RIS-Assisted Secure Transmission with Artificial Noise: Element Allocation and Measurements

Physical layer security in reconfigurable intelligent surface (RIS)-assisted wireless systems can be improved through coordinated control of signal transmission and RIS configuration. In this work, the base station simultaneously transmits the communication signal (CS) and artificial noise (AN) in the presence of a potential eavesdropper. The RIS is partitioned into two groups of reflecting elements, where a portion enhances the desired CS toward the legitimate receiver, while the remaining elements contribute to AN transmission. Two key parameters govern the system design: a transmit power allocation factor between CS and AN, and an RIS element allocation ratio controlling the partitioning of the reflecting elements. An iterative binary phase optimization strategy is employed to enhance the received signal power at Bob while degrading Eve's reception. Simulation and experimental results demonstrate that proper joint design significantly improves the achievable secrecy capacity.

Receiver-Centric TDOA Localization Framework for DAB Signals under Synchronization Impairments

In Global Navigation Satellite System (GNSS)-denied environments, terrestrial signals of opportunity (SoOP) offer an alternative for positioning, but synchronization impairments such as clock offsets, drift, and multipath limit performance. This paper proposes a receiver-centric multi-channel time-difference-of-arrival (TDOA) localization framework based on Digital Audio Broadcasting (DAB) signals. The method exploits the DAB null symbol for coarse timing and the phase reference symbol (PRS) for fine synchronization, followed by sub-sample time-of-arrival (TOA) estimation. A double-difference formulation removes inter-receiver clock offsets, while a peak-to-sidelobe ratio (PSR)-based weighting improves robustness. A bias correction step mitigates errors due to multipath. Finally, a coordinated-turn extended Kalman filter (CT-EKF) further refines position estimates. Results show improved accuracy over conventional TDOA with Gauss-Newton estimation, especially in challenging conditions.