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

2025-03-22

AI Summary

To address the limited task throughput and stringent latency requirements in vehicle-infrastructure cooperative edge computing—caused by occlusions from obstacles and service overload—this paper jointly optimizes the three-dimensional physical deployment (height and tilt angle) of reconfigurable intelligent surfaces (RIS) and multi-server task offloading strategies. We formulate the integrated RIS deployment and task scheduling problem as a mixed-integer nonlinear program (MINLP) for the first time. A two-layer optimization framework is proposed: an inner layer exploits the unimodality of task allocation for efficient computation, while an outer layer employs a low-complexity, near-optimal hill-climbing algorithm. By incorporating RIS-enabled channel modeling and beamforming optimization, our approach achieves up to 41.7% improvement in task throughput across diverse urban scenarios, with over 99.2% of tasks meeting sub-millisecond latency constraints.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Optimize RIS placement to enhance VEC task throughput
• Address signal interference and server access in vehicular networks
• Balance RIS altitude, tilt angle, and task assignment

Innovation

• Optimizes RIS altitude and tilt angle
• Two-layer optimization approach
• Near-optimal hill climbing algorithm