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Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications

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Document pages: 30 pages

Abstract: Reconfigurable Intelligent Surfaces (RISs) have been recently considered asan energy-efficient solution for future wireless networks due to their fast andlow-power configuration, which has increased potential in enabling massiveconnectivity and low-latency communications. Accurate and low-overhead channelestimation in RIS-based systems is one of the most critical challenges due tothe usually large number of RIS unit elements and their distinctive hardwareconstraints. In this paper, we focus on the downlink of a RIS-empoweredmulti-user Multiple Input Single Output (MISO) downlink communication systemsand propose a channel estimation framework based on the PARAllel FACtor(PARAFAC) decomposition to unfold the resulting cascaded channel model. Wepresent two iterative estimation algorithms for the channels between the basestation and RIS, as well as the channels between RIS and users. One is based onalternating least squares (ALS), while the other uses vector approximatemessage passing to iteratively reconstruct two unknown channels from theestimated vectors. To theoretically assess the performance of the ALS-basedalgorithm, we derived its estimation Cramér-Rao Bound (CRB). We also discussthe achievable sum-rate computation with estimated channels and differentprecoding schemes for the base station. Our extensive simulation results showthat our algorithms outperform benchmark schemes and that the ALS techniqueachieve the CRB. It is also demonstrated that the sum rate using the estimatedchannels reached that of perfect channel estimation under various settings,thus, verifying the effectiveness and robustness of the proposed estimationalgorithms.

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