Call for Papers: Signal Processing Techniques for Sustainable Cognitive Radio Communications
The growing devices and capacity requirements of wireless systems bring increasing demand for RF spectrum. Cognitive radio (CR) system is an emerging concept to increase the spectrum efficiency. CR system aims to enable opportunistic usage of the RF bands that are not occupied by their primary licensed users in spectrum overlay approach. This approach is especially important in signal and image processing, where sets of sensors, usually large and heterogeneous, provide large amounts of data, usually noisy and corrupted with various sources of interference. From a methodological point of view, cognitive communication is concerned with multi-dimensional and statistical signal processing, especially with problems such as detection, estimation, and optimization. In addition to classical sensing, detection, supervised, reinforcement and learning methods include Bayesian modeling, Markov models, support vector machines, and kernel methods. It spans a broad area of applications, such as military, industrial, medical, transportation and other fields like error control, error detection, adaptive filtering, computer vision, managing data, sensor control, data fusion, blind and semi-blind source separation, sparse analysis, brain-computer interfaces, signal processing and radio communication. The intelligent system for the cognitive communication is a collection of intelligent terminals with signal processing and mobile capabilities. Among them, each intelligent terminal carries out signal transmission and distributed processing with other intelligent terminals. However, most existing communication architectures, including their signal processing protocols and control algorithms, are designed for centralized networks by default.
This special issue aims to gather latest research and development achievements in this area and to promote their applications in all important fields with society needs.
Topics of interest include, but are not limited to, the following scope:
- Cognitive Communication for channel and signal categorization
- Optimization and learning of spectrum usage dynamics and spectrum access control
- Privacy-preserving machine learning for cognitive radio
- Deep cognitive learning for RF signal classification
- Signal processing in multiuser and dynamic spectrum access
- Spectrum handling and energy efficiency within sensor network systems
- Reconfigurable signal processing architectures for cognitive radio
- Cognitive technologies in 5G cellular networks and channel decoding
- RF-based geo location and signal association
- Distributed multi-agent learning in collaborative radio networks
- Cognitive radars for spectrum sharing with communication devices
- Reinforcement learning in wireless networks
- Energy and cyclostationary detection techniques
- Wavelet-based detection techniques in distributed spectrum sensing
Submission deadline: October 10, 2019
First notification: December 10, 2019
Submission of revised manuscript: January 15, 2020
Notification of the re-review: February 10, 2020
Final notification: March 10, 2020
Publication: April 2020
- Dr. Arulmurugan Ramu, Department of Computer Science and Engineering, Presidency University, Bangalore, Karnataka, India
- Dr. Mu-Yen Chen, Department of Information Management, National Taichung University of Science and Technology, Taiwan
- Dr. Sri Devi Ravana, Department of Information System, Faculty of Computer Science & Information Technology, University of Malaya, Malaysia
Authors should follow the WINET Journal manuscript format described at the journal site. Manuscripts should be submitted online through http://www.editorialmanager.com/wine/.
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