Learning Optimal Phase-Shifts of Holographic Metasurface Transceivers
This work addresses a critical bottleneck in HMT technology for improving wireless communication coverage and rate, representing an incremental advancement with a novel method for a known problem.
The paper tackles the problem of acquiring accurate channel state information in holographic metasurface transceiver (HMT)-assisted wireless communication systems by proposing an algorithm to learn optimal phase-shifts for the far-field channel model, achieving significant gains over state-of-the-art policies as validated by simulations.
Holographic metasurface transceivers (HMT) is an emerging technology for enhancing the coverage and rate of wireless communication systems. However, acquiring accurate channel state information in HMT-assisted wireless communication systems is critical for achieving these goals. In this paper, we propose an algorithm for learning the optimal phase-shifts at a HMT for the far-field channel model. Our proposed algorithm exploits the structure of the channel gains in the far-field regions and learns the optimal phase-shifts in presence of noise in the received signals. We prove that the probability that the optimal phase-shifts estimated by our proposed algorithm deviate from the true values decays exponentially in the number of pilot signals. Extensive numerical simulations validate the theoretical guarantees and also demonstrate significant gains as compared to the state-of-the-art policies.