Pinching-Antenna Enabled Multicell Wireless Systems

2026-06-11

AI Summary

This work addresses the weighted sum-rate maximization problem in multi-cell wireless networks, which is highly coupled and non-convex due to inter-cell interference. For the first time, it introduces a reconfigurable intelligent surface (RIS)-assisted “pinch antenna” architecture into multi-cell systems, jointly optimizing precoding matrices, power allocation, and antenna positions to actively reshape the wireless propagation environment. An efficient alternating optimization framework is developed by integrating fractional programming, block coordinate descent, and particle swarm optimization (PSO), effectively decoupling and solving the high-dimensional non-convex problem. Numerical experiments demonstrate that the proposed method significantly outperforms baseline schemes—including equal power allocation, fixed antenna deployment, and conventional MIMO—across various system configurations.

Abstract

Pinching antenna (PA) systems have recently emerged as a promising flexible-antenna technology, which can reconstruct the wireless propagation environment by dynamically adjusting the positions of pinching elements along dielectric waveguides, thereby providing new spatial degrees of freedom (DoFs) for enhancing wireless system performance. This paper investigates a multi-waveguide PA-based multi-cell communication system, focusing on the joint optimization of precoding matrices, waveguide power allocation, and antenna placement to maximize the weighted sum rate (WSR). In multi-cell scenarios, inter-cell interference typically leads to a highly coupled and nonconvex WSR maximization problem. To address this challenge, an efficient alternating optimization framework is adopted to optimize each variable in an iterative way. Specifically, fractional programming is first employed to reformulate the original problem by introducing auxiliary variables that decouple the signal and interference terms. Based on this reformulation, block coordinate descent is then applied to optimize the precoding matrices and power allocation, leading to closed-form or semi-closed-form updates. For the high-dimensional and nonconvex PA placement problem, particle swarm optimization (PSO) is utilized to perform an efficient search and improve scalability. Numerical results demonstrate that, under various system configurations, the proposed scheme significantly outperforms baseline methods, including average power allocation, fixed antenna placement, conventional multiple-input multiple-output (MIMO), and massive MIMO. These results highlight the strong potential of PA systems for large-scale multi-cell wireless communications.

Problem

• pinching antenna
• multi-cell wireless systems
• weighted sum rate
• inter-cell interference
• nonconvex optimization

Innovation

• Pinching Antenna
• Weighted Sum Rate Maximization
• Alternating Optimization
• Fractional Programming
• Particle Swarm Optimization