Name
Title | ||
|---|---|---|
Deep-Reinforcement-Learning-Based Mode Selection and Resource Allocation for Cellular V2X Communications |
Abstract | ||
|---|---|---|
Cellular vehicle-to-everything (V2X) communication is crucial to support future diverse vehicular applications. However, for safety-critical applications, unstable vehicle-to-vehicle (V2V) links, and high signaling overhead of centralized resource allocation approaches become bottlenecks. In this article, we investigate a joint optimization problem of transmission mode selection and resource allocation for cellular V2X communications. In particular, the problem is formulated as a Markov decision process, and a deep reinforcement learning (DRL)-based decentralized algorithm is proposed to maximize the sum capacity of vehicle-to-infrastructure users while meeting the latency and reliability requirements of V2V pairs. Moreover, considering training limitation of local DRL models, a two-timescale federated DRL algorithm is developed to help obtain robust models. Wherein, the graph theory-based vehicle clustering algorithm is executed on a large timescale and in turn, the federated learning algorithm is conducted on a small timescale. The simulation results show that the proposed DRL-based algorithm outperforms other decentralized baselines, and validate the superiority of the two-timescale federated DRL algorithm for newly activated V2V pairs. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JIOT.2019.2962715 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Resource management,Vehicle-to-everything,Reliability,Clustering algorithms,Quality of service,Interference,Reinforcement learning | Journal | 7 |
Issue | ISSN | Citations |
7 | 2327-4662 | 15 |
PageRank | References | Authors |
0.49 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinran Zhang | 1 | 38 | 12.02 |
| Mugen Peng | 2 | 2779 | 200.37 |
| Shi Yan | 3 | 38 | 3.91 |
| Yaohua Sun | 4 | 153 | 9.72 |