Low SNR Spatially Sparse Channel Estimation in Millimeterwave Hybrid MIMO Systems based on Adaptive Filtering Framework

B. K. Das
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Abstract

In this paper, we propose an adaptive filtering framework for spatially sparse channel estimation technique applicable to millimeter wave (mmWave) multiple-input-multiple-output (MIMO) wireless communication with hybrid precoding. The proposed algorithm is claimed to be a viable alternative to the recently proposed compressed sensing based framework, especially the celebrated orthogonal matching pursuit (OMP) algorithm. The proposed algorithm helps to attain better performance than the existing algorithms in low SNR as we demonstrate by numerical simulations. The paper is an attempt to justify the use of sparse adaptive filtering framework for these particular application, and we choose a simple narrow-band, single user scenario to do that. In future, it can be extended to include frequency selective and multi user cases.
基于自适应滤波框架的毫米波混合MIMO系统低信噪比空间稀疏信道估计
本文提出了一种适用于毫米波(mmWave)多输入多输出(MIMO)混合预编码无线通信的空间稀疏信道估计自适应滤波框架。该算法被认为是最近提出的基于压缩感知的框架,特别是著名的正交匹配追踪(OMP)算法的可行替代方案。数值模拟结果表明,该算法在低信噪比条件下比现有算法具有更好的性能。本文试图证明在这些特定应用中使用稀疏自适应滤波框架是合理的,我们选择了一个简单的窄带单用户场景来做这件事。未来,它可以扩展到包括频率选择和多用户用例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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