Using Deep Reinforcement Learning to Automate Network Configurations for Internet of Vehicles

In this paper, we address the issue of automating network configurations for dynamic network environments such as the Internet of Vehicles (IoV). Configuring network settings in IoV environments has proven difficult due to their dynamic and self-organizing nature. To address this issue, we propose a deep reinforcement learning-based approach to configure IoV network settings automatically. Specifically, we use a collection of neural networks to convert the observations of a communication environment (channel power gain, cross-channel power gain, etc.) into key features, which are then supplied to a deep Q neural network (DQN) as input for training. Afterward, the DQN will select the optimal network configuration for vehicles in the IoV environment. In addition, our approach considers both centralized and distributed training strategies. The centralized training strategy conducts the DQN training process on a roadside server, while the distributed training strategy trains the DQN on vehicles locally. Through our designed IoV simulation platform, we evaluate the efficacy of our proposed approach, demonstrating that it can improve the quality of services (QoS) in the IoV environments concerning reliability, latency, and service satisfaction.

Automated summary

In this paper, the authors propose a deep reinforcement learning-based approach to automate network configurations in dynamic network environments such as the Internet of Vehicles (IoV). They use a collection of neural networks to convert the observations of a communication environment into key features and then train a deep Q neural network (DQN) to select optimal network configurations for vehicles in the IoV environment. They also consider both centralized and distributed training strategies and evaluate the efficacy of their approach using an IoV simulation platform.