Two Stage Beamforming in Massive MIMO: A Combinatorial Multi-Armed Bandit Based Approach

被引:7
|
作者
Song, Yunchao [1 ]
Liu, Chen [1 ]
Zhang, Wenyi [1 ]
Liu, Yiliang [2 ]
Zhou, Haibo [3 ]
Shen, Xuemin [4 ]
机构
[1] Nanjing Univ Posts & Telecommun, Coll Elect & Opt Engn, Nanjing 210003, Peoples R China
[2] Jiaotong Univ, Sch Cyber Sci & Engn, Xian 710049, Shaanxi, Peoples R China
[3] Nanjing Univ, Sch Elect Sci & Engn, Nanjing 210023, Jiangsu, Peoples R China
[4] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada
关键词
Discrete Fourier transforms; Interference; Downlink; Array signal processing; Training; Massive MIMO; Channel estimation; combinatorial multi-armed bandit; upper confidence bound; chi-square distribution; JOINT SPATIAL DIVISION;
D O I
10.1109/TVT.2022.3229312
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In frequency division duplex (FDD) massive multi-input multi-output (MIMO), the two-stage beamforming (TSB) using channel covariance matrices (CCM) can significantly reduce the downlink training length (DTL) and channel feedback. However, the overhead to estimate the CCM is large. In this paper, a combinatorial multi-armed bandit (CMAB) based TSB scheme is proposed without requirement of CMM. Particularly, the problem of the pre-beamforming matrix design is transformed into a CMAB problem. We consider the pre-beamforming matrix design in each slot as the arm selection in the CMAB, and convert the problem of the arm selection into a 0-1 integer linear programming problem, which can be solved by the branch-and-bound method. During the training process, the maximum likelihood method is used to detect the power of angle spectrum, and the angle range of each user is determined adaptively. We prove that the regret grows logarithmically with time, such that the proposed scheme converges towards the optimal action. Finally, simulation results demonstrate that the proposed scheme can significantly improve the spectral efficiency.
引用
收藏
页码:6794 / 6799
页数:6
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