Novel Learning-Based Approaches for Cognitive Radio Networks

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.

Scope:

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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