Name
Title | ||
|---|---|---|
Reinforcement Learning Based Dynamic Energy-Saving Algorithm For Three-Tier Heterogeneous Networks |
Abstract | ||
|---|---|---|
To cope with the rapidly growing demand for data traffic, heterogeneous network (HetNet), including different types of base stations (BSs), is advocated as a promising network architecture. Considering the different quality of service (QoS) requirements of the Internet of things (IoT) users and ordinary users, we propose a three-tier HetNet model with non-equal bandwidth. To reduce the power consumption caused by the dense deployment of BSs, we propose a novel reinforcement-learning (RL) based dynamic pico-cell base station (PBS) operation scheme. The proposed RL scheme is based on the asynchronous advantage actor-critic (A3C) algorithm, and can dynamically determine the on/off state of each PBS, aiming to achieve the minimal total power of the macro-cell without any prior information. Simulation results show that the proposed algorithm can achieve 92.1% performance gain of the optimal level in terms of the power consumption saving while needs less training time and lower running resource requirements compared to the benchmarks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/PIMRC.2019.8904290 | PIMRC |
Keywords | Field | DocType |
Green communication, heterogeneous networks, reinforcement learning | Computer science,Computer network,Dynamic energy,Heterogeneous network,Distributed computing,Reinforcement learning | Conference |
ISSN | Citations | PageRank |
2166-9570 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Sun | 1 | 0 | 0.34 |
| Tiejun Lv | 2 | 669 | 97.19 |
| Xuewei Zhang | 3 | 70 | 12.33 |
| Ziyu Liu | 4 | 0 | 0.34 |