Transfer restless multi-armed bandit policy for energy-efficient heterogeneous cellular network

Transfer restless multi-armed bandit policy for energy-efficient heterogeneous cellular network

Abstract This paper proposes a learning policy to improve the energy efficiency (EE) of heterogeneous cellular networks. The combination of active and inactive base stations (BS) that allows for maximizing EE is identified as a combinatorial learning problem and requires high computational complexit...

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Bibliographic Details
Main Authors: Navikkumar Modi, Philippe Mary, Christophe Moy
Format: Article
Language:English
Published: SpringerOpen 2019-10-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
Energy efficiency
Green cellular networks
Upper confidence bound
Reinforcement learning
Transfer learning
Multi-armed bandit
Online Access:http://link.springer.com/article/10.1186/s13634-019-0637-1

Internet

http://link.springer.com/article/10.1186/s13634-019-0637-1

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