156 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
On the Role of 5G and Beyond Sidelink Communication in Multi-Hop Tactical Networks
This work investigates the potential of 5G and beyond sidelink (SL)
communication to support multi-hop tactical networks. We first provide a
technical and historical overview of 3GPP SL standardization activities, and
then consider applications to current problems of interest in tactical
networking. We consider a number of multi-hop routing techniques which are
expected to be of interest for SL-enabled multi-hop tactical networking and
examine open-source tools useful for network emulation. Finally, we discuss
relevant research directions which may be of interest for 5G SL-enabled
tactical communications, namely the integration of RF sensing and positioning,
as well as emerging machine learning tools such as federated and decentralized
learning, which may be of great interest for resource allocation and routing
problems that arise in tactical applications. We conclude by summarizing recent
developments in the 5G SL literature and provide guidelines for future
research.Comment: 6 pages, 4 figures. To be presented at 2023 IEEE MILCOM Workshops,
Boston, M
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