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

TL;DR

This paper presents a transfer learning approach for near-field spot beamfocusing with large-scale metasurfaces, significantly reducing training time and improving dynamic focal point management through correlation-based knowledge transfer.

Contribution

It introduces a novel similarity criterion and a policy propagation scheme for transfer learning in ELPM-based beamfocusing, enhancing training efficiency and adaptability.

Findings

Training speed improved by approximately 5 times.

Convergence rate increased up to 8-fold for dynamic DFP management.

Proposed methods outperform traditional independent training approaches.

Abstract

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.