Dynamic Antenna Placement for Mobile Users in Urban Micro Pinching-Antenna Systems

TL;DR

This paper introduces a bilevel optimization framework that uses reinforcement learning for dynamic antenna placement and beamforming in urban micro networks, significantly improving spectral efficiency and robustness amid user mobility and blockages.

Contribution

It presents a novel combined approach of reinforcement learning and precoding for real-time antenna positioning in mobile urban environments.

Findings

Significant improvement in spectral efficiency.

Enhanced robustness to user mobility and blockages.

Effective real-time antenna control policy learned by SAC.

Abstract

The pinching-antenna systems (PASS) enable blockage mitigation in urban micro (UMi) networks through flexible antenna placement. However, the joint optimization of antenna positions and beamforming precoding is inherently nonconvex and becomes significantly more challenging under user mobility. To address this issue, we propose a bilevel optimization framework for dynamic antenna positioning and beamforming precoding design. In the outer level, a soft actor-critic (SAC) agent learns a continuous control policy for real-time antenna positioning, while in the inner level, zero-forcing (ZF) precoding is applied based on the instantaneous effective channel. Numerical results demonstrate that the proposed framework significantly improves spectral efficiency (SE) and enhances robustness against user mobility and random blockages.