RL Based Beamforming Optimization for 3D Pinching Antenna Assisted ISAC Systems

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 show that the proposed 3D pinching antenna deployment improves the ISAC performance significantly, achieving around 10–20% higher communication rate compared with conventional 1D/2D layouts, while maintaining the sensing SNR near the required threshold with substantially lower power consumption. Moreover, the proposed HGRL algorithm surpasses other baselines in both performance and convergence speed due to the advanced observation construction of the environment.