Bivariate Pilot Optimization for Compressed Channel Estimation in RIS-Assisted Multiuser MISO-OFDM Systems

In reconfigurable intelligent surface (RIS)-assisted wireless communication systems, channel estimation is of paramount importance to unleash the full advantage of the high passive beamforming gains from RIS. However, it is particularly challenging due to the massive number of RIS reflecting elements which lack signal perception and processing capabilities, thus incurring prohibitively high pilot overhead between transceivers. To address this challenge, we focus on an RIS-assisted multiuser multiple-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system, and propose a practical transmission protocol with non-uniformly spaced comb-type pilots for compressed channel estimation and data transmission, by exploiting the sparsity of RIS-associated channels. To solve a bivariate pilot optimization problem with the objective of minimizing the mutual coherence of the measurement matrix influenced by pilot positions and pilot powers simultaneously, we propose two enhanced whale optimization algorithm (EWOA)-based approaches by leveraging the second order cone programming (SOCP) and compressed coding scheme (CCS). Simulation results validate the effectiveness of the proposed methods over benchmark schemes in various aspects. It corroborates that considering the coupling relationship between pilot positions and powers can help improve the channel estimation performance of the RIS-assisted MU-MISO-OFDM system with limited pilot overhead.