Name
Abstract | ||
|---|---|---|
This paper deals with the problem of the dynamic multichannel access (MCA) based on the LTE-WLAN aggregation in dynamic heterogeneous networks. To ensure the users' personalized requirements, the minimization after satisfied (MAS) criterion is firstly proposed. Then, a prediction-based deep deterministic policy gradient (P-DDPG) algorithm is presented, achieving continuous processing. Finally, a new reward function is designed according to the MAS criteria. Meanwhile, the virtual user approach and the base station-centric strategy are proposed to design the action spaces so that the number of actions is independent of the number of users. Simulation results demonstrate the effectiveness of the P-DDPG algorithm for solving the dynamic MCA problem and corroborate that the proposed MAS criterion is superior to the existing criteria. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/WCNC.2019.8885857 | 2019 IEEE Wireless Communications and Networking Conference (WCNC) |
Keywords | Field | DocType |
Deep reinforcement learning,dynamic heterogeneous networks,prediction-based deep deterministic policy gradient (P-DDPG),dynamic multichannel access (MCA),access criterion | Computer science,Real-time computing,Minification,Prediction algorithms,Artificial intelligence,Heterogeneous network,Virtual user,Reinforcement learning | Conference |
ISSN | ISBN | Citations |
1525-3511 | 978-1-5386-7647-9 | 0 |
PageRank | References | Authors |
0.34 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaoyang Wang | 1 | 3 | 2.74 |
| Tiejun Lv | 2 | 669 | 97.19 |