Name
Abstract | ||
|---|---|---|
In this article, we investigate spectrum allocation in vehicle-to-everything (V2X) network. We first express the V2X network into a graph, where each vehicle-to-vehicle (V2V) link is a node in the graph. We apply a graph neural network (GNN) to learn the low-dimensional feature of each node based on the graph information. According to the learned feature, multi-agent reinforcement learning (RL) is used to make spectrum allocation. Deep Q-network is utilized to learn to optimize the sum capacity of the V2X network. Simulation results show that the proposed allocation scheme can achieve near-optimal performance. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/GLOBECOM42002.2020.9322537 | GLOBECOM 2020 - 2020 IEEE Global Communications Conference |
Keywords | DocType | ISSN |
Vehicular Communications,Multi-agent RL,GNN,Resource Allocation | Conference | 1930-529X |
ISBN | Citations | PageRank |
978-1-7281-8299-5 | 1 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ziyan He | 1 | 6 | 1.10 |
| Liang Wang | 2 | 19 | 2.65 |
| Hao Ye | 3 | 11 | 1.98 |
| Geoffrey Ye Li | 4 | 937 | 99.10 |
| Biing-Hwang Juang | 5 | 3388 | 699.72 |