Learning Optimal Phase-Shifts of Holographic Metasurface Transceivers

Abstract

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.

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“…The computational cost and the training overhead of the proposed scheme do not scale with the number of phase-shift elements of the HMT, but they did not provide any theoretical guarantee on their proposed algorithm. A pure-exploration based algorithm with a theoretical guarantee is proposed in [21], which outperforms the one in [11]. However, none of the above-mentioned works exploit the unimodal structure of the far-field channel model of the HMT system.…”

“…In this section, we consider the channel model as given in [21] for which we propose our algorithm. We follow the setup and notation provided in [11], [21].…”

“…Finally, we denote θ and ϕ as the elevation and azimuth angles of the signals from the user to the centre of the HMT, refer to Fig. 2 in [21]. We consider the phase-shift imposed by the (m x , m y ) th element is set to…”

“…where β 1 and β 2 are the phase-shifting parameters [11], [34], [35] and they are the only degrees of freedom in β mxmy . With β mxmy as in (1), the channel between the HMT and the typical user [11], [21] for far-field regions is given by…”

“…Note that α 1 ∈ [−1, 1] and α 2 ∈ [−1, 1], and their values depend on the user's location, i.e., on θ and ϕ. Therefore, α 1 and α 2 are the two unknown parameters that need to be learned by the HMT [21].…”