6,836 research outputs found
Channels Reallocation In Cognitive Radio Networks Based On DNA Sequence Alignment
Nowadays, It has been shown that spectrum scarcity increased due to
tremendous growth of new players in wireless base system by the evolution of
the radio communication. Resent survey found that there are many areas of the
radio spectrum that are occupied by authorized user/primary user (PU), which
are not fully utilized. Cognitive radios (CR) prove to next generation wireless
communication system that proposed as a way to reuse this under-utilised
spectrum in an opportunistic and non-interfering basis. A CR is a self-directed
entity in a wireless communications environment that senses its environment,
tracks changes, and reacts upon its findings and frequently exchanges
information with the networks for secondary user (SU). However, CR facing
collision problem with tracks changes i.e. reallocating of other empty channels
for SU while PU arrives. In this paper, channels reallocation technique based
on DNA sequence alignment algorithm for CR networks has been proposed.Comment: 12 page
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
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