Journal Article10.1109/MNET.011.2000504
From Cognitive to Intelligent Secondary Cooperative Networks for the Future Internet: Design, Advances, and Challenges
Nada Abdel Khalek,Walaa Hamouda +1 more
25
TL;DR: An overview of the various learning techniques currently used in the literature of CR networks is given, focusing on feature classification and clustering algorithms, and their application in cooperative CR networks.
read more
Abstract: Cognitive Radio (CR) technology was first introduced to solve the problem of radio spectrum under-utilization. A cognitive radio network consists of smart radio devices that have the ability to sense radio environment variables and take actions accordingly. To realize their full potential and to become fully cognitive, the CR nodes need to be equipped with learning and reasoning capabilities. Machine learning has been one of the enabling vehicles for intelligent CR networks. Inspired by the cognition cycle of a CR node, over the past years there has been an ever growing interest in using machine learning techniques to enhance the performance of CR networks. In this article, an overview of the various learning techniques currently used in the literature of CR networks is given. We focus on feature classification and clustering algorithms, and their application in cooperative CR networks. We outline the steps to establishing a learning-based cooperative secondary network, highlighting factors that impact detection performance. Additionally, current state-of-the-art learning-based applications in Cognitive Internet of Things (CIoT) are presented. Finally, the key challenges and future directions of intelligent cognitive networks are discussed.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks
Xavier Fernando,George Christian Lazaroiu +1 more
- 11 Sep 2023
TL;DR: Spectrum sensing, clustering algorithms, and energy-harvesting technology enhance cognitive-radio-based IoT networks through deep-learning-based, nonorthogonal, multiple-access techniques.
40
Physical-Layer Security on Maximal Ratio Combining for SIMO Cognitive Radio Networks over Cascaded κ-μ Fading Channels
Deemah H. Tashman,Walaa Hamouda +1 more
TL;DR: Results indicate the evident effect of the cascade level and the number of antennas at the eavesdropper over the secrecy of the SUs pair and reveal that PLS can be strengthened by increasing the number-of- antenna at the legitimate receiver.
33
Automatic Jamming Signal Classification in Cognitive UAV Radios
TL;DR: In this article , the authors proposed a novel method for joint detection and automatic classification of multiple jammers attacking with different modulation schemes, which is based on learning a representation of the radio environment encoded in a Generalized Dynamic Bayesian Network (GDBN) whilst multiple GDBN models represent various jamming signals under different modulation scheme.
19
Unsupervised Two-Stage Learning Framework for Cooperative Spectrum Sensing
Nada Abdel Khalek,Walaa Hamouda +1 more
- 01 Jun 2021
TL;DR: In this paper, an unsupervised two-stage learning framework for cooperative spectrum sensing is proposed, which combines the superior performance of the Support Vector Machine (SVM) and low cost training data of the Gaussian Mixture Model (GMM).
13
Automatic Jamming Signal Classification in Cognitive UAV Radios
TL;DR: Simulated results demonstrate that the proposed GDBN-based method outperforms both Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) in terms of classifica- tion accuracy and achieves a higher degree of explainability of its own decisions by interpreting causes and effects at hierarchical levels under the Bayesian learning and reasoning processes.
10
References
A comparison of methods for multiclass support vector machines
Hsu Chih-Wei,Chih-Jen Lin +1 more
TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.
Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications
Abdelmohsen Ali,Walaa Hamouda +1 more
TL;DR: This survey paper focuses on the enabling techniques for interweave CR networks which have received great attention from standards perspective due to its reliability to achieve the required quality-of-service.
622
A New Deep-Q-Learning-Based Transmission Scheduling Mechanism for the Cognitive Internet of Things
TL;DR: A new Q-learning-based transmission scheduling mechanism using deep learning for the CIoT is proposed to solve the problem of how to achieve the appropriate strategy to transmit packets of different buffers through multiple channels to maximize the system throughput.
316
Artificial Intelligence Inspired Transmission Scheduling in Cognitive Vehicular Communications and Networks
TL;DR: A deep ${Q}$ -learning approach is adopted for designing an optimal data transmission scheduling scheme in cognitive vehicular networks to minimize transmission costs while also fully utilizing various communication modes and resources.
164
20 Years of Evolution From Cognitive to Intelligent Communications
TL;DR: In this paper, the authors provide an overview on the intelligent communication in the past two decades to illustrate the revolution of its capability from cognition to artificial intelligence (AI), which is an efficient approach to provide more access opportunities to connect massive wireless devices.
103