Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Massive MIMO Channel Estimation Algorithm Based on Weighted Compressed Sensing

  • Lv, Zhiguo (Dept. of Computer and Information Engineering, Luoyang Institute of Science and Technology) ;
  • Wang, Weijing (Dept. of Computer and Information Engineering, Luoyang Institute of Science and Technology)
  • Received : 2021.01.22
  • Accepted : 2021.05.04
  • Published : 2021.12.31
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Abstract

Compressed sensing-based matching pursuit algorithms can estimate the sparse channel of massive multiple input multiple-output systems with short pilot sequences. Although they have the advantages of low computational complexity and low pilot overhead, their accuracy remains insufficient. Simply multiplying the weight value and the estimated channel obtained in different iterations can only improve the accuracy of channel estimation under conditions of low signal-to-noise ratio (SNR), whereas it degrades accuracy under conditions of high SNR. To address this issue, an improved weighted matching pursuit algorithm is proposed, which obtains a suitable weight value uop by training the channel data. The step of the weight value increasing with successive iterations is calculated according to the sparsity of the channel and uop. Adjusting the weight value adaptively over the iterations can further improve the accuracy of estimation. The results of simulations conducted to evaluate the proposed algorithm show that it exhibits improved performance in terms of accuracy compared to previous methods under conditions of both high and low SNR.

Keywords

Acknowledgement

This work was supported in part by the science and technology breakthrough project of the Henan Science and Technology Department (No. 192102210249, 192102210116, and 212102210470), Key projects of colleges and universities in Henan Province (No. 19B510007 and 19A520006). It was also supported in part by the Science and Technology Development Plan of Henan Province (No. 202102310625).

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