Name
Title | ||
|---|---|---|
Reinforcement Learning For Delay-Constrained Energy-Aware Small Cells With Multi-Sleeping Control |
Abstract | ||
|---|---|---|
In 5G networks, specific requirements are defined on the periodicity of Synchronization Signaling (SS) bursts. This imposes a constraint on the maximum period a Base Station (BS) can be deactivated. On the other hand, BS densification is expected in 5G architecture. This will cause a drastic increase in the network energy consumption followed by a complex interference management. In this paper, we study the Energy-Delay-Tradeoff (EDT) problem in a Heterogeneous Network (HetNet) where small cells can switch to different sleep mode levels to save energy while maintaining a good Quality of Service (QoS). We propose a distributed Q-learning algorithm controller for small cells that adapts the cell activity while taking into account the co-channel interference between the cells. Our numerical results show that multi-level sleep scheme outperforms binary sleep scheme with an energy saving up to 80% in the case when the users are delay tolerant, and while respecting the periodicity of the SS bursts in 5G. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICCWorkshops49005.2020.9145431 | 2020 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS) |
Keywords | DocType | ISSN |
Energy saving, delay, multi-level sleep mode, 5G, Q-learning | Conference | 2164-7038 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali El-Amine | 1 | 1 | 3.41 |
| Paolo Dini | 2 | 0 | 0.68 |
| Loutfi Nuaymi | 3 | 226 | 29.39 |