  In this paper, we introduce the concept of spot beamfocusing (SBF) in the
Fresnel zone through extremely large-scale programmable metasurfaces (ELPMs) as
a key enabling technology for 6G networks. A smart SBF scheme aims to
adaptively concentrate the aperture's radiating power exactly at a desired
focal point (DFP) in the 3D space utilizing some Machine Learning (ML) method.
This offers numerous advantages for next-generation networks including
efficient wireless power transfer (WPT), interference mitigation, reduced RF
pollution, and improved information security. SBF necessitates ELPMs with
precise channel state information (CSI) for all ELPM elements. However,
obtaining exact CSI for ELPMs is not feasible in all environments; we alleviate
this by proposing an adaptive novel CSI-independent ML scheme based on the TD3
deep-reinforcement-learning (DRL) method. While the proposed ML-based scheme is
well-suited for relatively small-size arrays, the computational complexity is
unaffordable for ELPMs. To overcome this limitation, we introduce a modular
highly scalable structure composed of multiple sub-arrays, each equipped with a
TD3-DRL optimizer. This setup enables collaborative optimization of the
radiated power at the DFP, significantly reducing computational complexity
while enhancing learning speed. The proposed structures benefits in terms of 3D
spot-like power distribution, convergence rate, and scalability are validated
through simulation results.
