3D spot beamfocusing (SBF), in contrast to conventional angular-domain beamforming, concentrates radiating power within 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-largescale-programable-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 CSIindependent 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 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.

翻译：三维点波束聚焦（SBF）与传统的角度域波束成形不同，它能在近场区域内将辐射功率集中在径向和角度域上极小的体积内。最近，基于信道状态信息（CSI）无关的机器学习（ML）方法已被开发出来，利用超大规模可编程超表面（ELPMs）实现有效的SBF。这些方法将ELPMs划分为子阵列，并使用深度强化学习对它们进行独立训练，以共同将波束聚焦于期望焦点（DFP）。本文探讨了使用ELPMs的近场SBF，解决了由于子阵列独立训练导致的训练时间过长的挑战。为了实现更快的CSI无关解决方案，受子阵列波束聚焦矩阵间相关性的启发，我们利用迁移学习技术。首先，我们引入了一种基于子阵列孔径相位分布图像的新颖相似性准则。然后，我们设计了一种子阵列策略传播方案，将知识从已训练的子阵列迁移到未训练的子阵列。我们进一步通过引入准液态层作为自适应策略重用技术的改进版本来增强学习。仿真结果表明，所提方案将训练速度提高了约5倍。此外，针对动态DFP管理，我们设计了一种DFP策略融合过程，将收敛速度提升了高达8倍。