基于聚类块稀疏贝叶斯学习的毫米波信道估计

Jiawen Liu, Xiaohui Li, Kun Fang, Tao Fan
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引用次数: 3

摘要

提出了一种用于毫米波信道估计的聚类块稀疏贝叶斯学习(CB-SBL)算法。该算法利用毫米波信道之间的相关性,采用结构优先级聚类方法来处理毫米波系统中信道空间的稀疏系数。通过对邻域的超参数进行处理,改善了毫米波信道邻域估计系数的依赖性,避免了超参数纠缠引起的次优解。这种方法提高了毫米波信道估计的精度。同时,该算法还避免了块稀疏贝叶斯学习(BSBL)中参数选择的漏洞,提高了毫米波信道估计的鲁棒性。仿真结果表明,基于CBSBL的毫米波信道估计优于现有算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Millimeter Wave Channel Estimation Based on Clustering Block Sparse Bayesian Learning
The clustering block sparse Bayesian learning (CB-SBL) algorithm for millimeter wave (mmWave) channel estimation is proposed in this paper. Exploiting the correlation between the mmWave channel, the algorithm adopts the structure prioritization clustering method to cope with the sparse coefficients of the channel space in the mmWave system. The dependence of mmWave channel adjacent estimation coefficients is improved by processing the hyperparameters of the neighborhood, which is used to avoid the suboptimal solutions caused by the entanglement of hyperparameters. The mmWave channel estimation accuracy is improved in this way. Meanwhile the proposed algorithm also avoids the vulnerability of parameter choice in block sparse Bayesian learning (BSBL), which improves the robustness of mmWave channel estimation. The simulation results show that the mmWave channel estimation based on CBSBL outperforms the recently proposed algorithms.
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