73,822 research outputs found
On the Monotonicity of the Generalized Marcum and Nuttall Q-Functions
Monotonicity criteria are established for the generalized Marcum Q-function,
\emph{Q}_{M}, the standard Nuttall Q-function, \emph{Q}_{M,N}, and the
normalized Nuttall Q-function, , with respect to their real
order indices M,N. Besides, closed-form expressions are derived for the
computation of the standard and normalized Nuttall Q-functions for the case
when M,N are odd multiples of 0.5 and . By exploiting these results,
novel upper and lower bounds for \emph{Q}_{M,N} and are
proposed. Furthermore, specific tight upper and lower bounds for
\emph{Q}_{M}, previously reported in the literature, are extended for real
values of M. The offered theoretical results can be efficiently applied in the
study of digital communications over fading channels, in the
information-theoretic analysis of multiple-input multiple-output systems and in
the description of stochastic processes in probability theory, among others.Comment: Published in IEEE Transactions on Information Theory, August 2009.
Only slight formatting modification
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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