Enhanced Network Performance in Cognitive Radio Networks using Reinforcement Learning

Author(s):

B. Bhuvaneswari , Kongu Engineering College; Dr. T. Meera Devi, Kongu Engineering College

Keywords:

Cluster merging, Cluster splitting, Reinforcement Learning, State, Action, Reward

Abstract:

In wireless communication system there are several routing problems. To overcome this problem we use the future generation wireless communication system known as Cognitive Radio Network (CRN). It allows the less priority user to use the unused or underutilized spectrum of priority user. However, dynamic conditions of CRN (Priority user activity and channel availability) more routing more challenging. This challenge is overcome with the help of clustering mechanism. Cluster-Based Routing in CRN enhances network scalability and stability. Additionally, an artificial intelligence approach Reinforcement Learning (RL) is used to maximize the network performance. We present a model SMART, a cluster-based routing scheme and evaluate the performance using stimulation in order to show the effectiveness of Cluster-Based routing in CRNs using RL.

Other Details:

Manuscript Id	:	IJSTEV4I10053
Published in	:	Volume : 4, Issue : 10
Publication Date	:	01/05/2018
Page(s)	:	136-140

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