Universiti Teknologi Malaysia Institutional Repository

Channel decision in cognitive radio enabled sensor networks: A reinforcement learning approach

Abolarinwa, Joshua A. and Abdul Latiff, Nurul Mu Azzah and Syed Yusof, Sharifah Kamilah and Fisal, Norsheila (2015) Channel decision in cognitive radio enabled sensor networks: A reinforcement learning approach. International Journal Of Engineering And Technology, 7 (4). pp. 1394-1404. ISSN 0975-402

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Abstract

Recent advancements in the field of cognitive radio technology have paved way for cognitive radio-based wireless sensor networks. This has been tipped to be the next generation sensor. Spectrum sensing and energy efficient channel access are two important operations in this network. In this paper, we propose the use of machine learning and decision making capability of reinforcement learning to address the problem of energy efficiency associated with channel access in cognitive radio aided sensor networks. A simple learning algorithm was developed to improve network parameters such as secondary user throughput, channel availability in relation to the sensing time. Comparing the results obtained from simulations with other channel access without intelligent learning such as random channel assignment and dynamic channel assignment, the learning algorithm produced better performance in terms of throughput, energy efficiency and other quality of service requirement of the network application.

Item Type:Article
Uncontrolled Keywords:channel, decision, energy, learning, reinforcement
Subjects:A General Works
ID Code:58020
Deposited By: Haliza Zainal
Deposited On:04 Dec 2016 12:07
Last Modified:15 Feb 2017 15:01

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