RAISE: Optimizing RIS Placement to Maximize Task Throughput in Multi-Server Vehicular Edge Computing

Given the limited computing capabilities on autonomous vehicles, onboard processing of large volumes of latency-sensitive tasks presents significant challenges. While vehicular edge computing (VEC) has emerged as a solution, offloading data-intensive tasks to roadside servers or other vehicles faces a communication-computing bottleneck, such as signal blockage from other large vehicles and limited computing resources of roadside servers. To address these challenges, Reconfigurable Intelligent Surface (RIS) can be leveraged to create line-of-sight channels, mitigate interference on the ground, and extend connectivity to more edge servers by elevating RIS adaptively. To this end, we propose RAISE, an optimization framework for RIS placement in multi-server VEC systems. Specifically, RAISE optimizes RIS altitude and tilt angle together with the optimal task assignment to maximize task throughput under deadline constraints. To find a solution, a two-layer optimization approach is proposed, where the inner layer exploits the unimodularity of the task assignment problem to derive the efficient optimal strategy while the outer layer develops a near-optimal hill climbing (HC) algorithm for RIS placement with low complexity. Extensive experiments demonstrate that the proposed RAISE framework consistently outperforms existing benchmarks.