Sunday, 24 June 2018

CFP: IEEE TETCI Special Issue on Computational Intelligence for Cellular/Wireless Communications and Sensing (Oct 1)


I. AIM AND SCOPE


  As billions of phones, appliances, drones, traffic lights, security systems, environmental sensors, radars, and other radio-connected sensing and communication devices sum into a rapidly growing Internet of Things (IoT), many challenges such as spectrum allocation and efficiency, energy efficiency, security, have emerged as urgent topics to be solved. For example, 5G wireless communications will be deployed in the 28GHz, 37GHz, 39GHz frequency band, which may co-exist with radars and other sensing devices. Quite often, researchers often handle these challenges using traditional approaches such as game theory, convex optimization, etc. Computational intelligences techniques such as fuzzy systems, evolutionary computing, neural networks and learning systems are capable of handling resources allocation, decision making, where uncertainties abound, so it is very natural to apply computational intelligence to the above challenges in cellular/wireless communications and sensing.
  There are four important differences that make the emerging topics in Computational Intelligence for Cellular/Wireless Communications and Sensing (CICCS) unique.
  1. Compared to traditional communication and sensing problems, the RF data rate is much higher in the emerging area of communication and sensing which means real-time decision such as resource allocation or signal detection should be made much faster based on computational intelligence.
  2. The operating frequencies are much higher and users are heterogeneous.
  3. RF waveforms are typically captured and represented as complex numbers, underscoring the importance of both amplitude and phase of the signal. Although there has been interest recently in complex-valued neural networks, the technology for learning naturally in the complex plane is not fully developed and relies on treating complex variables as two real numbers.
  4. The integration of communication and sensing is highly desirable because the communication and sensing modules are often co-located such as in smart phones, and they may be operated in the same frequency band.

II. TOPICS

  Topics of interest for this special issue include, but are not limited to:

  • New computational intelligence models for communications and sensing
  • Computational intelligence for 5G Communications Wireless
  • Computational intelligence for IoT
  • Computational intelligence for sensor networks
  • Computational intelligence for remote sensing
  • Computational intelligence for spectrum efficiency
  • Computational intelligence for energy efficiency
  • Computational intelligence for radars
  • Computation intelligence for radar and communications co-existence
  • Computational intelligence for integration of communications and sensing


III. SUBMISSIONS


  Manuscripts should be prepared according to the “Information for Authors” section of the journal(http://cis.ieee.org/ieee-transactions-on-emerging-topics-in-computational-intelligence.html) and submissions should be done through the journal submission website: https://mc.manuscriptcentral.com/tetci-ieee, by selecting the Manuscript Type of Computational Intelligence for Cellular/Wireless Communications and Sensing (SI:CICCS)” and clearly marking “Computational Intelligence for Cellular/Wireless Communications and Sensing (SI: CICCS) Special Issue Paper” as comments tothe Editor-in-Chief. Submitted papers will be reviewed by at least three different expert reviewers. Submission of amanuscript implies that it is the authors’ originalunpublished work and is not being submitted for possible publication elsewhere.

IV. IMPORTANT DATES

Paper submission deadline: October 1, 2018
Final notice of acceptance/reject: February 1, 2019

V. GUEST EDITORS

Qilian Liang, University of Texas at Arlington, USA;
liang@uta.edu
Gary Yen, Oklahoma State University, USA;
gyen@okstate.edu
Tariq S. Durrani, University of Strathclyde, UK;
durrani@strath.ac.uk
Wei Wang, Tianjin Normal University, China;
weiwang@tjnu.edu.cn
Xin Wang, Qualcomm Inc, USA;
xinwng@qca.qualcomm.com

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