CN113872895B - Underwater channel estimation method based on multitasking Bayes compressed sensing - Google Patents
Underwater channel estimation method based on multitasking Bayes compressed sensing Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/0202—Channel estimation
- H04L25/024—Channel estimation channel estimation algorithms
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- H—ELECTRICITY
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- 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
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Abstract
The invention discloses an underwater channel estimation method based on multi-task Bayes compressed sensing, which utilizes wavelet decomposition estimation to obtain initial sparsity of channels, and adopts a multi-task Bayes algorithm to solve so as to obtain estimation values of all channels. Compared with the prior art, the invention can realize the self-adaptive adjustment of the channel estimation algorithm in the unknown underwater acoustic environment, and accurately estimate the state information of the channel by combining the self-adaptive thought of the existing compressed sensing self-adaptive algorithm; but also can estimate the transmitting signal passing through the underwater acoustic channel, so that the influence of the self transmitting transducer on the system can be reduced by a channel estimation mode.
Description
Technical Field
The invention relates to the field of underwater acoustic communication, in particular to an underwater channel estimation method based on multitasking Bayesian compressed sensing.
Background
Currently, underwater channel estimation usually adopts a pilot training insertion mode. Conventional channel estimation methods generally employ a least squares algorithm (LMS), since the least squares algorithm is easily interfered by noise, and inter-subcarrier interference also reduces the performance of the LMS algorithm. ZHAO Y et al propose a pilot noise reduction method using a transform domain filter, zhang Xiaoli et al propose a compressed sensing-based adaptive sparse underwater acoustic channel estimation algorithm, which combines a compressed sensing algorithm OMP/CoSaMP with a hard threshold LMS algorithm to estimate the underwater acoustic channel impulse response; wang Yonggang et al propose an OFDM underwater communication channel estimation based on pilots, which improves the training pilots, regards the underwater acoustic channel as a comb filter, and improves the inter-subcarrier interference problem by using an improved transform domain filter pilot noise reduction method. However, these algorithms require the sparse nature of the known channels, and in practical underwater environments, because the complex underwater space-time propagation channels generally do not satisfy the sparsity, sparse-class algorithms are not easily adopted.
In addition, the underwater duplex communication system performs receiving and transmitting operations simultaneously. The received data becomes very weak after long-distance attenuation; and its own transmit signal, a high power amplifier is typically required to drive the transducer transmit signal in order to be able to travel further distances. Thus, the received data is affected by the own transmitted signal.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides an underwater channel estimation method based on multi-task Bayes compressed sensing.
In order to achieve the above purpose, the invention is implemented according to the following technical scheme:
an underwater channel estimation method based on multitasking Bayesian compressed sensing comprises the following steps:
s1, assuming that the MIMO system has J receiving transducers and P transmitting transducers, and assuming that the data received by the receiving transducers isThe P transmitting transducers transmit data s p ＝(s p,0 ,s p,1 ,...,s p,L-1 ) T ,p∈[P]The channel from the p-th transmitting transducer to the j-th receiving transducer is +.>L is the channel length, then the data with the j-th received array element is:
wherein,is noise (I)>Is a matrix generated from the transmitted data as follows:
let Φ= (S 0 ,S 1 ,...S P-1 ) The following steps are:
x j ＝φh p，j +n j ；
s2, solving by adopting a multi-task Bayesian algorithm to obtain estimated values of all channels.
Further, the step S2 specifically includes:
s21, constructing a mapping matrix Φ= (S 0 ,S 1 ,...S P-1 )＝Φ 1 ＝Φ 2 ＝...＝Φ J ；
S22, define phi i ＝(Φ i,1 ,Φ i,2 ,...Φ i,M×P )，Σ i,(j,j) Is sigma i Elements of row j and column j;
s23, while converges;
s24, updating
S25, updating
S26 update a=diag (α) 1 ,α 2 ,...,α M×P )；
S27, updatingj=1, 2,..j, where because all mapping matrices are the same, only one update is needed;
s28, updatingi=1, 2, ·j, j=1, 2, ·m×p, where the update for i is calculated only once because all mapping matrices are the same;
s29, updatingi=1, 2, ·j, j=1, 2, ·m×p here because all mapping matrices are the same, only one calculation for the update of i is needed;
s210, updatei=1, 2, ·j, j=1, 2, ·m×p here because all mapping matrices are the same, only one calculation for the update of i is needed;
s211, updating
S212, outputting the estimated value of the channel.
Compared with the prior art, the method utilizes wavelet decomposition estimation to obtain the initial sparsity of the channel, and combines the self-adaptive thought of the existing compressed sensing self-adaptive algorithm to accurately estimate and obtain the state information of the channel; but also can estimate the transmitting signal passing through the underwater acoustic channel, so that the influence of the self transmitting transducer on the system can be reduced by a channel estimation mode.
Drawings
Fig. 1 shows the channel simulation estimation results of the ucembco and LMS method estimation of simulation example 1.
Fig. 2 shows the error of the channel estimated by the ucembco method and the LMS method of simulation example 2.
Fig. 3 shows the error of the channel estimated by the ucembco method and the LMS method of simulation example 3.
Fig. 4 shows the error of the channel estimated by the ucembco method and the LMS method of simulation example 4.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. The specific embodiments described herein are for purposes of illustration only and are not intended to limit the invention.
