Spatial Feature Aided Optimal Compressive Phase Training and Channel Estimation in Massive MIMO Systems with RIS

Accurate channel state information (CSI) acquisition with low pilot overhead has always been a problem for RIS-assisted massive MIMO systems. The design of phase shifts at RIS plays a key role in channel estimation (CE). In this paper, we incorporate the spatial feature of RIS into the design of a variational Bayesian inference-based CE algorithm, which is computational efficient and can exploit the sparse structure of cascaded channel. To optimally configure the phase shifts of RIS for CE, an optimal phase training algorithm and a simplified version are proposed by formulating the CE performance metric in terms of the RIS phases. The numerical simulations show that the proposed spatial feature aided optimal phase training of RIS as well as the compressive CE algorithm achieve superior CE performance with very low pilot overhead compared to the state-of-art baselines.