RL based Beamforming Optimization for 3D Pinching Antenna assisted ISAC Systems

TL;DR

This paper introduces a 3D pinching antenna array deployment and a reinforcement learning algorithm to optimize beamforming in ISAC systems, significantly improving communication and sensing performance.

Contribution

It proposes a novel 3D antenna deployment scheme and a HGRL algorithm for joint optimization in ISAC systems, outperforming existing methods.

Findings

3D deployment outperforms 1D and 2D in ISAC systems.

HGRL algorithm achieves better performance and faster convergence.

Simulation results validate the effectiveness of the proposed methods.

Abstract

In this paper, a three-dimensional (3D) deployment scheme of pinching antenna array is proposed, aiming to enhances the performance of integrated sensing and communication (ISAC) systems. To fully realize the potential of 3D deployment, a joint antenna positioning, time allocation and transmit power optimization problem is formulated to maximize the sum communication rate with the constraints of target sensing rates and system energy. To solve the sum rate maximization problem, we propose a heterogeneous graph neural network based reinforcement learning (HGRL) algorithm. Simulation results prove that 3D deployment of pinching antenna array outperforms 1D and 2D counterparts in ISAC systems. Moreover, the proposed HGRL algorithm surpasses other baselines in both performance and convergence speed due to the advanced observation construction of the environment.