Integrated Sensing and Communication for Segmented Waveguide-Enabled Pinching Antenna Systems

TL;DR

This paper introduces an integrated sensing and communication system using segmented waveguides and a reinforcement learning-based optimization to enhance performance and reduce hardware costs.

Contribution

It proposes a novel SWAN-ISAC system with a joint optimization framework and a segment hysteresis reinforcement learning algorithm for improved sensing and communication.

Findings

SWAN-ISAC outperforms baseline schemes in simulations.

HARL algorithm achieves better results than conventional RL methods.

The system reduces hardware costs while maintaining high performance.

Abstract

In this paper, an integrated sensing and communication (ISAC) design for segmented waveguide-enabled pinching-antenna array (SWAN) systems is proposed to improve the performance of systems by leveraging the low in-waveguide propagation loss of segmented waveguides. The hybrid segment selection and multiplexing (HSSM) protocol is implemented to provide favorable performance with less hardware cost. To achieve this, a joint transmit beamforming optimization, segment selection, and pinching antenna positioning problem is formulated to maximize the sum communication rate with the constraints of sensing performance. To solve the maximization problem, we propose a segment hysteresis based reinforcement learning (SHRL) algorithm to learn segment selection and pinching antenna positions in different progress to explore better strategies. Simulation results demonstrate that 1) the proposed SWAN-ISAC scheme outperforms the other baseline schemes, and 2) the proposed HARL algorithm achieves better performance compared to conventional RL algorithms.