{"ID":2844882,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.05132","arxiv_id":"2511.05132","title":"Near-optimal Reconfigurable Intelligent Surface Configuration: Blind Beamforming with Sensing","abstract":"Blind beamforming has emerged as a promising approach to configure reconfigurable intelligent surfaces (RISs) without relying on channel state information (CSI) or geometric models, making it directly compatible with commodity hardware. In this paper, we propose a new blind beamforming algorithm, so-called Blind Optimal RIS Beamforming with Sensing (\\textsc{BORN}), that operates using only received signal strength (RSS). In contrast to existing methods that rely on majority-voting mechanisms, \\textsc{BORN} exploits the intrinsic quadratic structure of the received signal-to-noise ratio (SNR). The algorithm proceeds in two stages: \\emph{sensing}, where a quadratic model is estimated from RSS measurements, and \\emph{optimization}, where the RIS configuration is obtained using the estimated quadratic model. Our novelties are twofold. Firstly, we show for the first time, that \\textsc{BORN} can achieve provable near-optimal performance using only $O(N \\log_2(N))$ samples, where $N$ is the number of RIS elements. As a by-product of our analysis, we show that quadratic models are learnable under Rademacher feature distributions when the second-order coefficient matrix is low-rank. This result, to our knowledge, has not been established in prior matrix sensing literature. Secondly, extensive simulations and real-world field tests demonstrate that \\textsc{BORN} achieves near-optimal performance, substantially outperforming state-of-the-art blind beamforming algorithms, particularly in scenarios with a weak background channel such as non-line-of-sight (NLOS).","short_abstract":"Blind beamforming has emerged as a promising approach to configure reconfigurable intelligent surfaces (RISs) without relying on channel state information (CSI) or geometric models, making it directly compatible with commodity hardware. In this paper, we propose a new blind beamforming algorithm, so-called Blind Optima...","url_abs":"https://arxiv.org/abs/2511.05132","url_pdf":"https://arxiv.org/pdf/2511.05132v1","authors":"[\"Son Dinh-Van\",\"Nam Phuong Tran\",\"Matthew D. Higgins\"]","published":"2025-11-07T10:27:23Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}