HoloBeam: Learning Optimal Beamforming in Far-Field Holographic Metasurface Transceivers

• URL: http://arxiv.org/abs/2401.05420v1
• Date: Sat, 30 Dec 2023 03:29:32 GMT
• Title: HoloBeam: Learning Optimal Beamforming in Far-Field Holographic Metasurface Transceivers
• Authors: Debamita Ghosh and Manjesh Kumar Hanawal and Nikola Zlatanova
• Abstract summary: Holographic Metasurface Transceivers (HMTs) are emerging as cost-effective substitutes to large antenna arrays for beamforming in Millimeter and TeraHertz wave communication. To achieve desired channel gains through beamforming in HMT, phase-shifts of a large number of elements need to be appropriately set, which is challenging. We develop a learning algorithm using a it fixed-budget multi-armed bandit framework to beamform and maximize received signal strength at the receiver for far-field regions.
• Score: 5.402030962296633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Holographic Metasurface Transceivers (HMTs) are emerging as cost-effective substitutes to large antenna arrays for beamforming in Millimeter and TeraHertz wave communication. However, to achieve desired channel gains through beamforming in HMT, phase-shifts of a large number of elements need to be appropriately set, which is challenging. Also, these optimal phase-shifts depend on the location of the receivers, which could be unknown. In this work, we develop a learning algorithm using a {\it fixed-budget multi-armed bandit framework} to beamform and maximize received signal strength at the receiver for far-field regions. Our algorithm, named \Algo exploits the parametric form of channel gains of the beams, which can be expressed in terms of two {\it phase-shifting parameters}. Even after parameterization, the problem is still challenging as phase-shifting parameters take continuous values. To overcome this, {\it\HB} works with the discrete values of phase-shifting parameters and exploits their unimodal relations with channel gains to learn the optimal values faster. We upper bound the probability of {\it\HB} incorrectly identifying the (discrete) optimal phase-shift parameters in terms of the number of pilots used in learning. We show that this probability decays exponentially with the number of pilot signals. We demonstrate that {\it\HB} outperforms state-of-the-art algorithms through extensive simulations.

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