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

2026-05-17

AI Summary

This work addresses integrated sensing and communication (ISAC) systems by introducing, for the first time, reconfigurable pinching antennas (PAs) at the transmitter and movable antennas (MAs) at the users to maximize total communication rate while satisfying a minimum sensing performance requirement. The authors derive a closed-form solution for the optimal sensing receive combiner and design an unsupervised deep neural network to jointly optimize the positions of transmit PAs and user MAs, along with communication precoding and sensing beamforming. By innovatively combining closed-form analytical methods with data-driven learning, the proposed framework overcomes the limitations of conventional fixed-antenna architectures. Experimental results demonstrate that the scheme significantly enhances the system sum rate, with gains increasing as transmit power rises, and reveal that communication performance is particularly sensitive to the sensing SINR threshold.

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.

Problem

• ISAC
• pinching antennas
• movable antennas
• sum-rate optimization
• sensing constraints

Innovation

• pinching antennas
• movable antennas
• deep learning
• ISAC optimization
• unsupervised learning