# Near-Field Spot Beamfocusing: A Correlation-Aware Transfer Learning Approach

arXiv: https://arxiv.org/abs/2405.19347 · HTML: https://mlsanity.com/paper/2405.19347/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 21 May 2024.

## Builds on (most cited first, 15 of 15)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [A Survey on Channel Estimation and Practical Passive Beamforming Design for Intelligent Reflecting Surface Aided Wireless Communications](https://mlsanity.com/paper/2110.01292/) (2021)
- [Fundamentals of Wireless Information and Power Transfer: From RF Energy Harvester Models to Signal and System Designs](https://mlsanity.com/paper/1803.07123/) (2018)
- [Near-Field Communications: A Tutorial Review](https://mlsanity.com/paper/2305.17751/) (2023)
- [Near-Field Channel Estimation for Extremely Large-Scale Reconfigurable Intelligent Surface (XL-RIS)-Aided Wideband mmWave Systems](https://mlsanity.com/paper/2304.00440/) (2023)
- [6G Wireless Communications: From Far-field Beam Steering to Near-field Beam Focusing](https://mlsanity.com/paper/2203.13035/) (2022)
- [Near-Field Sparse Channel Representation and Estimation in 6G Wireless Communications](https://mlsanity.com/paper/2212.13527/) (2022)
- [Adaptive Policy Transfer in Reinforcement Learning](https://mlsanity.com/paper/2105.04699/) (2021)
- [Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions](https://mlsanity.com/paper/2212.01257/) (2022)
- [A Tutorial on Holographic MIMO Communications--Part II: Performance Analysis and Holographic Beamforming](https://mlsanity.com/paper/2305.15730/) (2023)
- [Toward Beamfocusing-Aided Near-Field Communications: Research Advances, Potential, and Challenges](https://mlsanity.com/paper/2309.09242/) (2023)
- [6G Fresnel Spot Beamfocusing using Large-Scale Metasurfaces: A Distributed DRL-Based Approach](https://mlsanity.com/paper/2311.11109/) (2023)
- [Towards Near-Field 3D Spot Beamfocusing: Possibilities, Challenges, and Use-cases](https://mlsanity.com/paper/2401.08651/) (2023)

## Abstract (arXiv)

Three-dimensional (3D) spot beamfocusing (SBF), in contrast to conventional angular-domain beamforming, concentrates radiating power within a very small volume in both radial and angular domains in the near-field zone. Recently the implementation of channel-state-information (CSI)-independent machine learning (ML)-based approaches have been developed for effective SBF using extremely large-scale programmable metasurface (ELPMs). These methods involve dividing the ELPMs into subarrays and independently training them with Deep Reinforcement Learning to jointly focus the beam at the desired focal point (DFP). This paper explores near-field SBF using ELPMs, addressing challenges associated with lengthy training times resulting from independent training of subarrays. To achieve a faster CSI-independent solution, inspired by the correlation between the beamfocusing matrices of the subarrays, we leverage transfer learning techniques. First, we introduce a novel similarity criterion based on the phase distribution image (PDI) of subarray apertures. Then we devise a subarray policy propagation scheme that transfers the knowledge from trained to untrained subarrays. We further enhance learning by introducing quasi-liquid layers as a revised version of the adaptive policy reuse technique. We show through simulations that the proposed scheme improves the training speed about 5 times. Furthermore, for dynamic DFP management, we devised a DFP policy blending process, which augments the convergence rate up to 8-fold.