# Learning Optimal Phase-Shifts of Holographic Metasurface Transceivers

arXiv: https://arxiv.org/abs/2301.03371 · HTML: https://mlsanity.com/paper/2301.03371/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 12 Dec 2022.

## Builds on (most cited first, 12 of 12)

- [Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming](https://mlsanity.com/paper/1810.03961/) (2018)
- [Millimeter Wave Channel Modeling and Cellular Capacity Evaluation](https://mlsanity.com/paper/1312.4921/) (2013)
- [Holographic MIMO Surfaces for 6G Wireless Networks: Opportunities, Challenges, and Trends](https://mlsanity.com/paper/1911.12296/) (2019)
- [Terahertz Massive MIMO with Holographic Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2009.10963/) (2020)
- [Beam Focusing for Near-Field Multi-User MIMO Communications](https://mlsanity.com/paper/2105.13087/) (2021)
- [Physics-based Modeling and Scalable Optimization of Large Intelligent Reflecting Surfaces](https://mlsanity.com/paper/2004.12957/) (2020)
- [Beam Training based on Dynamic Hierarchical Codebook for Millimeter Wave Massive MIMO](https://mlsanity.com/paper/1901.01425/) (2019)
- [Joint Beam Training and Positioning For Intelligent Reflecting Surfaces Assisted Millimeter Wave Communications](https://mlsanity.com/paper/2009.03536/) (2020)
- [Path Loss in Reconfigurable Intelligent Surface-Enabled Channels](https://mlsanity.com/paper/1912.06759/) (2019)
- [Channel Modeling and Channel Estimation for Holographic Massive MIMO with Planar Arrays](https://mlsanity.com/paper/2108.04633/) (2021)
- [Holographic Metasurface Antennas for Uplink Massive MIMO Systems](https://mlsanity.com/paper/2108.12513/) (2021)
- [Parametric Channel Estimation for LoS Dominated Holographic Massive MIMO Systems](https://mlsanity.com/paper/2112.02874/) (2021)

## Built on by (most cited first, 1 of 1)

- [HoloBeam: Learning Optimal Beamforming in Far-Field Holographic Metasurface Transceivers](https://mlsanity.com/paper/2401.05420/) (2023)

## Abstract (arXiv)

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.