[hpc-announce] IEEE Transactions on Network Science and Engineering Call for Papers Special Issue on Artificial Intelligence / Machine Learning Enabled Reconfigurable Wireless Networks

[Uttam Ghosh]

IEEE Transactions on Network Science and Engineering
Call for Papers
Special Issue on Artificial Intelligence / Machine Learning Enabled
Reconfigurable Wireless Networks
https://www.comsoc.org/publications/journals/ieee-tnse/cfp/artificial-intelligence-machine-learning-enabled-reconfigurable
Aim and Scope

Reconfigurable Wireless Networks (RWNs) are composed of a set of
communicating nodes such that each one executes reconfigurable software
tasks to control local networking nodes. RWNs can include reconfiguration
of software, hardware and protocols. Software reconfiguration allows the
inclusion, exclusion or updation of tasks, hardware reconfiguration allows
the activation and deactivation of nodes, and protocol reconfiguration
allows the modification of routing protocols between nodes. In recent
years, we have witnessed rapid proliferation of the following: (1)
development of fully programmable, protocol-independent data planes and
languages for programming and (2) the emergence of new platforms, tools,
and algorithms for Artificial Intelligence (AI) and Machine Learning (ML).
Wireless networks possess perception, learning, reasoning, and
decision-making capabilities, which make AI/ML an indispensable tool to
optimize and to efficiently operate it. 5G wireless networks are rapidly
evolving towards an intelligent and software-defined design paradigm, where
different parts of the network might be configured and controlled via
user-centric AI. The future of wireless networks, giving rise to
intelligent processing, which aims at enabling the system to perceive and
assess the available resources, to autonomously learn to adapt to the
perceived wireless environment, and to reconfigure its operating mode to
maximize the utility of the available resources. Perception capability and
reconfigurability are essential features of cognitive technology while
modern ML techniques project effectiveness in system adaptation. Further,
due to massive data explosion currently there is a problem of huge spectrum
scarcity, which can be solved with the help of reconfigurable wireless
networks where nodes are capable of changing their frequencies. Therefore,
there is a requirement of AI and ML assisted algorithms for spectrum and
energy efficient communication, wireless security, MAC issues etc. The
Special issue will give emphasis on novel techniques for building
reconfigurable wireless networks and coupling the technological advances in
wireless networking with scientific innovations in AI and ML.

This SI seeks contributions from experts in areas such as network
programming, formal methods, control theory, distributed systems, machine
learning, data science, data structures and algorithms, and optimization in
the view of reconfigurable wireless networks as well as improving the
performance of AI and ML solutions.

This special issue seeks original contributions in, but not limited to,

   - Distributed machine learning techniques for reconfigurable wireless
   networks
   - Deep reinforcement learning techniques for reconfigurable wireless
   networks
   - AI/ML based testbeds and experimental evaluations for reconfigurable
   wireless networks
   - AI/ML for wireless network orchestration • AI/ML for resource
   allocation in reconfigurable wireless networks
   - AI/ML for traffic engineering, scheduling, network slicing and
   virtualization for reconfigurable wireless networks • AI/ML based network
   monitoring for reconfigurable wireless networks
   - Case studies demonstrating (dis)advantages of choosing AI/ML
   techniques for reconfigurable wireless networks over more traditional ones
   - AI/ML assisted routing protocols for reconfigurable wireless networks.
   - AI/ML assisted medium access control schemes for reconfigurable
   wireless networks.
   - AI/ML assisted reconfigurable wireless network for smart applications
   e.g., biomedical, Healthcare, Optical, etc,.
   - AI/ML for Dynamic Spectrum Allocation in Reconfigurable Wireless
   Networks.
   - Architecture, protocols, cross-layer design for reconfigurable
   wireless networks.

*Important Dates: *

   - Manuscripts due:  October 01 2020
   - Peer reviews to authors: January 01, 2021
   - Revised manuscripts due: March 01, 2021
   - Second-round reviews to authors: May 05, /2021
   - Final accepted manuscript due:June 01, 2021

*Guest Editors*

   - Danda B Rawat, Howard University, USA.
   - Uttam Ghosh, Vanderbilt University, USA.
   - Mohamad Assaad, CentraleSupelec, France.
   - Raja Datta, Indian Institute of Technology Kharagpur, India.
   - San Murugesan, Western Sydney University, Australia.
   - Xenofon Koutsoukos, Vanderbilt University, USA.

*Paper submission:*

Prospective authors are invited to submit their manuscripts electronically,
adhering to the IEEE Transactions on Network Science and Engineering
guidelines (
https://www.comsoc.org/publications/journals/ieee-tnse/ieee-transactions-network-science-and-engineering-information).
Note that the page limit is the same as that of regular papers. Please
submit your papers through the online system (
https://mc.manuscriptcentral.com/tnse-cs) and be sure to select the special
issue or special section name. Manuscripts should not be published or
currently submitted for publication elsewhere. Please submit only full
papers intended for review, not abstracts, to the ScholarOne portal. If
requested, abstracts should be sent by e-mail to the Guest Editors directly.


Kind Regards

Uttam Ghosh

Vanderbilt University
---
Uttam Ghosh
Assistant Professor of the Practice
Department of Electrical Engineering and Computer Science
School of Engineering
Vanderbilt University
Mob: +1-615-686-6352
Email: uttam.ghosh at vanderbilt.edu, ghosh.uttam at ieee.org
<ughosh at tnstate.edu>