NDLI: Small cell switch policy: A reinforcement learning approach

Content Provider	IEEE Xplore Digital Library
Author	Luyang Wang Xinxin Feng Xiaoying Gan Jing Liu Hui Yu
Copyright Year	2014
Description	Author affiliation: Dept. of Electron. Eng., Shanghai Jiao Tong Univ., Shanghai, China (Luyang Wang; Xinxin Feng; Xiaoying Gan; Jing Liu; Hui Yu)
Abstract	Small cell is a flexible solution to satisfy the continuously increasing wireless traffic demand. In this paper, we focus on on-off switch operation on small cell base stations (SBS) in heterogeneous networks. In our scenario, the users can either choose SBS when it is active or macro cell base station (MBS) to transmit data. Start-up energy cost is considered when SBS switches on. The whole network acts as a queueing system, and network latency is also under consideration. The network traffic is modeled by a Markov Modulated Poisson Process (MMPP) whose parameters are unknown to the network control center. To maximize the system reward, we introduce a reinforcement learning approach to obtain the optimal on-off switch policy. The learning procedure is defined as a Markov Decision Process (MDP). An estimation method is proposed to measure the load of the network. A single-agent Q-learning algorithm is proposed afterwards. The convergence of this algorithm is proved. Simulation results are given to evaluate the performance of the proposed algorithm.
Starting Page	1
Ending Page	6
File Size	272141
Page Count	6
File Format	PDF
ISBN	9781479973392
DOI	10.1109/WCSP.2014.6992126
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Energy consumption Microprocessors Scattering Switches Computer architecture Markov processes Convergence
Content Type	Text
Resource Type	Article

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