Channel Estimation for Millimeter-Wave Multiuser MIMO Systems via PARAFAC Decomposition
Channel Estimation for Millimeter-Wave Multiuser MIMO Systems via PARAFAC Decomposition
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DOI：
10.1109/twc.2016.2604259
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发表时间：
2016-02
影响因子：
10.4
通讯作者：
Zhou Zhou-Zhou;Jun Fang;Linxiao Yang;Hongbin Li;Zhi Chen;Shaoqian Li
中科院分区：
文献类型：
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作者：
Zhou Zhou-Zhou;Jun Fang;Linxiao Yang;Hongbin Li;Zhi Chen;Shaoqian Li
我们考虑毫米波（mmWave）系统的上行链路信道估计问题，其中基站（BS）和移动的站（MS）配备有大型天线阵列，以提供足够的波束成形增益用于室外无线通信。由于硬件约束，BS和MS都采用混合模拟和数字波束成形结构。我们提出了一种分层导频传输方案和一种基于CANDECOMP/PARAFAC（CP）分解的方法来联合估计来自多个用户的信道（即，MS）到BS。所提出的方法利用从多种模式收集的多路数据的固有低秩结构，其中低秩结构是毫米波信道的稀疏散射性质的结果。研究了CP分解的唯一性，得到了本质唯一的充分条件。这些条件为波束形成矩阵、合并矩阵和导频序列的设计提供了依据，同时也为系统参数的选择提供了一般性的指导。我们的分析表明，我们提出的方法可以实现一个实质性的训练开销减少，通过利用接收信号的低秩结构。仿真结果表明，该方法在估计精度和计算复杂度方面都明显优于基于压缩感知的方法。
We consider the problem of uplink channel estimation for millimeter wave (mmWave) systems, where the base station (BS) and mobile stations (MSs) are equipped with large antenna arrays to provide sufficient beamforming gain for outdoor wireless communications. Hybrid analog and digital beamforming structures are employed by both the BS and the MS due to hardware constraints. We propose a layered pilot transmission scheme and a CANDECOMP/PARAFAC (CP) decomposition-based method for joint estimation of the channels from multiple users (i.e., MSs) to the BS. The proposed method exploits the intrinsic low-rank structure of the multiway data collected from multiple modes, where the low-rank structure is a result of the sparse scattering nature of the mmWave channel. The uniqueness of the CP decomposition is studied, and the sufficient conditions for essential uniqueness are obtained. The conditions shed light on the design of the beamforming matrix, the combining matrix, and the pilot sequences, and meanwhile provide general guidelines for choosing system parameters. Our analysis reveals that our proposed method can achieve a substantial training overhead reduction by leveraging the low-rank structure of the received signal. Simulation results show that the proposed method presents a clear advantage over a compressed sensing-based method in terms of both estimation accuracy and computational complexity.