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Cluster sparse channel estimation method based on maximum dustpan tongue line criterion algorithm

A sparse channel and skip-line technology, which is applied in channel estimation, baseband system, baseband system components, etc., can solve the problems of many tap coefficients, difficult application of compressed sensing algorithm, and increased calculation amount, achieving high precision and reducing Effect of Redundant Channel Tap Computation

Active Publication Date: 2020-08-18
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

However, if the traditional estimation method does not adopt sparse constraint processing, the number of tap coefficients to be estimated will increase and the amount of calculation will increase. Fortunately, the sparse estimation strategy can simplify the number of estimated taps, which is expected to improve the estimation efficiency
Considering that the actual channel impulse response function is not a sparse signal in the strict sense, it is difficult to apply the current compressive sensing algorithm directly

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  • Cluster sparse channel estimation method based on maximum dustpan tongue line criterion algorithm
  • Cluster sparse channel estimation method based on maximum dustpan tongue line criterion algorithm

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Embodiment Construction

[0024] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0025] Reference to an embodiment of a cluster sparse channel estimation method based on the maximum skip-line criterion algorithm figure 1 , set the length of the sparse channel impulse response function to N=100, the block length d=5, the noise energy of the received signal is generated according to the standard normal distribution method and the Bernoulli distribution method, and the algorithm parameters are set to p=4, μ=0.0005, τ= 0.0039, the sparsity of the sparse channel is set to κ=1, κ=2, κ=3, κ=4, the obtained results are as follows figure 1 (a)(b)(c)(d), from figure 1 It can be seen that for GMCC and the MVC method, p=1, p=2, and p=4 can be set;

[0026] Proceed as follows:

[0027] Step 1: Calculate the estimation error of the sparse channel in the time domain: where y i is the discrete value of the received signal at the i-th moment, is the t...

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Abstract

The invention relates to a cluster sparse channel estimation method based on a maximum dustpan tongue line criterion algorithm, which is used for estimating a time domain cluster sparse channel impulse response function. Firstly, cluster sparse modeling is carried out on a time-varying channel, so that a structured sparse channel expression framework is expected to be obtained. A block-by-block training mode is adopted, an algorithm iteration mode based on the maximum dustpan tongue line criterion is designed, and cluster sparse channel impulse response function information can be estimated. The method is suitable for time-varying channel estimation, underwater acoustic communication and the like, and belongs to the fields of underwater acoustic communication, underwater acoustic signal processing and the like. The method has the beneficial effects that based on the cluster sparse norm constraint of the channel, the redundant channel tap calculation is effectively reduced, so that thechannel estimation result generated by the method has higher precision.

Description

technical field [0001] The invention belongs to the fields of underwater acoustic communication, underwater acoustic signal processing, etc., and relates to a cluster sparse channel estimation method based on the maximum skip line criterion algorithm. This estimation method will improve the estimation performance of time-varying underwater acoustic channel. Background technique [0002] Problems such as channel estimation and underwater acoustic communication can be attributed to the optimal estimation of the impulse response function, and the sparse representation of the time-varying channel is estimated based on the training sequence and the received signal. At present, channel estimation methods include finite impulse response framework and block-by-block estimation framework in time domain. For the algorithm details of the finite impulse response framework, please refer to "Non-uniform norm constraint LMS algorithm for sparse system identification", which was published ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L25/02
CPCH04L25/0212H04L25/0216H04L25/024
Inventor 伍飞云田天吴梦行杨坤德
Owner NORTHWESTERN POLYTECHNICAL UNIV