RAISE: Optimizing RIS Placement to Maximize Task Throughput in Multi-Server Vehicular Edge Computing
Yanan Ma, Zhengru Fang, Longzhi Yuan, Yiqin Deng, Xianhao Chen, Yuguang Fang
TL;DR
RAISE tackles the challenge of maximizing task throughput in a multi-server VEC system with mobility and occlusions. It jointly optimizes RIS placement (altitude $h_R$ and tilt $theta_R$) and task offloading using a two-layer framework: an inner unimodular-based LP solves the binary offloading problem, while an outer hill-climbing (with grid search as a baseline) discovers near-optimal RIS placements. The key contributions are (i) proving that the task offloading problem is equivalent to its LP due to total unimodularity, (ii) introducing a scalable HC RIS placement algorithm with complexity $O(N_g J)$, and (iii) demonstrating substantial throughput gains over benchmark schemes under probabilistic deadline constraints in diverse mobility scenarios. The approach yields practical insights for deploying RIS-enabled VEC to extend edge computing reach and improve latency-sensitive task performance in dynamic road networks.
Abstract
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 is hindered by large obstacles like trucks/buses and the surge in service demands during rush hours. To address these challenges, Reconfigurable Intelligent Surface (RIS) can be leveraged to mitigate interference from ground signals and reach 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.