Vehicular system

Mobility-Aware Robust Resource Allocation and Computation Migration in Heterogeneous Virtualized Vehicular Networks by Low Overhead Multi-Agent DRL-based Approach

In emerging vehicular networks, delay-sensitive tasks can be processed in real time by offloading to

the edge computing servers. Unlike the legacy scenarios, in this paper a novel data offloading/delivery

decision making framework is proposed, where users have the option to divide their task into several

portions and partially offload their data to a complex multi-access edge computing (MEC) environment,

consisting of several MEC servers located on road side units (RSUs), base stations (BSs), and unused

Categories:
Machine Learning
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