Optimizing Information Freshness in RIS-Assisted Cooperative Autonomous Driving
Ibrahim Sorkhoh, Mohamed Amine Arfaoui, Maurice Khabbaz, Chadi M. Assi · 2022
Cooperative-Autonomous-Driving (CAD) systems stringently require that vehicle status information (e.g, speed, position, etc) be timely disseminated for safety reasons. CAD systems rely on real-time information to make critical decisions; hence, the paramount criticality of temporally valid information generation and dissemination. However, the timely information update messages’ delivery faces numerous challenges due to the highly alternating wireless signal propagation in vehicular environments as a result of, for instance, shadowing and blockage, which lead to the unavailability of reliable communication links between cooperating vehicles. Under such harsh conditions, Reconfigurable-Intelligent-Surfaces (RISs) have been proven to highly contribute in mitigating the propagation-induced impairments of the wireless environments and, hence, promoting more robust communication links, which, in turn, allow for maintaining the required freshness of information. In the above-context, this paper revolves around the minimization of the Age-of-Information (AoI) perceived by each of a CAD system’s destination node. The problem is formulated as an Integer-Linear-Program (ILP), which turns out to be quite complex. To work around this complexity, it is proposed herein to use decomposition based on the Lagrangian relaxation method, which largely facilitates the problem’s resolution following typical dynamic programming methodologies. Consequently a feasible solution is extracted using a relatively simple heuristic. An analytical framework is established to reveal insights into the proposed solution and gauge its merits through the establishment of a thorough simulation framework involving various scenarios aiming at verifying its correctness, validity and superiority as compared to other solutions derived using the state-of-the-art branch-and-cut method implemented by CPLEX.