Name
Abstract | ||
|---|---|---|
In mobile ad-hoc networks (MANETs), network performance depends strongly on routing, therefore intensive researches about routing protocol have been developed. However, most traditional routing protocols suffer from significant performance degradation as they attempt to find a shortest path without considering the impact of congestion and channel quality. In this work, we propose an intelligent routing algorithm based on prioritized replay double deep Q-network (PRD-DQN). We design two kinds of packet, fast routing (FR) packet and experience transfer (ET) packet, to explore network, and a reward function is defined in which congestion and channel quality are both considered for adaptive routing decision. The simulation results demonstrate that the proposed algorithm can effectively learn a routing strategy with low congestion and high signal to noise ratio (SNR) and outperform the Q-Learning algorithm and the optimized link state routing (OLSR) protocol in terms of convergence speed, end-to-end latency and network throughput. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/VTC2020-Fall49728.2020.9348471 | 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall) |
Keywords | DocType | ISSN |
Mobile ad-hoc network,routing protocol,prior-itized replay double deep Q-network | Conference | 1090-3038 |
ISBN | Citations | PageRank |
978-1-7281-9485-1 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jue Cai | 1 | 0 | 0.34 |
| Chan Wang | 2 | 0 | 2.03 |
| Ming Lei | 3 | 4 | 3.85 |
| Minjian Zhao | 4 | 224 | 34.77 |