Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks
CoRR(2024)
摘要
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.
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