Abstract
The potential degradation of the Global Positioning System and other Global Navigation Satellite Systems under several circumstances gives rise to the development of alternative position, navigation, and timing (PNT) technologies aiming at maintaining efficient and safe operations. In this article, we exploit the use of reconfigurable intelligent surfaces (RISs) as enablers for the design of an alternative PNT solution, with improved accuracy and efficiency. The specific problem of RISs' orchestration and configuration is treated via the adoption of game theory and reinforcement learning (RL). Initially, a satisfaction game is formulated and solved among the targets, enabling them to autonomously determine the optimal number of RISs that will contribute to their PNT service, while the specific set of RISs to be used is determined by a novel RL algorithm. In order to further maximize the received signal strength at each target of the reflected signals from the specific set of RISs, the phase-shift optimization of the latter is performed. Based on the above, an iterative least squares algorithm is adopted, following the multilateration technique, in order for each target to estimate its position and timing. The performance evaluation of the proposed approach is achieved via modeling and simulation.
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