Abstract
By leveraging the 5G enabled V2X networks, the vehicles connected by cellular base-stations can support a wide variety of computation-intensive services. In order to solve the arisen challenges in end-to-end low-latency transmission and backhaul resources, mobile edge computing (MEC) is now regarded as a promising paradigm for 5G-V2X communications. Considering the importance of both reliability and delay in vehicle communication, this article innovatively envisions a joint computation and URLLC resource allocation strategy for collaborative MEC assisted cellular-V2X networks and formulate a jointly power consumption optimization problem while guaranteeing the network stability. To solve this NP hard problem, we decouple it into two sub-problems: URLLC resource allocation for multi-cells to multi-vehicles and computation resource decisions among local vehicle, serving MEC server and collaborative MEC server. Secondly, non-cooperative game and bipartite graph are introduced to reduce the inter-cell interference and decide the channel allocation, which aims to maximize the throughput with a guarantee of reliability in URLLC V2X communication. Then, an online Lyapunov optimization method is proposed to solve computation resource allocation to get a trade-off between the average weighted power consumption and delay where CPU frequency are calculated using Gauss-Seidel method. Finally, the simulation results demonstrate that our proposed strategy can get better trade-off performance among power consumption, overflow probability and execution delay than the one based on centralized MEC assisted V2X.
Highlights
As the increasing amount of connected autonomous vehicles, a wide variety of computation-intensive, latency sensitive and power-hungry applications are emerging, such as autonomous driving, image or video-aided real-time navigation, real-time traffic monitoring, etc
We formulate the task offloading problem to minimize the power consumption of collaborative mobile edge computing (MEC) servers and vehicles, which considers the constraint of the task buffer stability for hard delay in V2X
We find the optimal average weighted power consumption and execution delay trade-off under tasks buffers stability constrained by the hard delay in V2X
Summary
As the increasing amount of connected autonomous vehicles, a wide variety of computation-intensive, latency sensitive and power-hungry applications are emerging, such as autonomous driving, image or video-aided real-time navigation, real-time traffic monitoring, etc. B. CONTRIBUTIONS In this paper, we jointly optimize the URLLC radio and computational resources in collaborative MEC assisted cellularV2X networks to minimize the overall power consumption for data offloading. We formulate the task offloading problem to minimize the power consumption of collaborative MEC servers and vehicles, which considers the constraint of the task buffer stability for hard delay in V2X This model can cover the V2X performance requirement on the reliability, power consumption and latency. We design a non-cooperative game power control algorithm to get the optimal edge weight of the bipartite graph Both the utility and cost are considered in the pricing scheme so that the cellular vehicle communication system reaches a Nash equilibrium to pursue the maximized overall rate under the reliability guaranteeing. The vehicle terminals scheduled by the same sub-channel in different cells will result in inter-cell interference (i.e. co-channel interference) to each other, which will cause the transmission rate attenuation and affects the offloading efficiency
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