Computer Science > Distributed, Parallel, and Cluster Computing
arXiv:2407.00933 (cs)
[Submitted on 1 Jul 2024]
Title:Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks: Design Optimization and Analysis
Authors:Xueyao Zhang,Bo Yang,Zhiwen Yu,Xuelin Cao,George C. Alexandropoulos,Yan Zhang,Merouane Debbah,Chau Yuen
View a PDF of the paper titled Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks: Design Optimization and Analysis, by Xueyao Zhang and 6 other authors
View PDFHTML (experimental)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.
| Subjects: | Distributed, Parallel, and Cluster Computing (cs.DC); Signal Processing (eess.SP) |
| Cite as: | arXiv:2407.00933 [cs.DC] |
| (orarXiv:2407.00933v1 [cs.DC] for this version) | |
| https://doi.org/10.48550/arXiv.2407.00933 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks: Design Optimization and Analysis, by Xueyao Zhang and 6 other authors
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