Novel Learning-Based Approaches for Cognitive Radio Networks

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (15 December 2017) | Viewed by 6305

Special Issue Editors

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Guest Editor
Department of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, Republic of Korea
Interests: edge computing; network resource management; service management; mobile security
Special Issues, Collections and Topics in MDPI journals
Network Science, Wireless, and Security (NetSciWiS@VT) Laboratory, Wireless@VT, Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
Interests: static and dynamic game theory; network science; learning; self-organizing networks; behavioral game theory; prospect theory; bounded rationality; applications of game theory in wireless and cyber-physical systems

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Guest Editor
University of Houston, Houston, TX 77004, USA

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Guest Editor
Centre for Wireless Communication (CWC), University of Oulu, Oulu, Finland

Special Issue Information

Dear Colleagues,

Significant design efforts are made to enable new applications and handle the explosion of intelligent mobile devices. The increased resource requirements to enable this expansion will cause significant engineering complexity, scarcity of the licensed spectrum and increase costs. These challenges require a transformation to a new network paradigm with flexible management of next generation of wireless networks. To this end, Cognitive Radio (CR) has emerged to play an important role in next generation wireless networks. CR techniques enable us to utilize a limited spectrum and thus to relieve the current spectrum scarcity. In addition, some of the key emerging technologies can be used with CR, i.e., wireless virtualization, non-orthogonal multiple access (NOMA), LTE Carrier Aggregation (CA) and so on. Fusion of these technologies with CR can potentially improve the network capacity to support the requirements of next generation wireless networks. However, the challenges and obstacles in efficient deployment of CRs for resolving large-scale distributed computing problems by actively learning, adapting, and steering user behavior in future ubiquitous networks have yet to be addressed.

Recent developments in the field of learning-based approaches offer powerful tools to handle large-scale distributed computing problems. We believe that a cognitive formalism, such as artificial intelligence, deep learning, online-learning based control, and Gibbs sampling architectures, will result in a new leap forward from the current perception of information processing and management. This special issue aims to promote the dissemination of high-quality research in the design, implementation, techniques, and tools required to active learning-based approaches for gathering recent contributions and advances in CR networks. These can resolve large-scale distributed computing problems for future ubiquitous mobile systems.

Articles should be written in a style comprehensible to readers outside the topic of the article.


Topics of interest include, but are not limited to, novel learning-based approaches to:

•      Network selection issues based on learning approaches in CRNs.
•      Learning-based algorithms for adaption and resource management in CRNs.
•      Spectrum sensing.
•      Spectrum mobility.
•      Cognitive radio management in cloud computing.
•      Aggregate interference and coexistence issues.
•      Cognitive medium access control and routing protocols.
•      Resource allocation management for cognitive radio networks.
•      Cross-layer design and optimization of CRNs.
•      Self-configuration, self-organization, self-optimization, and interoperability issues.
•      Practical testbeds and standardization for flexible cognitive radio networks.
•      Cognitive Radio in NOMA, LTE-Carrier Aggregation, V2X, and D2D networks.
•      Cognitive Radio in Wireless Network Virtualization.
•      Energy efficiency in flexible radio networks.

Prof. Choong Seon Hong
Prof. Walid Saad
Prof. Zhu Han
Prof. Mehdi Bennis
Guest Editors

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  • - Cognitive radio networks
  • - Cloud-assisted CRNs
  • - Virtualization-based CRNs
  • - Resource management
  • - 5G
  • - Learning-aided scheduling
  • - Deep learning
  • - Game-theoretic learning models
  • - Learning-based optimization theory
  • - Machine learning

Published Papers (1 paper)

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On the Interdependence of the Financial Market and Open Access Spectrum Market in the 5G Network
by Juraj Gazda, Peter Tóth, Jana Zausinová, Marcel Vološin and Vladimír Gazda
Symmetry 2018, 10(1), 12; - 31 Dec 2017
Cited by 4 | Viewed by 5922
Modern 5G networks offer a large space for innovation and a completely new approach to addressing network functioning. A fixed spectrum assignment policy is a significant limitation of today’s wireless communication network practice and is to be replaced by a completely new approach [...] Read more.
Modern 5G networks offer a large space for innovation and a completely new approach to addressing network functioning. A fixed spectrum assignment policy is a significant limitation of today’s wireless communication network practice and is to be replaced by a completely new approach called dynamic spectrum access (DSA). However, there is no general agreement on the organization of the DSA. Some studies suggest that open access market can be inspired by the electricity or financial markets. It allows to treat operators with region coverage as investors entering the market and trading the spectra on an on-demand basis. Because investors operate in both the financial markets and the markets for spectra, new interference between both markets emerges. Our paper shows how the risk-free rate of return stemming from the financial markets influences the techno-economic properties of the network. We show that, for low risk-free returns, the spectrum market becomes oversupplied, which keeps service prices very low and spectrum trading volumes large. In contrast, if risk-free returns are high, then spectrum trading volumes decline and the market becomes price sensitive; in other words, economic rules begin to work better. Full article
(This article belongs to the Special Issue Novel Learning-Based Approaches for Cognitive Radio Networks)
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