An improved fast adaptive complex approximation message passing algorithm

To solve the problem of reconstructing complex sparse signals from linear measurements of additive white Gaussian noise (AWGN) under the condition of unknown sparse signal distribution, this paper proposes a fast adaptive complex approximate message passing (CAMP) algorithm with significantly reduce...

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Vydané v:Digital signal processing Ročník 137; s. 104016
Hlavní autori: Chen, Zirui, Chen, Alei, Liu, Weijian, Chen, Wenfeng, Ma, Xiaoyan, Lv, Mingjiu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Inc 15.06.2023
Predmet:
Complex approximate message passing
Compressed sensing
Shrinkage function
Sparse reconstruction
Complex approximate message passing
Sparse reconstruction
Compressed sensing
Shrinkage function
ISSN:1051-2004, 1095-4333
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Popis
Shrnutí:To solve the problem of reconstructing complex sparse signals from linear measurements of additive white Gaussian noise (AWGN) under the condition of unknown sparse signal distribution, this paper proposes a fast adaptive complex approximate message passing (CAMP) algorithm with significantly reduced mean square error and adaptive to parameter selection. First, we establish a complex sparse signal distribution model. Secondly, the unknown parameters of the distribution model are estimated in each iteration, and the estimated values are used as prior information to obtain the complex shrinkage function with minimum mean square error (MMSE). Finally, an improved fast adaptive CAMP algorithm is obtained by combining the updated shrinkage function with the CAMP. The algorithm has the advantages of fast convergence, low computational complexity, small mean square error, and good robustness. Theoretical analysis and simulation verify the effectiveness of the proposed algorithm. •An improved fast adaptive complex approximate message passing algorithm is proposed.•Better reconstruction performance by improving shrinkage function.•An adaptive scheme is given by parameter estimation.•Algorithm performance is affected by the actual function distribution.
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2023.104016