Analysis and Comparison of Different Fuzzy Inference Systems used in Decision Making for Secondary Users in Cognitive Radio Network

TL;DR

This paper compares different fuzzy inference systems, specifically Mamdani and Sugeno models, to optimize decision-making in cognitive radio networks for better spectrum utilization.

Contribution

It provides a comparative analysis of various fuzzy inference systems to enhance decision-making in cognitive radio spectrum management.

Findings

Mamdani and Sugeno systems show different performance characteristics.

The proposed fuzzy logic approach improves spectrum utilization efficiency.

The study identifies optimal membership functions for decision accuracy.

Abstract

Spectrum scarcity is one of the major challenges that the modern communication engineers are going through because of inefficient utilization of allocated frequency spectrum. The spectrum scarcity is a problem because there is not enough wavelengths/frequency to match the number of channels which are required to broadcast in a given bandwidth. Therefore, the utilization of available allocated spectrum when licensed users are not in use offers an opportunity as well as challenge, also, to increase the efficiency of spectrum utilization. Cognitive Radio offers a promising solution by reutilisation of unused allocated frequency spectrum. It helps to fulfil the demand of frequency requirement for modern communication system to accommodate more data transmission. In this optimum utilization of reuse of frequency spectrum required optimising algorithms in all parts of Cognitive Cycle. This paper focuses on designing a system based on fuzzy logic with a set of input and output parameters to obtain an optimised solution. A comparative analysis is also carried out among various types of membership functions of input and output on Mamdani Fuzzy Inference System and Sugeno Fuzzy Inference System. The proposed approach is applicable to design a better system model for a given set of rules.