Enhancing Sensing Accuracy in AN Transmission-Based Secure Multi-Target Massive MIMO ISAC

Abstract

The integration of sensing and communication is one of the key feature for 6G networks, but its deployment raises serious concerns over privacy, security, and robust target detection under hostile conditions. In downlink massive multiple-input multiple-output systems, this letter examines the design of a secure multi-target integrated sensing and communication (ISAC) framework under both the availability and absence of eavesdroppers’ (Eves’) channel state information (CSI), as well as under imperfect CSI, for which a robust secure design is proposed under bounded CSI error model. We aim to minimize the direction-of-arrival (DoA) estimation error in terms of root mean square error (RMSE) while satisfying signal-to-interference-plus-noise ratio (SINR) constraints at legitimate users and secrecy constraints at the Eves. We address the dual challenge of robust sensing and secure communication in the presence of Eves. To ensure physical layer security, we incorporate the concept of artificial noise (AN) into the precoding design and analyze the performance in terms of sensing accuracy and secrecy. Simulation results indicate that AN significantly improves the sensing performance, reducing the RMSE of DoA in comparison with the non-AN baseline. Additionally, in the absence of Eve’s CSI, the Eve’s SINR is suppressed up-to significant levels under tight secrecy constraints. The simulation results highlight the dual role of AN in maintaining both sensing fidelity and communication secrecy across a wide range of power budgets and security requirements.