Name
Abstract | ||
|---|---|---|
With the development of heterogeneous wireless networks, it is particularly important to build a reasonable network selection mechanism of user in the 5G heterogeneous networks. In this paper, we improve the reward function in Q-Learning using the AHP (Analytic Hierarchy Process) method and make a simple analysis about network resources competition in the case of multi-agent scenario. Then we propose two network selection algorithms: SANSA (single agent network selection algorithm) and MANSA (multi-agent network selection algorithm) which are based on Q-Learning and Nash Q-Learning respectively to deal with the network selection problem. Simulations show that our proposed algorithms have a better performance of network load balancing than the contrast scheme. In addition, the MANSA can effectively reduce the system total power consumption. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICT.2019.8798797 | 2019 26th International Conference on Telecommunications (ICT) |
Keywords | Field | DocType |
Heterogeneous wireless networks,Network selection,Q-Learning,Nash Q-Learning | Wireless network,Resource (disambiguation),Network Load Balancing,Computer science,Selection algorithm,Q-learning,Computer network,Heterogeneous network,Analytic hierarchy process,Power consumption | Conference |
ISBN | Citations | PageRank |
978-1-7281-0274-0 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoqian Wang | 1 | 335 | 16.72 |
| Xin Su | 2 | 283 | 53.83 |
| Bei Liu | 3 | 26 | 12.94 |