Tensor-Based Low-Complexity Channel Estimation for mmWave Massive MIMO-OTFS Systems  

作　　者：Xianda Wu Shaodan Ma Xi Yang 

机构地区：[1]State Key Laboratory of Internet of Things for Smart City,University of Macao,Macao 999078,China

出　　处：《Journal of Communications and Information Networks》2020年第3期324-334,共11页通信与信息网络学报（英文）

基　　金：This work was supported by the Science and Technology Development Fund,Macao S.A.R.,China(File No.0036/2019/A1 and File No.SKL-IOTSC2018-2020).

摘　　要：Orthogonal time frequency space(OTFS)modulation,collaborated with millimeter-wave(mmWave)massive multiple-input-multiple-output(MIMO),is a promising technology for next generation wireless communications in high mobility scenarios.However,one of the main challenges for mmWave massive MIMO-OTFS systems is the enormous computational complexity of channel estimation incurred by the huge OTFS symbol size and the large number of antennas.To address this issue,in this paper,a tensor-based orthogonal matching pursuit(OMP)channel estimation algorithm is proposed by exploiting the channel sparsity in the delay-Doppler-angle domain.In particular,we firstly propose a novel pilot design for the OTFS symbol structure in the frequency-time domain.Then,based on the proposed pilot structure,we formulate the channel estimation as a sparse signal recovery problem,and the tensor decomposition and parallel support detection are introduced into the tensor-based OMP algorithm to reduce the signal processing dimension significantly.Numerical simulations are performed to verify the superiority and the robustness of the proposed tensor-based OMP algorithm.

关 键 词：OTFS MILLIMETER-WAVE massive MIMO channel estimation compressed sensing 

分 类 号：TN929.5[电子电信—通信与信息系统]