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.

翻译：集成感知与通信 (ISAC) 因其能利用共享硬件和频谱资源提供通信与感知服务，被视为未来无线系统的关键技术。此外，近期发展的夹持天线 (PA) 与可移动天线 (MA) 的引入有望进一步提升ISAC的性能增益。因此，本文旨在研究配备PA和MA且能够满足最小感知需求的ISAC系统总速率优化。为实现此目标，我们推导出最优感知接收合路器的闭式解，并证明该解由其他优化变量决定。针对这些其他变量（即发射PA的位置、用户MA的位置、通信预编码矩阵及感知发射波束形成器），我们提出一种深度学习 (DL) 网络以求解其最优值。为以无监督方式训练该网络，我们构建了一个由目标函数及与PA和MA位置约束相关的惩罚项组成的损失函数。仿真结果表明，在ISAC系统中使用PA和MA相较于仅使用固定天线的ISAC系统能提供更大的总速率，且此性能优势随最大发射功率增加而增强。此外，我们证明所考虑系统的通信性能受感知信干噪比 (SINR) 阈值的影响略大于感知性能。