CN109194596A - A kind of underwater sound OFDM time-varying channel estimation method based on management loading - Google Patents
A kind of underwater sound OFDM time-varying channel estimation method based on management loading Download PDFInfo
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Abstract
The present invention relates to a kind of underwater sound OFDM time-varying channel estimation method based on management loading, comprising the following steps: step 1: input channel estimates parameter, comprising: receives symbolic vector YP, dictionary matrix Φp, maximum number of iterations rmax, terminate thresholding e and noise variance σ2；Step 2: initialization hyper parameter matrix Γ and iteration count r；Step 3: hyper parameter γ is solved using expectation-maximization algorithm；Step 4: stopping criterion for iteration judgement, if r < rmaxAndEnable r=r+1, return step three；If r < rmaxAndThen terminate iteration；If r >=rmax, then iteration is terminated.Step 5: output estimation parameter, including the estimation of condition of sparse channel vector and the estimation of hyper parameter vector.The advantage of the invention is that the method increase the precision of channel estimation compared with existing CS method, the bit error rate of system is reduced, in actual underwater sound ofdm communication system, there is practical application value.
Description
Technical Field
The invention relates to an underwater sound OFDM time-varying channel estimation method, in particular to an underwater sound OFDM time-varying channel estimation method based on sparse Bayesian learning, and belongs to the field of underwater sound communication.
Background
Oceans occupy most of the total surface area of the earth, vast oceans contain a large amount of undeveloped wealth, and the development process of ocean resources cannot be supported by underwater communication technology. In recent years, Orthogonal Frequency Division Multiplexing (OFDM) technology has been widely used in underwater communication systems due to its high spectrum utilization and frequency selective fading resistance. However, the underwater acoustic channel has serious noise interference and severely limited bandwidth, is a time-varying and frequency-varying fading channel, and for the OFDM system, how to accurately estimate the underwater acoustic channel is still a hot point of research.
Compared with a Compressed Sensing (CS) method, the method provided by the invention reduces convergence errors in a sparse signal reconstruction process and improves the precision of channel estimation. The method designs a channel estimator based on SBL, utilizes a sparse structure of the channel to independently process the signal block by block, verifies the effectiveness of the method under an underwater acoustic time-varying channel through performance simulation and sea test data processing, and simultaneously proves that the method has lower system error rate compared with the existing CS method (such as Orthogonal Matching Pursuit (OMP)).
Disclosure of Invention
Aiming at the prior art, the invention aims to provide an underwater sound OFDM time-varying channel estimation method based on sparse Bayesian learning, which can improve the channel estimation accuracy of an underwater sound OFDM system.
In order to solve the technical problem, the invention provides an underwater sound OFDM time-varying channel estimation method based on sparse Bayesian learning, which comprises the following steps:
the method comprises the following steps: inputting channel estimation parameters, including: receiving a symbol vector YPDictionary matrix phipMaximum number of iterations rmaxA termination threshold e and a noise variance σ2；
Step two: initializing a hyper-parameter matrix gamma and an iteration count r;
step three: solving the hyperparameter gamma by adopting an expectation maximization algorithm;
step four: judging the iteration termination condition, if r is less than rmaxAnd isMaking r be r +1, and returning to the step three; if r is less than rmaxAnd isThe iteration is terminated;if r is greater than or equal to rmaxThen the iteration is terminated;
step five: and outputting estimation parameters including sparse channel vector estimation and hyperparametric vector estimation.
The invention relates to an underwater sound OFDM time-varying channel estimation method based on sparse Bayesian learning, which further comprises the following steps:
1. in the second step, the initialized hyper-parameter matrix gamma meets the following conditions: gamma-shaped(0)＝ILThe initialization iteration count r satisfies: r is 0, wherein ILIs an L × L identity matrix.
2. The expectation-maximization algorithm in the third step comprises a step E and a step M, wherein the step E satisfies the following conditions:
wherein the M step satisfies:
3. sparse channel vector estimation in step fiveThe hyper-parameter vector is estimated as gamma.
