random interleaver to obtain an interleaved and coded vector c; c, grouping and obtaining a symbol vector X, performing serial-parallel conversion on the X to obtain a two-dimensional data matrix X of K rows and L columns to be modulated, wherein elements in the matrix are represented by X (K, L); then inverse octyl Fourier transform is carried out on the data matrix to obtain X (n, m), then a time domain transmitting signal X (t) is obtained through Heisenberg transform, and finally the transmitting signal is modulated and moved to a designated transmitting frequency band and is transmitted out through an underwater acoustic transducer;
(2) transmitting signals generated in the step (1) pass through a time-varying double-spread underwater acoustic channel and then reach a receiving hydrophone, and after frame synchronization, demodulation and down-sampling are carried out on the receiving end, receiving passband signals are converted into symbol rate baseband signals Y (t), Y (t) is firstly subjected to Weiganer transform to obtain Y (n, m), and then Y (k, l) is obtained through octyl Fourier transform;
(3) extracting a pilot frequency symbol, and estimating a sparse two-dimensional delay-Doppler channel by a multi-path matching tracking algorithm;
(4) equalizing the received signal by using the estimated channel, demodulating and decoding the equalized signal to finally obtain the estimated information bit stream of the transmitted signal
2. The method according to claim 1, wherein the step (1) is specifically that at the transmitting end, M × N information data symbols are placed in a delay-doppler domain signal grid, where there are M rows of data in the delay domain and N rows of data in the doppler domain; transforming the time delay-Doppler domain signal into the time-frequency domain X (n, m) by inverse symplectic Fourier transform, i.e.
The time-frequency domain signal is converted into a time domain signal x (t) through Heisebauer transformation.
3. The method as claimed in claim 1, wherein the step (2) is specifically that, after the transmission signal passes through the time-varying double-spread hydroacoustic channel, at the receiving end, the time-domain received signal Y (t) is transformed from wigner to a time-frequency domain signal Y (n, m), and is demodulated by the fourier transform process to obtain the data Y (k, l) of the delay-doppler domain, that is, the data Y (k, l) of the delay-doppler domain is obtained
The OTFS communication system modulates and demodulates the related transformation to obtain the OTFS system input-output relation through the underwater sound channel
Wherein Q is the number of the underwater sound multi-path channels, M is the number of the subcarriers, and N is the number of symbols of one frame; n ═ k-betaq)NIs represented by (k-beta)q-N) the value of k when evenly divisible by N, m ═ aq)MRepresents (l-alpha)p-M) the value of l when it can be divided exactly by M, g [ k, l]Representing the noise in the delay-doppler domain,
representing the channel amplitude gain; channel gain hqIs composed of
Wherein tau isq＝αq/MΔf，vq＝βq/NT，τqRepresenting the amount of time delay, vqRepresenting the amount of doppler frequency shift;
based on the input-output relationship of the underwater acoustic OTFS communication system, the pilot frequency input-output relationship is obtained, namely
yp＝Xphp+gP
Wherein, ypFor pilot signal reception vector of size MpNp×1；hpIs a channel gain vector of size MpNp×1；gpIs MpNpA noise vector of size x 1; m is a group ofpDiscrete quantity alpha for maximum time delaymax+1，NpDiscrete quantity beta for maximum doppler shiftmax+1, the number of paths Q is less than or equal to MpNp(ii) a Due to the difference in resolution, here
Discrete amount of Doppler shift
And time delay dispersion
The number of all combinations is MpNpA part of (a); xpIs a pilot symbol matrix of size MpNp×MpNp。
4. The method for estimating sparse channel in mobile underwater acoustic OTFS communication according to claim 3Characterised by that XpJ (k + N) th column (j)pl，j＝0,1,…MpNp-1) is represented by
When the number of paths Q is MpNpTime, matrix XpIs a block circulant matrix with circulant blocks, XpHaving MpA cyclic block, each cyclic block having a size of Np×NpAnd forming a block circulant matrix through cyclic shift.
5. The method as claimed in claim 1, wherein in step (3), in the sparse channel estimation step, the received pilot y is usedpAnd transmit pilot XpNamely, a multi-path matching tracking algorithm can be used to hpThe estimation is carried out by the following specific steps:
(3.1) algorithm initialization, i.e. let k equal 0, r0＝yp，
hp＝0；
(3.2) carrying out iteration circulation until the set maximum iteration number K-1 is reached, and then stopping; updating in an iterative process
And
(3.3) find the optimal set of candidate paths with the smallest recovery residual, i.e.
(3.4) solving the optimal solutionI.e. by
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