Name
Abstract | ||
|---|---|---|
Wireless networks are trending towards large scale systems, containing thousands of nodes, with multiple co-existing applications. Congestion is an inevitable consequence of this scale and complexity, which leads to inefficient use of the network capacity. This paper proposes an autonomous and adaptive wireless network management framework, utilising multi-agent deep reinforcement learning, to achieve efficient use of the network. Its novel reward function incorporates application awareness and fairness to address both node and network level objectives. Our experimental results demonstrate the proposed approach's ability to be optimised for application-specific requirements, while optimising the fairness of the network. The results reveal significant performance benefits in terms of adaptive data rate and an increase in responsiveness compared to a single-agent approach. Some significant qualitative benefits of the multi-agent approach-network size independence, node-led priorities, variable iteration length, and reduced search space-are also presented and discussed. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/s22031019 | SENSORS |
Keywords | DocType | Volume |
large scale networks, congestion, reinforcement learning, multi-agent, LoRaWAN, fairness, application awareness | Journal | 22 |
Issue | ISSN | Citations |
3 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ameer Ivoghlian | 1 | 0 | 0.34 |
| Zoran Salcic | 2 | 0 | 0.68 |
| Kevin I-Kai Wang | 3 | 0 | 0.34 |