Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks: Design Optimization and Analysis

TL;DR

This paper introduces a reconfigurable intelligent computational surface (RICS) to optimize task offloading and interference management in MEC-assisted autonomous driving networks, significantly enhancing safety and V2V data rates.

Contribution

It proposes a novel RICS deployment for joint interference cancellation and spectrum sharing, with an optimization framework and solution approach for autonomous driving safety enhancement.

Findings

34% improvement in safety coefficient of CVs

60% increase in V2V data rate

Effective interference mitigation via RICS adjustments

Abstract

This paper investigates autonomous driving safety improvement via task offloading from cellular vehicles (CVs) to a multi-access edge computing (MEC) server using vehicle-to-infrastructure (V2I) links. Considering that the latter links can be reused by vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of the V2I link may suffer from severe interference that can cause outages during the task offloading. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) whose computationally capable metamaterials are leveraged to jointly enable V2I reflective links as well as to implement interference cancellation at the V2V links. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices to maximize an autonomous driving safety task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems solved via an alternate approximation method. Our simulation results showcase that the proposed RICS-assisted offloading framework significantly improves the safety of the considered autonomous driving network, yielding a nearly 34\% improvement in the safety coefficient of the CVs. In addition, it is demonstrated that the V2V data rate can be improved by around 60\% indicating that the RICS-induced adjustment of the signals can effectively mitigate interference at the V2V link.