RIS-based Communication Enhancement and Location Privacy Protection in UAV Networks
Ziqi Chen, Jun Du, Chunxiao Jiang, Tony Q. S. Quek, Zhu Han
TL;DR
This work tackles SU location privacy in UAV networks by introducing an Active RIS (ARIS) with an integrated artificial-noise module and a dynamic virtual partition that splits the surface into ARIS-CE (communication enhancement) and ARIS-LI (localization interference). It derives a RSS-based CRLB for MU localization and develops a multi-objective optimization framework that jointly maximizes RU data rates and localization-error at MUs under average-channel conditions, using adaptive ARIS partitioning to decouple the two tasks. The optimization employs fractional programming and SDP-based alternating schemes to jointly design beamforming, ARIS reflection, and AN for both partitions. Simulation results show substantial improvements in MU localization error with only modest RU rate loss, and demonstrate robustness to varying numbers of MUs and ARIS elements, highlighting practical privacy gains for UAV networks.
Abstract
With the explosive advancement of unmanned aerial vehicles (UAVs), the security of efficient UAV networks has become increasingly critical. Owing to the open nature of its communication environment, illegitimate malicious UAVs (MUs) can infer the position of the source UAV (SU) by analyzing received signals, thus compromising the SU location privacy. To protect the SU location privacy while ensuring efficient communication with legitimate receiving UAVs (RUs), we propose an Active Reconfigurable Intelligent Surface (ARIS)-assisted covert communication scheme based on virtual partitioning and artificial noise (AN). Specifically, we design a novel ARIS architecture integrated with an AN module. This architecture dynamically partitions its reflecting elements into multiple sub-regions: one subset is optimized to enhance the communication rate between the SU and RUs, while the other subset generates AN to interfere with the localization of the SU by MUs. We first derive the Cramér-Rao Lower Bound (CRLB) for the localization with received signal strength (RSS), based on which, we establish a joint optimization framework for communication enhancement and localization interference. Subsequently, we derive and validate the optimal ARIS partitioning and power allocation under average channel conditions. Finally, tailored optimization methods are proposed for the reflection precoding and AN design of the two partitions. Simulation results validate that, compared to baseline schemes, the proposed scheme significantly increases the localization error of the SU by MUs while maintaining efficient communication between the SU and RUs, thereby effectively protecting the SU location privacy.