The embodiment provides an underwater channel estimation method based on multi-task Bayes compressed sensing, which comprises the following steps:
an underwater channel estimation method based on multitasking Bayesian compressed sensing comprises the following steps:
s1, assuming that the MIMO system has J receiving transducers and P transmitting transducers, and assuming that the data received by the receiving transducers isThe P transmitting transducers transmit data s p ＝(s p,0 ,s p,1 ,...,s p,L-1 ) T ,p∈[P]The channel from the p-th transmitting transducer to the j-th receiving transducer is +.>L is the channel length, then the data with the j-th received array element is:
wherein,is noise (I)>Is a matrix generated from the transmitted data as follows:
let Φ= (S 0 ,S 1 ,...S P-1 ) The following steps are:
X j ＝φh p，j +n j ；
s2, solving by adopting a multi-task Bayesian algorithm to obtain estimated values of all channels.
Further, the step S2 specifically includes:
s21, constructing a mapping matrix Φ= (S 0 ,S 1 ,...S P-1 )＝Φ 1 ＝Φ 2 ＝...＝Φ J ；
S22, define phi i ＝(Φ i,1 ,Φ i,2 ,...Φ i,M×P )，Σ i,(j,j) Is sigma i Elements of row j and column j;
s23, while converges;
s24, updating
S25, updating
S26 update a=diag (α) 1 ,α 2 ,...,α M×P )；
S27, updatingj=1, 2,..j, where because all mapping matrices are the same, only one update is needed;
s28, updatingi=1, 2, ·j, j=1, 2, ·m×p, where the update for i is calculated only once because all mapping matrices are the same;
s29, updatingi=1, 2, ·j, j=1, 2, ·m×p here because all mapping matrices are the same, only one calculation for the update of i is needed;
s210, updatei=1, 2, ·j, j=1, 2, ·m×p here because all mapping matrices are the same, only one calculation for the update of i is needed;
s211, updating
S212, outputting the estimated value of the channel.
Further, in order to verify that the invention can realize higher-precision channel estimation, the following simulation experiment is performed:
simulation example 1
A model with single input and single output is set, the transmitted signal is a random signal, the signal length is 1024, and Gaussian white noise with 5dB signal-to-noise ratio is added. The channel length is set to 200. The LMS algorithm is adopted as comparison, and the simulation result is shown in figure 1. As can be seen from fig. 1, the channel estimated by the method of the present invention ucembco is more accurate than the LMS.
Simulation example 2
Let us assume a 1-in 1-out signal model in which the length of the channel is set to 200, the length of the transmission signal per frame is 1024, the transmission signal is a random signal, and 0dB of gaussian white noise is added. And transmitting 50 frames of signals in total, and observing error conditions of channel estimation results.
Because of the longer length of a frame, both algorithms have converged in the computation of the first frame. The absolute error of the method of Lms is about 9 and the absolute error of two ucemmco is less than 1. As shown in FIG. 2, the simulation result shows that the error of the method ucemmbco is far lower than that of the lms method according to the invention, and a more accurate channel estimation result is obtained.
Simulation example 3
Let us assume a 1-in-1-out signal model in which the length of the channel is set to 200 and the length of the transmission signal per frame is 512 and the transmission signal is a random signal. The signal to noise ratio is set to-20 dB to 20dB, with one calculation per 1dB interval. 50 simulations were performed at each snr setting, and the error averaged.
As shown in FIG. 3, as can be seen from FIG. 3, the estimation error of the method of the present invention, ucembco, is always smaller than lms in the signal-to-noise ratio interval of [ -20,20] dB. The method of the invention is always superior to the lms method, the estimation error of the method of the invention is rapidly reduced along with the increase of the signal-to-noise ratio, and the method of the invention can obtain better results in the actual environment.
Simulation example 4
Let us assume a 2-in 2-out signal model in which the length of the channel is set to 20, the length of the transmit signal is 1024, the transmit signal is a random signal, and 0dB of white gaussian noise is added. The observation simulation results are shown in fig. 4. The results of fig. 4 show that the ucembco of the present invention gives very accurate estimation results for all channels.
The technical scheme of the invention is not limited to the specific embodiment, and all technical modifications made according to the technical scheme of the invention fall within the protection scope of the invention.
Claims (1)
1. An underwater channel estimation method based on multitasking Bayesian compressed sensing is characterized by comprising the following steps:
s1, assuming that the MIMO system has J receiving transducers and P transmitting transducers, and assuming that the data received by the receiving transducers isThe P transmitting transducers transmit data s p ＝(s p,0 ,s p,1 ,...,s p,L-1 ) T ,p∈[P]The channel from the p-th transmitting transducer to the j-th receiving transducer is +.>L is the channel length, then the data with the j-th received array element is:
wherein,is noise (I)>Is a matrix generated from the transmitted data as follows:
let Φ= (S 0 ,S 1 ,...S P-1 ) The following steps are:
x j ＝Φh p，j +n j ，
s2, solving by adopting a multi-task Bayesian algorithm to obtain estimated values of all channels, wherein the method specifically comprises the following steps:
s21, constructing a mapping matrix Φ= (S 0 ,S 1 ,...S P-1 )＝Φ 1 ＝Φ 2 ＝...＝Φ J ；
S22, define phi i ＝(Φ i,1 ,Φ i,2 ,...Φ i,M×P )，Σ i,(j,j) Is sigma i Elements of row j and column j;
s23, while converges;
s24, updating h (j) ＝α 0 Σ j Φ j T x j ；
S25, updating
S26 update a=diag (α) 1 ,α 2 ,...,α M×P )；
S27, updatingHere, because all mapping matrices are the same, only one update is needed;
s28, updatingBecause all the mapping matrices are the same, the updating of i is only calculated once;
s29, updatingBecause all the mapping matrices are the same, the updating of i is only calculated once;
s210, updateBecause all the mapping matrices are the same, the updating of i is only calculated once;
s211, updating
S212, outputting the estimated value of the channel.
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