Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach

TLDR

Connected vehicle development has been driven by ICT innovations in networking, caching, and computing, yet these technologies have traditionally been studied separately, making joint consideration highly complex. The paper proposes an integrated framework and a deep reinforcement learning approach to dynamically orchestrate networking, caching, and computing resources for improved next‑generation vehicular network performance. The authors formulate resource allocation as a joint optimization problem that jointly accounts for networking, caching, and computing gains, and solve it using deep reinforcement learning. Simulations across various system parameters demonstrate the effectiveness of the proposed integrated framework.

Abstract

The developments of connected vehicles are heavily influenced by information and communications technologies, which have fueled a plethora of innovations in various areas, including networking, caching, and computing. Nevertheless, these important enabling technologies have traditionally been studied separately in the existing works on vehicular networks. In this paper, we propose an integrated framework that can enable dynamic orchestration of networking, caching, and computing resources to improve the performance of next generation vehicular networks. We formulate the resource allocation strategy in this framework as a joint optimization problem, where the gains of not only networking but also caching and computing are taken into consideration in the proposed framework. The complexity of the system is very high when we jointly consider these three technologies. Therefore, we propose a novel deep reinforcement learning approach in this paper. Simulation results with different system parameters are presented to show the effectiveness of the proposed scheme.