Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

A Review of Spectrum Sensing Techniques Based on Machine Learning

A Review of Spectrum Sensing Techniques Based on Machine Learning
View Sample PDF
Author(s): Andres Rojas (Instituto Nacional de Astrofísica, Óptica y Electrónica, Mexico)and Gordana Jovanovic Dolecek (Instituto Nacional de Astrofísica, Óptica y Electrónica, Mexico)
Copyright: 2025
Pages: 21
Source title: Encyclopedia of Information Science and Technology, Sixth Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Founding Editor-in-Chief, Information Resources Management Journal (IRMJ), USA)
DOI: 10.4018/978-1-6684-7366-5.ch050


View A Review of Spectrum Sensing Techniques Based on Machine Learning on the publisher's website for pricing and purchasing information.


This article presents a survey of current spectrum sensing (SS) research involving the application of image processing and deep learning techniques. This document includes approaches to narrowband, wideband, and cooperative SS. A current trend, automatic classification modulation (AMC), is also included in this review. It is closely related to SS by recognizing the spectrum availability and classifying the signal type currently using the licensed band of interest. This chapter is helpful for comparison of the current tendencies in spectrum sensing in terms of signal simulation, including different analog and digital modulation types, image-based approaches such as covariance matrix or spectrogram, and wireless channel simulations. The extensive review included in this document mainly focuses on deep learning architectures and image processing techniques that can help improve CR systems' detection probability to maximize the underutilized RF spectrum in 5G.

Related Content

Christian Rainero, Giuseppe Modarelli. © 2025. 26 pages.
Beatriz Maria Simões Ramos da Silva, Vicente Aguilar Nepomuceno de Oliveira, Jorge Magalhães. © 2025. 21 pages.
Ann Armstrong, Albert J. Gale. © 2025. 19 pages.
Zhi Quan, Yueyi Zhang. © 2025. 21 pages.
Sanaz Adibian. © 2025. 19 pages.
Le Ngoc Quang, Kulthida Tuamsuk. © 2025. 21 pages.
Jorge Lima de Magalhães, Carla Cristina de Freitas da Silveira, Tatiana Aragão Figueiredo, Felipe Gilio Guzzo. © 2025. 17 pages.
Body Bottom