Location-Aware Communication for RIS-Aided Distributed MIMO Systems

被引:0
|
作者
Yang, Ziang [1 ]
Zhang, Hongliang [1 ]
Di, Boya [1 ]
Li, Xiang [2 ]
Hou, Xiaolin [2 ]
Song, Lingyang [3 ,4 ]
机构
[1] Peking Univ, Dept Elect, State Key Lab Adv Opt Commun Syst & Networks, Beijing 100871, Peoples R China
[2] DOCOMO Beijing Commun Labs Co Ltd, Beijing 100190, Peoples R China
[3] Peking Univ, Sch Elect, State Key Lab Adv Opt Commun Syst & Networks, Beijing 100871, Peoples R China
[4] Peking Univ, Sch Elect & Comp Engn, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China
基金
美国国家科学基金会; 北京市自然科学基金;
关键词
Location awareness; Training; Optimization; Array signal processing; Accuracy; Wireless communication; Reconfigurable intelligent surfaces; MIMO communication; Uncertainty; Fingerprint recognition; Reconfigurable intelligent surface (RIS); distributed MIMO; wireless localization; distributionally robust optimization (DRO); SURFACES;
D O I
10.1109/TVT.2024.3494880
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 reconfigurable intelligent surface (RIS)-aided distributed multiple-input multiple-output (D-MIMO) systems, instantaneous channel state information (CSI) is essential for the optimization of the RIS phase shifts. However, the D-MIMO systems often involve numerous RISs and access points (APs), requiring the transmission of a significant amount of pilots for CSI estimation, thus resulting in high signaling overhead. To mitigate the high signaling overhead associated with CSI estimation, this paper introduces a location-aware communication method that only relies on large-scale fading information. Two new challenges have arisen in the proposed location-aware communication method. First, it is challenging to design a user position estimation method to achieve fast and accurate localization. Second, it is hard to handle the location uncertainty brought by the estimation error when performing beamforming. In response to the above challenges, we first design a space-time cooperative beam training-based localization method to achieve a favorable trade-off between localization accuracy and time cost. Subsequently, to handle the location uncertainty, by taking historical error distribution into consideration, we formulate a distributionally robust optimization (DRO)-based problem for sum-rate maximization. Numerical evaluations demonstrate the effectiveness of our proposed localization method, which can achieve sub-meter accuracy with low beam training overhead. Furthermore, the proposed DRO-based beamforming method can improve the sum-rate by 11.4% compared to the robust optimization and non-robust schemes.
引用
收藏
页码:4445 / 4460
页数:16
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