Joint Beamforming and Antenna Placement Optimization in Pinching Antenna Systems with User Mobility: A Deep Reinforcement Learning Approach

TL;DR

This paper proposes a deep reinforcement learning method to optimize beamforming and antenna placement in pinching antenna systems with mobile users, addressing real-time configurability and environmental uncertainties.

Contribution

It introduces a novel RL-based joint optimization framework for PASS that accounts for user mobility and environmental randomness, improving system adaptability.

Findings

The DDPG approach effectively tracks user movement and optimizes configurations in real time.

Simulation results show significant performance improvements over static methods.

The method maintains QoS constraints under dynamic environmental conditions.

Abstract

Recently, the pinching antenna systems (PASS) have attracted significant attention due to their ability to exploit dynamically reconfigurable pinching points along waveguides for flexible signal transmission. However, existing work largely overlooks user mobility although the optimal pinching configuration is highly dependent on the user's location and must be continuously adjusted. In this work, we investigate a PASS-enabled system model in which a base station (BS) serves a mobile user. We formulate an optimization problem that aims to maximize the user's average sum rate over a predefined time horizon while satisfying quality-of-service (QoS) constraint. This objective is achieved by jointly optimizing the beamforming vector at the BS and the pinching locations along the waveguides. Nevertheless, the resulting problem is highly non-convex and challenging to solve using conventional optimization techniques due to the intricate coupling among variables. The difficulty is further exacerbated by environmental randomness arising from user mobility and a probabilistic blockage model. This reveals a key engineering challenge: the performance gains of PASS critically rely on the ability to track or predict user trajectories in real time. To address these challenges, we adopt a deep deterministic policy gradient (DDPG) approach within a reinforcement learning framework, which is well-suited for continuous state and action spaces. Finally, extensive simulations are conducted to validate the proposed approach and demonstrate the importance of real-time configurability.