An Application of Complex Fuzzy Soft Matrices in Signal Processing

TL;DR

This paper introduces complex fuzzy soft matrices and demonstrates their application in signal processing, specifically in identifying reference signals from large datasets using Fourier transform, showing improved results over other methods.

Contribution

It presents a novel application of complex fuzzy soft matrices combined with Fourier transform for signal identification in digital receivers.

Findings

Fourier transform yields higher optimal values for reference signal identification.

Complex fuzzy soft matrices effectively model signals in the proposed framework.

The method improves accuracy in detecting reference signals.

Abstract

In this paper, we study the concept of complex fuzzy soft matrices. The application of complex fuzzy soft matrices in signals and systems via the cross product of complex fuzzy soft matrices and Fourier transform was carried out. In this application, an algorithm for the identification of a reference signal out of large interest signals detected by a digital receiver was presented. It was recorded that, the Fourier transform is better because it gave a higher optimal value and as a result, there was a better reference signal $R.$