A Distributed Abstract MAC Layer for Cooperative Learning on Internet of Vehicles

This paper addresses the problem of reliable communications for cooperative learning on Internet-of-Vehicles, where a large amount of data from users and services needs to be processed. Previous works have proposed various cooperative learning schemes, but they often assume that the communications between vehicles are reliable, without considering how to achieve this in an Internet-of-Vehicles network. This paper is the first one that implements an abstract MAC layer using a distributed deep reinforcement learning scheme, which can directly meet the reliable communication requirements of cooperative learning in previous works. Our abstract MAC layer performs two operations:
acknowledgement
, which makes sure that all vehicles can successfully broadcast their messages to all of their neighbors, and
progress
, which ensures that each vehicle can receive at least one message from its neighbors. These operations facilitate vehicles to exchange and update their training models in a cooperative learning service. Our simulation results show the efficiency and fairness of our deep reinforcement learning abstract MAC layer.