IEEE Computational Intelligence Magazine - February 2018 - 28
Guest
Editorial
Junaid Qadir
Information Technology University, Lahore, Pakistan
Amir Hussain
University of Stirling, UK
Kok-Lim Alvin Yau
Sunway University, Malaysia
Muhammad Ali Imran
University of Glasgow, UK
Adam Wolisz
Technische Universität Berlin, Germany
Computational Intelligence Techniques for Mobile Network Optimization
m
odern society has become
increasingly reliant on mobile
networks for their communication needs. Such networks are characterized by their dynamic, heterogeneous,
complex, and data intensive nature,
which makes them more amenable to
automated mobile network optimization performed using "computational
intelligence'' (CI) techniques rather than
traditional optimization approaches. CI
techniques-which subsume multidisciplinary techniques from machine learning (ML), optimization theory, game
theory, control theory, and meta-heuristics-have a rich history in terms of
being deployed in networking.
CI techniques are highly suited to the
mobile networking architectures and the
dynamic environments they characterize.
Looking ahead, it looks likely that CI
will play a leading role in upcoming 5th
generation (5G) wireless mobile networks for developing optimized solutions for vexing problems-such as
traffic scheduling and routing, capacity,
coverage, and power optimization-in
the face of stringent requirements and
highly dynamic conditions.
The importance of our proposed
theme of mobile network optimization
(MNO) motivated us to propose this special issue in the IEEE Computational
Intelligence Magazine (CIM)-the premier IEEE magazine for professionals
Digital Object Identifier 10.1109/MCI.2017.2773799
Date of publication: 10 January 2018
28
interested in CI techniques and their
applications. We were successful in attracting several high-quality submissions (fourteen submissions in total). All of the
submitted papers were reviewed by at
least three competent independent referees and also by one editor. After a rigorous peer review process, we finally
selected three papers as part of our special
issue.These papers are described below.
In the first paper, "Machine Learning for
Performance Prediction in Mobile Cellular
Networks", Riihijärvi and Mähönen present a case study of how machine learning
can be used to predict performance of
mobile cellular networks at locations,
times, and for metrics for which direct
measurements are not available. The
authors propose using various ML and CI
techniques for the task of temporal, spatial,
and multidimensional prediction (including Gaussian process regression, exponential smoothing of time series, and random
forests) in mobile cellular networks. The
authors establish the viability and the
promise of their proposed techniques
using extensive real-world drive test data.
The paper "Cluster-based Content Distribution Integrating LTE and IEEE
802.11p with Fuzzy Logic and Q-learning"
by Wu et al. focuses on using CI techniques (Q-learning and fuzzy logic based
algorithms) for integrating the mobile
cellular LTE service with the IEEE
802.11p standard that works in vehicular
ad-hoc networking (VANET) environments. The authors propose a clustering
based content distribution approach that
IEEE ComputatIonal IntEllIgEnCE magazInE | FEbruary 2018
improves the performance of LTE and
IEEE 802.11p point solutions by successfully performing resource pooling of two
disparate technologies (LTE and 802.11p),
using CI techniques.
Finally, the paper "A Fast, Adaptive,
Energy-Efficient Data Collection Protocol in
Multi-Channel-Multi-Path Wireless Sensor
Networks" by Liew et al. proposes an
adaptive energy-efficient scheduling solution for multi-channel wireless sensor
networks (WSNs). The authors propose a
two-phase approach in which nodechannel assignment is performed using a
graph-coloring approach in the first
phase, and scheduling is performed in the
second phase using a three-dimensional
parallel iterative matching (3DPIM) approach.The authors demonstrate superior
simulation results compared to traditional
approaches and show their solution is
able to perform fast and energy-efficient
data collection while remaining responsive to network conditions.
We sincerely thank all of the authors
who submitted their papers to our special
issue, and to the large number of distinguished reviewers who volunteered their
time and expertise and helped us in curating a high-quality special issue on this
important and timely topic. We will also
like to thank Prof. Hisao Ishibuchi, the
Editor-in-Chief of IEEE Computational
Intelligence Magazine (IEEE CIM), and
other staff members of IEEE CIM for
their continuous support and guidance,
which made this special issue possible.
Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - February 2018
Contents
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