Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914662687178752 |
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| author | Yang, Bo Zhang, Xueyao Yu, Zhiwen Cao, Xuelin Huang, Chongwen Alexandropoulos, George C. Zhang, Yan Debbah, Merouane Yuen, Chau |
| author_facet | Yang, Bo Zhang, Xueyao Yu, Zhiwen Cao, Xuelin Huang, Chongwen Alexandropoulos, George C. Zhang, Yan Debbah, Merouane Yuen, Chau |
| contents | In this paper, we focus on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to an multi-access edge computing (MEC) server. Considering that the frequencies used for V2I links can be reused for vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of each V2I link may suffer from severe interference, causing outages in the task offloading process. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links, but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. 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, with the objective to maximize a safety-based autonomous driving 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 and solved via an alternate approximation method. Our simulation results demonstrate the effectiveness of the proposed RICS optimization in improving the safety in autonomous driving networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00398 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks Yang, Bo Zhang, Xueyao Yu, Zhiwen Cao, Xuelin Huang, Chongwen Alexandropoulos, George C. Zhang, Yan Debbah, Merouane Yuen, Chau Distributed, Parallel, and Cluster Computing Signal Processing In this paper, we focus on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to an multi-access edge computing (MEC) server. Considering that the frequencies used for V2I links can be reused for vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of each V2I link may suffer from severe interference, causing outages in the task offloading process. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links, but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. 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, with the objective to maximize a safety-based autonomous driving 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 and solved via an alternate approximation method. Our simulation results demonstrate the effectiveness of the proposed RICS optimization in improving the safety in autonomous driving networks. |
| title | Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks |
| topic | Distributed, Parallel, and Cluster Computing Signal Processing |
| url | https://arxiv.org/abs/2402.00398 |