2,307 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
Architecture design for disaster resilient management network using D2D technology
Huge damages from natural disasters, such as earthquakes, floods, landslide, tsunamis, have been reported in recent years, claiming many lives, rendering millions homeless and causing huge financial losses worldwide. The lack of effective communication between the public rescue/safety agencies, rescue teams, first responders and trapped survivors/victims makes the situation even worse. Factors like dysfunctional communication networks, limited communications capacity, limited resources/services, data transformation and effective evaluation, energy, and power deficiency cause unnecessary hindrance in rescue and recovery services during a disaster. The new wireless communication technologies are needed to enhance life-saving capabilities and rescue services. In general, in order to improve societal resilience towards natural catastrophes and develop effective communication infrastructure, innovative approaches need to be initiated to provide improved quality, better connectivity in the events of natural and human disasters.
In this thesis, a disaster resilient network architecture is proposed and analysed using multi-hop communications, clustering, energy harvesting, throughput optimization, reliability enhancement, adaptive selection, and low latency communications. It also examines the importance of mode selection, power management, frequency and time resource allocation to realize the promises of Long-term Evolution (LTE) Device to Device (D2D) communication. In particular, to support resilient and energy efficient communication in disaster-affected areas.
This research is examined by thorough and vigorous simulations and validated through mathematical modelling. Overall, the impact of this research is twofold: i) it provides new technologies for effective inter- and intra-agency coordination system during a disaster event by establishing a stronger and resilient communication; and ii) It offers a potential solution for stakeholders such as governments, rescue teams, and general public with new informed information on how to establish effective policies to cope with challenges before, during and after the disaster events
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