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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