Deep Learning Meets Cognitive Radio: Predicting Future Steps

Shenfield, Alex; Khan, Zaheer; Ahmadi, Hamed

doi:10.1109/vtc2020-spring48590.2020.9129042

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…They also proposed a new method that combines support vector machines with the firefly algorithm. The authors of [96] proposed a deep learning approach to learn channel activities and predict its availability in future time slots. The ability to predict the channel occupancy in the next time slots may increase the efficiency of selecting the more appropriate channel at the instant t (For example, choosing the channel having the highest probability of being unoccupied in the next time slots).…”

Section: Machine Learning For Spectrum Sensingmentioning

confidence: 99%

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

Kassri¹,

En‐Nouaary²,

Bah³

et al. 2021

IJACSA

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The inherent scarcity of frequency spectrum, along with the fixed spectrum allocation adopted policy, has led to a dire shortage of this indispensable resource. Furthermore, with the tremendous growth of wireless applications, this problem is intensified as the unlicensed frequency spectrum becomes overcrowded and unable to meet the requirement of emerging radio devices operating at higher data rates. Additionally, the already assigned spectrum is underutilized. That has prompted researchers to look for a way to address spectrum scarcity and enable efficient use of the available spectrum. In this context, Cognitive Radio (CR) technology has been proposed as a potential means to overcome this issue by introducing opportunistic usage to less congested portions of the licensed spectrum. In addition to outlining the fundamentals of Cognitive Radio, including Dynamic Spectrum Access (DSA) paradigms and CR functions, this paper has a three-fold objective: first, providing an overview of Software Defined Radio (SDR), in which the architecture, benefits, and ongoing challenges of SDR are presented; second, giving an extensive review of spectrum sensing, covering sensing types, narrowband and wideband sensing schemes with their pros and cons, Machine Learningbased sensing, and open issues that need to be further addressed in this field; third, exploring the use of Cognitive Radio in the Internet of Things (IoT) while highlighting the crucial contribution of CR in enabling IoT. This Review is elaborated in an informative fashion to help new researchers entering the area of Cognitive Radio Networks (CRN) to easily get involved.

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Section: Machine Learning For Spectrum Sensingmentioning

confidence: 99%