Adaptive Blind Beamforming for Intelligent Surface
Wenhai Lai, Wenyu Wang, Fan Xu, Xin Li, Shaobo Niu, Kaiming Shen

TL;DR
This paper introduces an adaptive blind beamforming method for intelligent surfaces that overcomes limitations of existing algorithms in non-line-of-sight scenarios by using a grouping strategy to extract environmental features without channel knowledge.
Contribution
The paper proposes a novel grouping-based adaptive blind beamforming algorithm that improves performance in NLoS conditions, extending existing methods like RFocus and CSM.
Findings
Effective in NLoS scenarios
Validated on real-world networks at 3.5 GHz
Outperforms existing blind beamforming methods
Abstract
Configuring intelligent surface (IS) or passive antenna array without any channel knowledge, namely blind beamforming, is a frontier research topic in the wireless communication field. Existing methods in the previous literature for blind beamforming include the RFocus and the CSM, the effectiveness of which has been demonstrated on hardware prototypes. However, this paper points out a subtle issue with these blind beamforming algorithms: the RFocus and the CSM may fail to work in the non-line-of-sight (NLoS) channel case. To address this issue, we suggest a grouping strategy that enables adaptive blind beamforming. Specifically, the reflective elements (REs) of the IS are divided into three groups; each group is configured randomly to obtain a dataset of random samples. We then extract the statistical feature of the wireless environment from the random samples, thereby coordinating…
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Taxonomy
TopicsAdvanced Wireless Communication Technologies · Underwater Vehicles and Communication Systems · Indoor and Outdoor Localization Technologies