4. Variance of noise σ2Satisfies the following conditions:
σ2＝E[|Yn|2]
wherein, YnTo receive null carriers.
The invention has the beneficial effects that: compared with the existing CS method, the method has the advantages of improving the precision of channel estimation, reducing the error rate of the system and having practical application value in the practical underwater sound OFDM communication system.
Drawings
FIG. 1 is a comparison graph of SNR-RMS performance in simulation of the present invention method and LS channel estimation method and OMP channel estimation method.
FIG. 2 is a comparison graph of SNR-BER performance in simulation of the present invention method, LS channel estimation method and OMP channel estimation method.
FIG. 3 shows the comparison result of the effective noise variance when the method of the present invention and the OMP channel estimation method process actual data.
FIG. 4 is a comparison graph of error rate performance when the method of the present invention and the OMP channel estimation method process actual data.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
Compared with a compressed sensing method, the method reduces convergence errors in the sparse signal reconstruction process, improves the channel estimation precision and reduces the error rate of the system.
The following is detailed according to the basic underwater acoustic OFDM communication system model, the SBL-based channel estimation method, the simulation performance analysis and the sea test data processing:
1. basic underwater sound OFDM communication system model
The invention considers a CP-OFDM system, and assumes that an OFDM block has K subcarriers in total, wherein K is containeddIndividual data sub-carriers, KpA pilot subcarrier, KnThe null carriers, pilot subcarriers and null subcarriers are uniformly distributed. Then the symbol transmitted at the k-th subcarrier is Xk. Defining one OFDM block period as T and cyclic prefix length as Tcp. Will f iscDefined as the center frequency, the k-th subcarrier frequency is
fk＝fc+k/T,k＝-K/2,…,K/2-1. (1.1)
The transmitted OFDM signal can be written as
Wherein q (t) is a pulse shaping filter written as
For the underwater acoustic sparse time-varying channel model, assuming there are L paths, the channel impulse response can be represented as
Wherein A isl，τlRespectively, the amplitude and the time delay of the ith path, and a represents the doppler factor of the path. Assuming that the path gain and doppler factor are constant within an OFDM block, block-to-block; the path delay remains stable over several consecutive OFDM blocks.
OFDM signals received via a channel can be written as
WhereinIs additive noise.
Representing the received signal after Doppler compensation and CP-OFDM demodulation as
Y＝XFh+W (1.6)
Wherein F is a K multiplied by L discrete Fourier transform matrix, X is a K multiplied by K diagonal matrix composed of K transmission symbols, W is additive Gaussian noise, and h is [ h ]1,h2,…,hL]TRepresenting the entire channel.
The system model can be written if only P pilot subcarriers are considered
YP＝XPFPh+WP(1.7)
Wherein, YPIs receiving a pilot symbol, XPIs KpK consisting of one transmitted pilotp×KpOrder diagonal matrix, FPIs the matrix of rows in the F matrix where the corresponding pilots are located, WPIs gaussian noise at the pilot location.
2. Underwater sound sparse time-varying channel estimation based on SBL
Equation (1.7) can be written as
YP＝ΦPh+WP(2.1)
Wherein phiP＝XPFPIs a known dictionary matrix and the main task is to estimate a sparse vector h with most elements being zero.
In the SBL algorithm, the channel model is assumed to satisfyWherein Γ is γ ═ γ [ γ ]1,γ2,…,γL]TThe diagonal matrix of (a). If gamma isi→0,i∈[1,L]Then h isi→ 0. The conditional probability density function of the prior parameter in the SBL algorithm can be written as
γ included in the matrix Γ is a hyperparameter, each hyperparameter controlling the variance of a corresponding channel coefficient. The hyperparameter γ can be solved using a class II Maximum Likelihood (ML) estimation, i.e.
The hyperparameter can also be solved by adopting an expectation-maximization (EM) algorithm, the EM algorithm obtains the hyperparameter in an iterative mode, and the E step and the M step in the r iteration are
E, step E:
and M:
the calculation of equation (2.4) requires a posterior probability density of the sparse vector consisting of hyper-parameters, i.e.
