{"ID":2873928,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.05639","arxiv_id":"2509.05639","title":"Power-Measurement-Based Channel Estimation for Beyond Diagonal RIS","abstract":"Beyond diagonal reconfigurable intelligent surface (BD-RIS), with its enhanced degrees of freedom compared to conventional RIS, has demonstrated notable potential for enhancing wireless communication performance. However, a key challenge in employing BD-RIS lies in accurately acquiring its channel state information (CSI) with both the base station (BS) and users. Existing BD-RIS channel estimation methods rely mainly on dedicated pilot signals, which increase system overhead and may be incompatible with current communication protocols. To overcome these limitations, this letter proposes a new single-layer neural network (NN)-enabled channel estimation method utilizing only the easily accessible received power measurements at user terminals. In particular, we show that the received signal power can be expressed in a form similar to a single-layer NN, where the weights represent the BD-RIS's CSI. This structure enables the recovery of CSI using the backward propagation, based on power measurements collected under varying training reflection coefficients. Numerical results show that our proposed method can achieve a small normalized mean square error (NMSE), particularly when the number of training reflections is large.","short_abstract":"Beyond diagonal reconfigurable intelligent surface (BD-RIS), with its enhanced degrees of freedom compared to conventional RIS, has demonstrated notable potential for enhancing wireless communication performance. However, a key challenge in employing BD-RIS lies in accurately acquiring its channel state information (CS...","url_abs":"https://arxiv.org/abs/2509.05639","url_pdf":"https://arxiv.org/pdf/2509.05639v2","authors":"[\"Yijie Liu\",\"Weidong Mei\",\"He Sun\",\"Dong Wang\",\"Peilan Wang\"]","published":"2025-09-06T08:14:06Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}