TL;DR
This paper investigates the necessity of channel estimation for RIS-assisted massive MIMO systems, proposing Bayesian estimators and phase-shift heuristics, and compares short-term and long-term RIS configurations for spectral efficiency gains.
Contribution
It introduces Bayesian channel estimators for RIS-assisted massive MIMO and a heuristic phase-shift selection method, analyzing their impact on spectral efficiency.
Findings
RIS-assisted framework improves cell-edge spectral efficiency
Short-term channel estimation is beneficial under certain channel conditions
Long-term RIS configuration reduces computational complexity
Abstract
Reconfigurable intelligent surfaces (RISs) consist of many passive elements of metamaterials whose impedance can be controllable to change the characteristics of wireless signals impinging on them. Channel estimation is a critical task when it comes to the control of a large RIS when having a channel with a large number of multipath components. In this paper, we derive Bayesian channel estimators for two RIS-assisted massive multiple-input multiple-output (MIMO) configurations: i) the short-term RIS configuration based on the instantaneous channel estimates; ii) the long-term RIS configuration based on the channel statistics. The proposed methods exploit spatial correlation characteristics at both the base station and the planar RISs, and other statistical characteristics of multi-specular fading in a mobile environment. Moreover, a novel heuristic for phase-shift selection at the RISs…
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