Wherein,using the posterior probability density, a maximum posterior probability (MAP) estimate of the sparse channel vector can be obtained at the end of an EM algorithm iteration, i.e.The M step can be simplified to
Wherein(i,i)Is the ith diagonal component of Σ, μiIs the ith component of μ.
The method comprises the following specific steps:
(1) inputting: receiving a symbol vector YPDictionary matrix phipMaximum number of iterations rmaxTermination threshold e, noise variance σ2。
(2) Initialization: hyper-parametric matrix gamma(0)＝ILThe iteration count r is 0.
(3) E, step E:
(4) and M:
(5) and (3) iteration termination judgment: if r < rmaxReturning to step (3) when r is r + 1; or ifThe iteration is terminated.
(6) And (3) outputting: estimated sparse channel vectorThe estimated hyper-parametric vector gamma.
It is noted that the noise variance σ2The following are determined by the idler:
σ2＝E[|Yn|2](2.13)
whereinYnIndicating reception of null carrier symbols. And finishing the channel estimation based on the sparse Bayesian learning algorithm through the steps.
3. Simulation performance analysis
In order to verify the performance of the channel estimation method, an underwater sound OFDM system is built, and the underwater sound OFDM system comprises 256 subcarriers, wherein the data subcarriers Kd200 pilot subcarriers Kp32, no carrier wave Kn24, bandwidth B1.5 kHz, center frequency fc2.25kHz, sample rate fs12kHz, signal length T171 ms, cyclic prefix TcpOne frame signal contains 4 OFDM blocks for 10 ms. The underwater sound sparse time-varying channel model adopts 10 randomly generated paths, the delay interval follows exponential distribution with the average value of 0.5ms, the Doppler factor of each OFDM block is assumed to be randomly changed, and the range is [ -v ]p/c,vp/c]Wherein the relative velocity vp1.5m/s and the speed of sound in water c 1500m/s, the path amplitude follows a rayleigh distribution with path delay. With QPSK modulation, 1/2 non-binary LDPC coding.
In the simulation, an LS channel estimation method, an OMP algorithm-based channel estimation method and the channel estimation method of the invention are respectively adopted for comparison.
Fig. 1 is a comparison graph of signal-to-noise ratio-Mean Square Error (MSE) performance of the method of the present invention, the LS channel estimation method, and the OMP channel estimation method. It can be seen from the simulation that the mean square error performance of the LS channel estimation method is the worst, and the MSE performance of the SBL channel estimation method is about 2dB better than the performance of the OMP channel estimation method, which indicates that the channel estimation accuracy of the SBL channel estimation algorithm is higher.
Fig. 2 is a comparison graph of signal-to-noise ratio-Bit Error Rate (BER) performance of the inventive method and the LS channel estimation method and the OMP channel estimation method. It can be seen that the BER performance of the LS channel estimation method is still the worst, and the BER performance of the OMP channel estimation method is about 0.5dB worse than that of the SBL channel estimation method, which indicates that the system performance using the SBL channel estimation algorithm is better.
4. Sea test data processing analysis
The algorithm was further verified using experimental data obtained 2014 in south China sea. The transmitting transducers were spaced about 5km apart, with a transmitting transducer depth of 27m and a receiving transducer depth of 30 m.
One OFDM symbol contains K681 subcarriers, where the data subcarrier Kd571 pilot subcarriers Kp86, no carrier wave Kn24, bandwidth B4 kHz, center frequency fc8kHz, sample rate fs48kHz, signal length T170 ms, cyclic prefix TcpOne frame signal contains 8 OFDM blocks for 20 ms. QPSK modulation and convolutional code coding are adopted. 12 frames of OFDM symbols are transmitted consecutively with a time interval of 2s between each frame. The LFM signal is set before each frame of signal for synchronization.
The performance of the sea trial data processing channel estimation is evaluated by introducing the effective noise variance, which is defined as follows
Wherein,is obtained by fourier transformation of the estimated sparse channel. This value includes the error of the channel estimate, the ambient noise and the residual doppler shift.
