Bibliographic Details
| Title: |
Deep Reinforcement Learning for Router Selection in Network With Heavy Traffic |
| Authors: |
Ruijin Ding, Yadong Xu, Feifei Gao, Xuemin Shen, Wen Wu |
| Source: |
IEEE Access, Vol 7, Pp 37109-37120 (2019) |
| Publisher Information: |
IEEE, 2019. |
| Publication Year: |
2019 |
| Collection: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| Subject Terms: |
Deep reinforcement learning, routing, network congestion, network throughput, deep Q network, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| More Details: |
The rapid development of wireless communications brings a tremendous increase in the amount number of data streams and poses significant challenges to the traditional routing protocols. In this paper, we leverage deep reinforcement learning (DRL) for router selection in the network with heavy traffic, aiming at reducing the network congestion and the length of the data transmission path. We first illustrate the challenges of the existing routing protocols when the amount of the data explodes. We then utilize the Markov decision process (RSMDP) to formulate the routing problem. Two novel deep Q network (DQN)-based algorithms are designed to reduce the network congestion probability with a short transmission path: one focusing on reducing the congestion probability; while the other focuses on shortening the transmission path. The simulation results demonstrate that the proposed algorithms can achieve higher network throughput comparing to existing routing algorithms in heavy network traffic scenarios. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/8673947/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2019.2904539 |
| Access URL: |
https://doaj.org/article/5acc798d4a2b4d828859c18801026f36 |
| Accession Number: |
edsdoj.5acc798d4a2b4d828859c18801026f36 |
| Database: |
Directory of Open Access Journals |
