Work

Gramian Angular Field and Convolutional Neural Networks for Real-Time Multiband Spectrum Sensing in Cognitive Radio Networks

Year: 2025

Type: article

Abstract: Multiband spectrum sensing in a cooperative environment is a novel solution for efficient spectrum resource management under the cognitive radio networks (CRNs) paradigm. This paper presents a distinctive framework where a central entity collects power spectral density data from multiple geographically distributed secondary users and applies the Gramian angular field (GAF) summation method to transform the time-series data into image representations. A major contribution of this work is the integration of these GAF images with a convolutional neural network (CNN), enabling precise and real-time detection of primary user activity and spectrum occupancy. The proposed approach achieves 99.6% accuracy in determining spectrum occupancy, significantly outperforming traditional sensing techniques. The main contributions of this study are (i) the introduction of GAF-based image representations for cooperative spectrum sensing in CRNs; (ii) the development of a CNN-based classification framework for enhanced spectrum occupancy detection; and (iii) the demonstration of superior detection performance in dynamic, real-time environments.

Source: Sensors

DOI: 10.3390/s25123580

Authors: Yanqueleth Molina-Tenorio, Alfonso Prieto-Guerrero, Enrique Rodriguez-Colina, Luis Alberto Vásquez-Toledo, Omar Alejandro Olvera-Guerrero

Institutions: Universidad Autónoma Metropolitana, Universidad Politécnica de Chiapas

Language: English

Topic: Cognitive Radio Networks and Spectrum Sensing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Universidad Autónoma Metropolitana