Adaptive Blind Beamforming for Intelligent Surface

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 phase shifts of the IS without channel acquisition. The RE grouping plays a critical role in guaranteeing performance gain in the NLoS case. In particular, if we place all the REs in the same group, the proposed algorithm would reduce to the RFocus and the CSM. We validate the advantage of the proposed blind beamforming algorithm in the real-world networks at 3.5 GHz aside from simulations.