Tensor-Based Channel Estimation for RIS-Assisted Communications with Non-Ideal Phase Shift Responses

Abstract

Reconfigurable intelligent surface (RIS) is a candidate technology for future wireless networks. It is known that the promised gains of RIS-assisted communications depend on the channel estimation performance. When the RIS is affected by imperfections, the associated phase shift responses present a non-ideal behavior, which translates into unknown, and possibly time-varying, phase deviations. Such perturbations can be caused by physical, electronic, or environmentalrelated conditions. In this scenario, traditional channel estimation schemes may fail to provide sufficiently accurate channel estimates. In this work, considering a time-varying RIS imperfection model, we propose an efficient and low-complexity tensor-based method to estimate the involved communication channels under unknown phase-shift responses. The proposed algorithm relies on a tensor modeling of the received signals and has a closed-form solution based on the higher order singular value decomposition. Simulation results show the effectiveness of our proposed solution in terms of estimation accuracy and computational complexity compared to the benchmark method.