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Publications

Publication Date

Manuscript Submission Deadline

Special Issue

Call for Papers

Background and Motivation: The future of wireless communication is transcending toward networks where the radio environment becomes controllable, programmable, and intelligent by leveraging emerging technologies such as Intelligent Reflecting Surface and Cognitive Radio. These networks possess perception, learning, reasoning, and decision-making capabilities where different parts of the network might be configured and controlled via user-centric AI as well as through intelligent and software-defined network paradigm. Reconfigurable wireless networks aim at enabling “intelligence” into the existing system to perceive and assess the available resources, to autonomously learn to adapt to the dynamism in the wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. Therefore, with the help of reconfigurable wireless networks, where nodes are capable of changing their frequencies, the problem of huge spectrum scarcity can also be addressed .

 Energy Efficiency is a critical issue in reconfigurable wireless communications networks, not only due to the technological limitations on energy supplies, but also due to the environmental impact caused by the information and communication technologies. Besides the environmental concerns about energy consumption in wireless networks, and the possible reduction in operational expenditures, the need to improve energy-efficiency becomes apparent, as the number of power consuming functionalities required from these devices increases. The existing wireless communication architectures and technologies may not be able to address these issues, as reconfigurable wireless networks have various distinct characteristics that require novel methods and tools to optimize and to efficiently operate them . Therefore, new approaches are required, using which spectrum and energy resources can be efficiently collected and intelligently managed.

Technical Scope of the Proposal: This Special Issue addresses this need and primarily covers the following:

  1. Novel energy-efficient methods for reconfigurable wireless communication and networks.
  2. Energy efficiency improvement, including energy saving reconfigurable hardware and devices. 
  3. Energy-efficient communication techniques for reconfigurable wireless networks/ intelligent reflecting surfaces.
  4. Design of energy-aware reconfigurable network architectures/ intelligent reflecting surface aided wireless network and protocols.
  5. Energy-friendly software and applications supporting sustainability and  use of renewable energy sources.
  6. Machine learning algorithms for spectrum-and energy-efficient communications in reconfigurable wireless environments.
  7. Smart and reconfigurable environment using intelligent reflecting surface aided wireless network.

Submission Guidelines

Solicited and invited papers shall undergo the standard IEEE peer review process. After initial screening, paper will immediately go to the review process. Submission to this Special Issue should be made only on the IEEE TGCN’s online manuscript submission portal and in the submission process authors are instructed to select the manuscript type as "Special Issue: Energy Efficient Reconfigurable Wireless Communication &  Networks". All the submitted manuscripts should be based on the layout and formatting guidelines.

Important Dates

Manuscript Submission: 31 May 2021 (Extended Deadline)
Notification of First Review: 30 June 2021
Revision Submission, if any: 31 July 2021
Final Acceptance: 31 August 2021
Publication: December 2021

Guest Editors

Sudip Misra (Lead)
Indian Institute of Technology Kharagpur, India

Yue Gao
University of Surrey, UK

Nitin Gupta
National Institute of Technology, Hamirpur, India

Falko Dressler
School of Electrical Engineering and Computer Science, TU Berlin, Germany

Vincenzo Piuri
Dipartimento di Informatica, Universita' degli Studi di Milano, Italy

Guoliang (Larry) Xue
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, USA