AI-Empowered Resource Allocation for Wirelessly Powered Pinching-Antenna Systems

TL;DR

This paper introduces a deep reinforcement learning approach to optimize resource allocation in a multi-user wirelessly powered system with adaptive pinching antennas, significantly improving energy efficiency.

Contribution

It presents a novel joint optimization framework for antenna placement, power control, and time-switching, solved via deep reinforcement learning in dynamic, uncertain environments.

Findings

The proposed scheme outperforms fixed-antenna methods in energy efficiency.

Deep reinforcement learning effectively manages complex, coupled variables.

Simulation results confirm substantial EE gains with the PA-assisted system.

Abstract

This paper considers a multi-user system, where the users first harvest energy from the base station and then use the harvested energy to transmit information via non-orthogonal multiple access (NOMA). A pinching antenna array is adopted to assist the energy transfer and information transmission, owing to its ability to adapt to dynamic propagation conditions. To enhance the system's energy efficiency (EE), we formulate a joint optimization problem involving antenna positioning, transmit power control, and time-switching ratio selection. The problem is non-convex due to the coupled variables, nonlinear energy-harvesting characteristics, and uncertainties in user locations and battery states. To effectively solve this problem, a deep reinforcement learning-based algorithm is proposed to autonomously learn near-optimal resource allocation policies in dynamic environments. Simulation results demonstrate that the proposed PA-assisted scheme achieves significant gains in EE compared with conventional fixed-antenna schemes.