CaMPASS-Net: A Deep Learning Framework on Capacity Maximization for MIMO Pinching Antenna Systems in IoT

Pinching antenna system (PASS) has been demonstrated as a feasible flexible-antenna technology for upcoming 6G wireless networks and Internet-of-Things (IoT). In this paper, we investigate a new design problem on capacity maximization for a point-to-point multiple-input multiple-output (MIMO) PASS in a realistic IoT environment by jointly optimizing precoding matrix and antenna positioning. Unfortunately, this problem is not mathematically tractable. To break through this challenge in an effective and intelligent manner, we propose a novel and high-performing deep learning framework, named CaMPASS-Net, based on an advanced dual-stream network architecture with a residual connection, inspired by our insight into the problem. Furthermore, we present an effective unsupervised training strategy for the proposed CaMPASS-Net based on an innovative loss function design. Simulation results confirm that the proposed CaMPASS-Net exhibits remarkable performance improvements over baseline and existing schemes.