5,461 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail
While the notion of age of information (AoI) has recently emerged as an
important concept for analyzing ultra-reliable low-latency communications
(URLLC), the majority of the existing works have focused on the average AoI
measure. However, an average AoI based design falls short in properly
characterizing the performance of URLLC systems as it cannot account for
extreme events that occur with very low probabilities. In contrast, in this
paper, the main objective is to go beyond the traditional notion of average AoI
by characterizing and optimizing a URLLC system while capturing the AoI tail
distribution. In particular, the problem of vehicles' power minimization while
ensuring stringent latency and reliability constraints in terms of
probabilistic AoI is studied. To this end, a novel and efficient mapping
between both AoI and queue length distributions is proposed. Subsequently,
extreme value theory (EVT) and Lyapunov optimization techniques are adopted to
formulate and solve the problem. Simulation results shows a nearly two-fold
improvement in terms of shortening the tail of the AoI distribution compared to
a baseline whose design is based on the maximum queue length among vehicles,
when the number of vehicular user equipment (VUE) pairs is 80. The results also
show that this performance gain increases significantly as the number of VUE
pairs increases.Comment: Accepted in IEEE GLOBECOM 2018 with 7 pages, 6 figure
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
A survey of self organisation in future cellular networks
This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks
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