采用压缩感知的贝叶斯信道估计算法

doi:10.3969/j.issn.1001-2400.2018.02.003

Abstract

Abstract:

The high-order multiple-input multiple-output system can improve the energy efficiency and transmission reliability. However, it is difficult to perform channel estimation because of the large number of antennas. Although the SABMP (Support Agnostic Bayesian Matching Pursuit) algorithm can estimate the channel accurately, the complexity is too high. To address this issue, an EPMP (Expectation Prune Matching Pursuit) algorithm is proposed in the paper. At each sparsity level of the channel, an expanded support set is given by adding some positions corresponding to the atoms that have a larger inner product value with the current residual signal. Then the best support set is obtained by removing the wrong positions in the expanded support set. The estimated channel and the relative probability of the best support set at each sparse level are calculated. Finally, the expectation of the channel is calculated and regarded as the estimation of the channel. Compared with the SABMP algorithm, the EPMP algorithm can reduce the computational complexity while maintaining the estimation accuracy. The effectiveness of the EPMP algorithm is validated by simulation results.

Key words: Bayesian estimation, compressed sensing, sparse reconstruction, channel estimation

LV Zhiguo;LI Ying. Compressed sensing-based Bayesian channel estimation algorithm[J].Journal of Xidian University, 2018, 45(2): 13-18+25.

References

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Compressed sensing-based Bayesian channel estimation algorithm

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