Advances in Cognitive Radio and Cognitive Radio Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (15 September 2025) | Viewed by 610

Special Issue Editors


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Guest Editor
James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Interests: 5G and beyond wireless communication technologies; integration of AI and machine learning in next-generation networks; development of intelligent and adaptive communication systems

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Guest Editor
School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK
Interests: wireless networks; mobile internet of things; machine learning; signal processing

Special Issue Information

Dear Colleagues,

The rapid expansion of wireless communication networks has ushered in an era of unprecedented demand for spectrum efficiency, adaptability, and scalability. Cognitive radio (CR) and cognitive radio networks (CRNs) have emerged as transformative solutions to these challenges, leveraging dynamic spectrum access and intelligent decision-making to optimize network performance. This Special Issue aims to bring together cutting-edge research and innovative solutions in CR and CRN technologies to address the growing complexities of next-generation wireless networks.

We invite the submission of original research articles and comprehensive reviews that explore advancements in cognitive radio technologies, including, but not limited to, spectrum sensing, dynamic spectrum sharing, interference management, and security. Contributions focusing on the integration of machine learning and artificial intelligence in CRNs, the development of energy-efficient protocols, and the deployment of CRNs in real-world applications are highly encouraged. Studies that tackle the challenges of 5G and beyond, such as spectrum scarcity, low-latency communication, and the role of CRNs in the Internet of Things (IoT), are also welcome to be submitted.

This Special Issue aims to provide a platform for researchers and industry experts to share their insights and advancements, promoting collaboration and innovation in the field. The purpose of this Special Issue is to report on the latest achievements and progress in the theories, methodologies, and applications of cognitive radio and cognitive radio networks, including, but not limited to, the following:

  • Spectrum sensing and dynamic spectrum access techniques.
  • Interference management and mitigation strategies.
  • Security challenges and solutions in cognitive radio networks.
  • Integration of AI and machine learning for intelligent decision-making in CRNs.
  • Energy-efficient protocols and green communication solutions.
  • Deployment of CRNs for 5G and beyond wireless systems.
  • Cognitive radio applications in IoT and smart environments.
  • Challenges and innovations in hardware implementation for CRNs.

We aim to provide a platform for researchers and practitioners to share innovative solutions and foster collaboration in addressing the challenges of next-generation wireless communication networks.

Dr. Sajjad Hussain
Dr. Sami Haider
Guest Editors

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Keywords

  • cognitive radio
  • cognitive radio networks
  • spectrum sensing
  • dynamic spectrum access
  • interference management
  • security in CRNs
  • AI in cognitive networks
  • energy-efficient CR protocols
  • IoT and CRNs
  • 5G and beyond

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Published Papers (1 paper)

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Research

20 pages, 3102 KB  
Article
Compressive Sensing-Based 3D Spectrum Extrapolation for IoT Coverage in Obstructed Urban Areas
by Kun Yin, Shengliang Fang and Feihuang Chu
Electronics 2025, 14(21), 4177; https://doi.org/10.3390/electronics14214177 - 26 Oct 2025
Viewed by 315
Abstract
As a fundamental information carrier in Industrial Internet of Things (IIoT), electromagnetic spectrum data presents critical challenges for efficient spectrum sensing and situational awareness in smart industrial cognitive radio systems. Addressing sparse sampling limitations caused by energy-constrained transceiver nodes in Unmanned Aerial Vehicle [...] Read more.
As a fundamental information carrier in Industrial Internet of Things (IIoT), electromagnetic spectrum data presents critical challenges for efficient spectrum sensing and situational awareness in smart industrial cognitive radio systems. Addressing sparse sampling limitations caused by energy-constrained transceiver nodes in Unmanned Aerial Vehicle (UAV) spectrum monitoring, this paper proposes a compressive sensing-based 3D spectrum tensor completion framework for extrapolative reconstruction in obstructed areas (e.g., building occlusions). First, a Sparse Coding Neural Gas (SCNG) algorithm constructs an overcomplete dictionary adaptive to wide-range spectral fluctuations. Subsequently, a Bag of Pursuits-optimized Orthogonal Matching Pursuit (BoP-OOMP) framework enables adaptive key-point sampling through multi-path tree search and temporary orthogonal matrix dimensionality reduction. Finally, a Neural Gas competitive learning strategy leverages intermediate BoP solutions for gradient-weighted dictionary updates, eliminating computational redundancy. Benchmark results demonstrate 43.2% reconstruction error reduction at sampling ratios r ≤ 20% across full-space measurements, while achieving decoupling of highly correlated overlapping subspaces—validating superior estimation accuracy and computational efficiency. Full article
(This article belongs to the Special Issue Advances in Cognitive Radio and Cognitive Radio Networks)
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