Physical Layer Security with Artificial Noise in MIMO Pinching-Antenna Systems

As next-generation wireless networks emerge, security is becoming a critical performance metric. However, conventional multiple-input-multiple-output (MIMO) systems often suffer from severe path loss and are vulnerable to nearby eavesdroppers due to their fixed-antenna configurations. Pinching-antenna systems (PASs) offer a promising alternative, leveraging reconfigurable pinching antennas (PAs) positioned along low-loss dielectric waveguides to enhance channel conditions and dynamically mitigate security threats. In this paper, we propose an artificial noise (AN)-aided beamforming framework for the PAS downlink that maximizes the secrecy rate (SR) by jointly optimizing the information beams, the AN covariance, and the PA positions. We examine both perfect and imperfect channel state information (CSI) for the eavesdropper's channel. For the latter, location errors are mapped via a Jacobian into an ellipsoidal channel uncertainty set to accurately formulate the problem. We derive a closed-form solution for the single-waveguide scenario, yielding the optimal PA location and an information/AN power-splitting rule. For multiple waveguides and users, we develop a deep neural network (DNN)-aided joint optimizer that outputs beams, AN, and PA placements. Numerical results demonstrate that the proposed scheme improves SR consistently over PAS baselines in single- and multi-user settings under both perfect and imperfect CSI.