宽带毫米波MIMO-OFDM系统中信道估计的稀疏贝叶斯学习算法

Sitong Wang, Jing He, Jiali Cao, Y. Guan, Meng Han, Weijia Yu
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引用次数: 0

摘要

对于毫米波(mmWave)多输入多输出(MIMO)系统,现有的几种宽带系统波束空间信道估计方案倾向于假设波束空间信道在频域具有共同支持。在实际应用中,由于宽带引起的波束斜视效应,限制了所得结果的有效性。本文提出了一种用于宽带毫米波MIMO正交频分复用(OFDM)系统信道估计的多稀疏贝叶斯学习(M-SBL)算法。在缺乏共同支持假设的情况下,使用经验贝叶斯先验估计方便的后验分布候选基向量来估计信道。仿真分析表明,该方法能够准确估计低复杂度波束空间信道,且具有比传统算法更小的归一化均方误差(NMSE)性能。基于M-SBL的方法在用户数量大、导频数量少的情况下仍然具有较好的估计性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Sparse Bayesian Learning Algorithm for Channel Estimation in Wideband mmWave MIMO-OFDM Systems
For millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, several existing beamspace channel estimation schemes for wideband systems tend to assume that the beamspace channels have common support in the frequency domain. In this case, the validity of the results obtained is limited due to the beam squint effect caused by wideband in practice. In this paper, a multiple-sparse Bayesian learning (M-SBL) algorithm is proposed for channel estimation of wideband mmWave MIMO orthogonal frequency division multiplexing (OFDM) systems. In the absence of a common supporting hypothesis, an empirical Bayesian prior is used to estimate a convenient posterior distribution candidate basis vector to estimate the channel. Simulation analysis shows that the proposed method based on M-SBL can accurately estimate the low-complexity beamspace channel and has smaller normalized mean square error (NMSE) performance than the traditional algorithms. The method based on M-SBL still has better estimation performance when the number of users is large and the number of pilots is small.
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