International Journal of Innovative Engineering, Technology & Science

Optimization of Resource Allocation in Cognitive Radio Network Using Artificial Intelligence

Optimization of Resource Allocation in Cognitive Radio Network Using Artificial Intelligence

Publication Date : 12/10/2020

Author(s) :

Odo, K. C, Nwabueze, C. A. , Akaneme, S. A. .

Volume/Issue :
Volume 3
Issue 2
(10 - 2020)

Abstract :

Cognitive radio network (CRN), which has been adopted as a promising solution for optimization of the limited available radio-frequency spectrum, has two major drawbacks: Missed Detection (MD) and False Alarm (FA). This work proposed fuzzy-based intelligent resource allocation in cognitive radio network (FIRA-CRN) as a solution to the identify drawbacks. In the methodology, the available channels are classified based on the primary users’ (PUs) utilization, the number of cognitive radio neighbours using the channels and the capacity of available channels. The Fuzzy Logic technique is used to determine a channel’s weight value by combining these parameters. The channels with the highest weight value are selected for transmission. The proposed strategy takes into account false alarm (FA) and miss detection (MD) metrics to classify the sensed channels into four categories (FA, MD, ON and OFF) based on K-means learner. This classification helps the strategy to avoid accessing occupied channels. Average interference ratio (AIR), end-to-end delay (EED) and packet delivery ratio (PDR) were used as key performance indicators to evaluate the proposed scheme while comparing it with other schemes visa-viz: best-fit channel selection (BFC), GA-based selection (GA), Intelligent Channel Selection Scheme a Self-Organized Map Followed by Simple Segregation (ICSSSS), and longest idle time channel selection (LITC). Results showed that FIRA-CRN reduced the AIR by 60%, 40%, 32%, and 7% when compared with LITC, GA, BFC and ICSSSS respectively. With respect to PDR, it is also observed that FIRA-CRN outperformed ILTC, BFC, GA, and ICSSSS by 45%, 28.3%, 14.8%, and 7.5% respectively. Besides, FIRA-CRN reduced EED by 88.7%, 84.4%, 77.8%, and 28.3% for LITC, BFC, GA, and ICSSSS respectively. This work can be used to improve the overall performance of cognitive radio networks.

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