Fig. 3 shows the comparison of the effective noise variance between the method of the present invention and the OMP channel estimation method. As can be seen, the effective noise variance of the SBL channel estimation method is lower than that of the OMP algorithm when processing actual data.
Fig. 4 is a graph comparing BER performance of the method of the present invention and OMP channel estimation. It can be seen that the BER curve of the OMP channel estimation method is higher than that of the SBL channel estimation method, which indicates that the bit error rate of the SBL algorithm is still lower than that of the OMP algorithm when actual data is processed.
The invention designs a channel estimator based on SBL, processes the signals block by using the sparse structure of the channel, solves the hyperparameter by adopting an Expectation Maximization (EM) algorithm, and realizes the estimation of the underwater sound time-varying sparse channel.
The specific implementation mode of the invention also comprises:
the method for solving the technical problem comprises the following steps:
(1) inputting channel estimation parameters, including: receiving a symbol vector, a dictionary matrix, a maximum number of iterations, a termination threshold, and a noise variance.
(2) Initializing, including: and initializing a hyper-parameter matrix and initializing iteration count.
(3) And solving the hyper-parameters by adopting an EM algorithm.
(4) Judging iteration termination conditions, and if the conditions are met, terminating the iteration; and if not, returning to the step (3).
(5) And outputting estimation parameters including sparse channel vector estimation and hyperparametric vector estimation.
Claims (5)
1. An underwater sound OFDM time-varying channel estimation method based on sparse Bayesian learning is characterized by comprising the following steps:
the method comprises the following steps: inputting channel estimation parameters, including: receiving a symbol vector YPDictionary matrix phipMaximum number of iterations rmaxA termination threshold e and a noise variance σ2；
Step two: initializing a hyper-parameter matrix gamma and an iteration count r;
step three: solving the hyperparameter gamma by adopting an expectation maximization algorithm;
step four: judging the iteration termination condition, if r is less than rmaxAnd isMaking r be r +1, and returning to the step three; if r is less than rmaxAnd isThe iteration is terminated; if r is greater than or equal to rmaxThen the iteration is terminated;
step five: and outputting estimation parameters including sparse channel vector estimation and hyperparametric vector estimation.
2. The underwater acoustic OFDM time-varying channel estimation method based on sparse Bayesian learning as recited in claim 1, wherein: in the second step, the initialized hyper-parameter matrix gamma meets the following conditions: gamma-shaped(0)＝ILThe initialization iteration count r satisfies: r is 0, wherein ILIs an L × L identity matrix.
3. The underwater acoustic OFDM time-varying channel estimation method based on sparse Bayesian learning as recited in claim 1, wherein: the expectation-maximization algorithm in step three comprises a step E and a step M, wherein the step E satisfies the following conditions:
wherein the M step satisfies:
4. the sparse-based bayes as in claim 1The underwater sound OFDM time-varying channel estimation method of the learning is characterized in that: step five the sparse channel vector estimationThe hyper-parameter vector is estimated as gamma.
5. The underwater acoustic OFDM time-varying channel estimation method based on sparse Bayesian learning as recited in claim 1, wherein: the noise variance σ2Satisfies the following conditions:
σ2＝E[|Yn|2]
wherein, YnTo receive null carriers.
