Efficient Compressive Channel Estimation for Millimeter-Wave Large-Scale Antenna Systems
Efficient Compressive Channel Estimation for Millimeter-Wave Large-Scale Antenna Systems
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DOI：
10.1109/tsp.2018.2811742
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发表时间：
2018-05
影响因子：
5.4
通讯作者：
Cheng-Rung Tsai;Yu-Hsin Liu;A. Wu
中科院分区：
文献类型：
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作者：
Cheng-Rung Tsai;Yu-Hsin Liu;A. Wu
大规模天线系统被认为是补偿毫米波（mmWave）通信中的巨大路径损耗的可行技术。然而，由于大量的天线，信道状态信息（CSI）的获取是昂贵的和具有挑战性的。在本文中，我们开发了一种新的压缩信道估计框架的基础上，多个测量向量（MMV）。与传统的基于单测量向量（SMV）的方法相比，该框架利用了局部散射相对丰富的毫米波信道的结构稀疏性，大大降低了训练和计算开销。此外，我们提出了一种基于多信号分类（MUSIC）的信道子空间匹配追踪（CSMP）算法作为MMV求解器。通过利用MUSIC的优点，所提出的CSMP可以适当地利用结构稀疏性的分集增益，并通过超分辨率能力进一步提高估计质量。同时，提出了一种有效的实现方法，建议CSMP。与传统的MMV求解器相比，所提出的CSMP具有更低的复杂度。最后，几个仿真结果表明，基于MMV的CSMP实现了显着的性能增益比其他估计算法，特别是当角分辨率高。关于计算成本，仿真结果表明，基于MMV的估计算法是大约两个数量级小于基于SMV的估计算法。
Large-scale antenna systems are considered as a viable technology to compensate for huge path loss in millimeter-wave (mmWave) communications. However, due to the massive antennas, the channel state information (CSI) acquisition is costly and challenging. In this paper, we develop a novel compressive channel estimation framework based on multiple measurement vectors (MMV). Compared with conventional single measurement vector (SMV)-based approach, the proposed framework exploits structural sparsity exhibited in the relatively rich local scattering mmWave channels to greatly reduce the training and computational overheads. Moreover, we propose a channel subspace matching pursuit (CSMP) algorithm based on the MUltiple SIgnal Classification (MUSIC) as an MMV solver. By leveraging the benefits of MUSIC, the proposed CSMP can properly exploit the diversity gain from structural sparsity, and further improve the estimation quality via the superresolution capability. Meanwhile, an efficient implementation method of the proposed CSMP is also presented. Compared to the conventional MMV solver, the proposed CSMP exhibits much lower complexity. Finally, several simulation results show that the MMV-based CSMP achieves significant performance gains over other estimation algorithms, especially when the angular resolutions are high. Regarding the computational cost, the simulation result shows that the MMV-based estimation algorithms are approximately two orders of magnitude smaller than the SMV-based estimation algorithms.