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

Qiushi Zhao, Zihan Feng, Ximing Xie, Hao Qin, Yuanwei Liu, Xingqi Zhang

TL;DR

A bilevel optimization framework for dynamic antenna positioning and beamforming precoding design that significantly improves spectral efficiency (SE) and enhances robustness against user mobility and random blockages is proposed.

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.

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

TL;DR

A bilevel optimization framework for dynamic antenna positioning and beamforming precoding design that significantly improves spectral efficiency (SE) and enhances robustness against user mobility and random blockages is proposed.

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.
Paper Structure (18 sections, 14 equations, 5 figures, 2 tables)

This paper contains 18 sections, 14 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. System Scenario and User Mobility
  4. Dynamic Channel Model
  5. LoS Probability
  6. Channel Coefficients
  7. Signal Model and Beamforming
  8. Problem Formulation
  9. DRL-Based Dynamic Antenna Positioning Design
  10. MDP Formulation for PASS
  11. State Space $\mathcal{S}$
  12. Action Space $\mathcal{A}$
  13. Reward Function $r$
  14. SAC Algorithm Design
  15. Numerical Results
  16. ...and 3 more sections

Figures (5)

  • Figure 1: Illustration of the downlink PASS system with $N$ waveguides and $K$ mobile users in a UMi scenario.
  • Figure 2: Framework of proposed bilevel beamforming optimization system in UMi.
  • Figure 3: Robustness analysis across 5 different random seeds.
  • Figure 4: CDF of episode-wise Sum SE. SAC demonstrates superior tail distribution performance.
  • Figure 5: Case study of autonomous user tracking. (a)-(d): Dynamic snapshots of PAs (orange) and users (blue) showing proactive geometric adjustment. (e)-(h): Real-time power allocation $\|\mathbf{W}_{n,k}\|^2$. The visualization confirms the system's ability to maintain reliable links through coupled antenna positioning and digital precoding.