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Cited By (12)
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| CN110082761A (en) * | 2019-05-31 | 2019-08-02 | 电子科技大学 | Distributed external illuminators-based radar imaging method |
| CN110336761A (en) * | 2019-07-12 | 2019-10-15 | 电子科技大学 | The beam space channel estimation methods of the extensive mimo system of millimeter wave |
| CN111147407A (en) * | 2019-12-31 | 2020-05-12 | 哈尔滨哈船海洋信息技术有限公司 | TMSBL underwater acoustic OFDM time-varying channel estimation method based on channel prediction |
| CN111245751A (en) * | 2020-01-10 | 2020-06-05 | 北京星河亮点技术股份有限公司 | Partition matrix iteration method and system for sparse Bayesian learning channel estimation |
| CN111277522A (en) * | 2020-01-23 | 2020-06-12 | 青岛科技大学 | Method for quickly reconstructing channel parameters in underwater acoustic OFDM communication system |
| CN111525955A (en) * | 2020-04-13 | 2020-08-11 | 青岛大学 | Visible light communication balancing method and system based on sparse Bayesian learning |
| CN111666688A (en) * | 2020-06-09 | 2020-09-15 | 太原科技大学 | Corrected channel estimation algorithm combining angle mismatch with sparse Bayesian learning |
| CN112104580A (en) * | 2020-09-11 | 2020-12-18 | 中海石油(中国)有限公司湛江分公司 | Sparse underwater acoustic channel estimation method based on generalized approximate message transfer-sparse Bayesian learning |
| CN113098801A (en) * | 2021-03-16 | 2021-07-09 | 华中科技大学 | Channel estimation method and system for underwater sound OFDM system precision-complexity joint optimization |
| CN113872895A (en) * | 2021-10-21 | 2021-12-31 | 武汉中科海讯电子科技有限公司 | Underwater channel estimation method based on multi-task Bayes compressed sensing |
| CN115695105A (en) * | 2023-01-03 | 2023-02-03 | 南昌大学 | Channel estimation method and device based on deep iteration intelligent super-surface auxiliary communication |
| CN115694688A (en) * | 2022-10-28 | 2023-02-03 | 中国科学技术大学 | Intelligent reflector auxiliary communication system channel estimation method, equipment and storage medium |
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| CN110082761A (en) * | 2019-05-31 | 2019-08-02 | 电子科技大学 | Distributed external illuminators-based radar imaging method |
| CN110336761A (en) * | 2019-07-12 | 2019-10-15 | 电子科技大学 | The beam space channel estimation methods of the extensive mimo system of millimeter wave |
| CN110336761B (en) * | 2019-07-12 | 2021-04-02 | 电子科技大学 | Wave beam space channel estimation method of millimeter wave large-scale MIMO system |
| CN111147407A (en) * | 2019-12-31 | 2020-05-12 | 哈尔滨哈船海洋信息技术有限公司 | TMSBL underwater acoustic OFDM time-varying channel estimation method based on channel prediction |
| CN111147407B (en) * | 2019-12-31 | 2022-09-09 | 哈尔滨哈船海洋信息技术有限公司 | TMSBL underwater acoustic OFDM time-varying channel estimation method based on channel prediction |
| CN111245751A (en) * | 2020-01-10 | 2020-06-05 | 北京星河亮点技术股份有限公司 | Partition matrix iteration method and system for sparse Bayesian learning channel estimation |
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| CN111666688A (en) * | 2020-06-09 | 2020-09-15 | 太原科技大学 | Corrected channel estimation algorithm combining angle mismatch with sparse Bayesian learning |
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| CN112104580A (en) * | 2020-09-11 | 2020-12-18 | 中海石油(中国)有限公司湛江分公司 | Sparse underwater acoustic channel estimation method based on generalized approximate message transfer-sparse Bayesian learning |
| CN112104580B (en) * | 2020-09-11 | 2023-07-21 | 中海石油(中国)有限公司湛江分公司 | Sparse underwater acoustic channel estimation method based on generalized approximate message transfer-sparse Bayesian learning |
| CN113098801A (en) * | 2021-03-16 | 2021-07-09 | 华中科技大学 | Channel estimation method and system for underwater sound OFDM system precision-complexity joint optimization |
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| CN113872895B (en) * | 2021-10-21 | 2023-12-12 | 武汉中科海讯电子科技有限公司 | Underwater channel estimation method based on multitasking Bayes compressed sensing |
| CN115694688A (en) * | 2022-10-28 | 2023-02-03 | 中国科学技术大学 | Intelligent reflector auxiliary communication system channel estimation method, equipment and storage medium |
| CN115695105A (en) * | 2023-01-03 | 2023-02-03 | 南昌大学 | Channel estimation method and device based on deep iteration intelligent super-surface auxiliary communication |
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Application publication date: 20190111 |