Cloud and Big Data-based Next-generation Cognitive Radio Networks

Submission Deadline: 31 January 2018

IEEE Access invites manuscript submissions in the area of Recent Advances in Cloud and Big Data-based Next-generation Cognitive Radio Networks.

In cognitive radio networks (CRN), secondary users (SUs) are required to detect the presence of the licensed users, known as primary users (PUs), and to find spectrum holes for opportunistic spectrum access without causing harmful interference to PUs. However, due to the complicated data processing, non-real-time information exchange and limited memory, SUs often suffer from the imperfect sensing and unreliable spectrum access. Cloud computing can solve this problem by allowing the data to be stored and processed in a shared environment. Furthermore, the information from a massive number of SUs allows for more comprehensive information exchanges to assist the resource allocation and interference management at the cloud center while relieving the stringent capacity demands in fronthaul links. Moreover, spectrum resources should be made available to more users, especially when the spectrum is underutilized but occupies a large band. Hence, cloud-based CRN can generate massive sensing samples that will benefit the applications of big data algorithms. The approaches to spectrum sensing and spectrum management can be greatly improved with decision making capabilities of spectral big data.

To integrate cloud and big data in CRN and to support high quality transmission, many challenges need to be addressed, such as cloud-based CRN, cloud-based spectrum management, big data-based spectrum sensing. This Special Section in IEEE Access will bring together leading researchers and developers from both industry and academia to discuss and present their views on all aspects of cloud and big data-based CRN.

The topics of interest include, but are not limited to:

• Dynamic spectrum sharing and access in cloud-based CRN
• Challenges and issues in designing cloud-based CRN
• Cooperative and coordinated communications in cloud-based CRN
• Modeling and performance evaluation for cloud-based CRN
• Waveform design, modulation, and interference aggregation for spectrum shared-based cloud-based CRN
• Architectures and building blocks of cloud-based CRN
• Physical-layer security in spectrum shared-based cloud-based CRN
• Spectral big data analyzing and mining
• Big data-based spectrum sensing
• Big data communication for CRN

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Associate Editor: Nan Zhao, Dalian University of Technology, China

Guest Editors:

1. Xin Liu, Dalian University of Technology, China
2. F. Richard Yu, Carleton University, Canada
3. Yunfei Chen, University of Warwick, UK
4. Tao Han, University of North Carolina at Charlotte, US
5. Zheng Chang, University of Jyvaskyla, Finland

Relevant IEEE Access Special Sections:

1. Convergence of Sensor Networks, Cloud Computing, and Big Data in Industrial Internet of Things
2. Emerging Trends, Issues, and Challenges in Energy-Efficient Cloud Computing
3. Advanced Data Analytics for Large-scale Complex Data Environments

IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: zhaonan@dlut.edu.cn