2015 IEEE International Conference on Communication Workshop (ICCW) 2015
DOI: 10.1109/iccw.2015.7247475
|View full text |Cite
|
Sign up to set email alerts
|

Abstract: Abstract-We consider a two-tier urban Heterogeneous Network where small cells powered with renewable energy are deployed in order to provide capacity extension and to offload macro base stations. We use reinforcement learning techniques to concoct an algorithm that autonomously learns energy inflow and traffic demand patterns. This algorithm is based on a decentralized multi-agent Q-learning technique that, by interacting with the environment, obtains optimal policies aimed at improving the system performance … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
32
0

Year Published

2015
2015
2019
2019

Publication Types

Select...
8

Relationship

6
2

Authors

Journals

citations
Cited by 38 publications
(32 citation statements)
references
References 10 publications
0
32
0
Order By: Relevance
“…In fact, RL operates by applying the experience that it has gained through interacting with the network [18]. RL methods have been applied in the field of wireless communications in areas such as resource management [19]- [24], energy harvesting [25], and opportunistic spectrum access [26], [27]. A comprehensive review of RL applications in wireless communications can be found in [28].…”
Section: A Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…In fact, RL operates by applying the experience that it has gained through interacting with the network [18]. RL methods have been applied in the field of wireless communications in areas such as resource management [19]- [24], energy harvesting [25], and opportunistic spectrum access [26], [27]. A comprehensive review of RL applications in wireless communications can be found in [28].…”
Section: A Related Workmentioning
confidence: 99%
“…Hence, this property is against increasing sum transmission rate of the network. The exponential and proximity reward functions have the property in (23) for the rate of the FBS, and the property in (25) for the rate of the MUE. In another words, they satisfy the following properties ∂R k ∂r 0 × (r 0 − log 2 (1 + Γ 0 )) ≤ 0,…”
Section: B Proposed Reward Functionmentioning
confidence: 99%
“…The subMDP modeling this use case can also be solved through reinforcement learning, as it is demonstrated in [37].…”
Section: Esmentioning
confidence: 99%
“…While the works [67]- [70] use particular optimization techniques mainly MINLP and DP, the authors in [71] applied reinforcement learning to find an optimal energy management strategy for BS powered by EH. A distributed Q-learning algorithm is used in such a way that each BS will learn their optimal energy management policy.…”
Section: A Optimization With Ehbssmentioning
confidence: 99%