DL-Driven Optimization for ISAC System Equipped With Pinching and Movable Antennas

TL;DR

This paper proposes a deep learning-based optimization approach for an ISAC system with pinching and movable antennas, enhancing sum-rate performance while satisfying sensing requirements.

Contribution

It introduces a DL network to optimize antenna positions and precoding in an ISAC system with PAs and MAs, providing a closed-form solution for the sensing combiner.

Findings

Using PAs and MAs increases sum-rate compared to fixed antennas.

Performance gains are more significant at higher transmit power.

Communication performance is more sensitive to sensing SINR thresholds.

Abstract

Integrated sensing and communication (ISAC) is considered to be a promising technology for future wireless systems due to its ability to provide communication and sensing services using shared hardware and spectrum resources. Moreover, the introduction of recently developed pinching antennas (PAs) and movable antennas (MAs) has the potential to further improve the performance gains of ISAC. Therefore, our goal is to study the optimization of the sum-rate for an ISAC system equipped with PAs and MAs, capable of satisfying minimal sensing requirements. To achieve it, we derive a closed-form solution for the optimal sensing receive combiner, and show that it is determined by other optimization variables. For these other variables (i.e., the positions of the transmit PAs, the positions of the users' MAs, the communication precoding matrices, and the sensing transmit beamformer), we propose a deep learning (DL) network that finds their optimal values. To train the network in an unsupervised manner, we formulate a loss function consisting of the objective function, as well as the penalty terms related to the constraints for the PAs and MAs positions. Simulation results show that using PAs and MAs in ISAC systems provides a larger sum-rate compared to ISAC systems with only fixed antennas, and that this performance advantage is increased with the maximum transmit power. Furthermore, we demonstrate that the communication performance of the considered system is a bit more affected by the sensing signal-to-interference-plus-noise ratio (SINR) threshold compared to the sensing performance.