Antenna Placement Design for Interference Exploitation in Pinching-Antenna Systems

TL;DR

This paper explores the integration of pinching-antenna systems with symbol-level precoding to enhance wireless communication performance, optimizing antenna placement and beamforming to reduce power consumption and improve signal quality.

Contribution

It introduces a joint optimization framework for antenna placement and beamforming in PASs using SLP, employing an alternating optimization approach with a PGD-based method for efficient solution.

Findings

Performance improvements verified through simulations.

Effective antenna placement enhances interference exploitation.

Proposed algorithm achieves near-optimal power minimization.

Abstract

Pinching-antenna systems (PASs) have been proposed as a flexible antenna technology to fulfill the stringent requirements of high data rate and large-scale equipment deployment in future wireless networks. The principle of PA involves mapping a signal over dielectric waveguides for transmission. By adjusting the positions of pinching antennas (PAs) over the waveguides, with the aim of gain enhancement for line-of-sight links and the reduction of large-scale path loss. Symbol-level precoding (SLP) is a nonlinear precoding technique, which converts multi-user interference into constructive interference via beamforming design at symbol level. In this paper, we study the combination of SLP and PAS, leveraging the advantages of PAS to further enhance the ability of SLP to convert constructive interference. The transmit power minimization problem is formulated and solved for the multiple waveguides multiple PAs system by jointly beamforming and PAs' positions design under the SLP principle. The alternating optimization (AO) framework is applied to decouple the beamforming vector and the position coefficient of PA. For the given beamforming vectors, a new objective function is formulated with respect to the positions of the PAs. With the characteristics of the formulated objective function, the optimization problem for the position coefficients of PAs can be decomposed into multiple independent subproblems, each corresponding to a PA's position coefficient, and a projected gradient descent (PGD)-based method, constrained by the feasible movable region of each PA, is then developed to obtain the suboptimal position coefficients. The performance improvements achieved by the combination of PAS and SLP, as well as the effectiveness of the proposed algorithm are verified through the simulation results.