4,748 research outputs found
Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks
In this paper, a sequential probing method for interference constraint
learning is proposed to allow a centralized Cognitive Radio Network (CRN)
accessing the frequency band of a Primary User (PU) in an underlay cognitive
scenario with a designed PU protection specification. The main idea is that the
CRN probes the PU and subsequently eavesdrops the reverse PU link to acquire
the binary ACK/NACK packet. This feedback indicates whether the probing-induced
interference is harmful or not and can be used to learn the PU interference
constraint. The cognitive part of this sequential probing process is the
selection of the power levels of the Secondary Users (SUs) which aims to learn
the PU interference constraint with a minimum number of probing attempts while
setting a limit on the number of harmful probing-induced interference events or
equivalently of NACK packet observations over a time window. This constrained
design problem is studied within the Active Learning (AL) framework and an
optimal solution is derived and implemented with a sophisticated, accurate and
fast Bayesian Learning method, the Expectation Propagation (EP). The
performance of this solution is also demonstrated through numerical simulations
and compared with modified versions of AL techniques we developed in earlier
work.Comment: 14 pages, 6 figures, submitted to IEEE JSTSP Special Issue on Machine
Learning for Cognition in Radio Communications and Rada
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
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey
The Byzantine attack in cooperative spectrum sensing (CSS), also known as the
spectrum sensing data falsification (SSDF) attack in the literature, is one of
the key adversaries to the success of cognitive radio networks (CRNs). In the
past couple of years, the research on the Byzantine attack and defense
strategies has gained worldwide increasing attention. In this paper, we provide
a comprehensive survey and tutorial on the recent advances in the Byzantine
attack and defense for CSS in CRNs. Specifically, we first briefly present the
preliminaries of CSS for general readers, including signal detection
techniques, hypothesis testing, and data fusion. Second, we analyze the spear
and shield relation between Byzantine attack and defense from three aspects:
the vulnerability of CSS to attack, the obstacles in CSS to defense, and the
games between attack and defense. Then, we propose a taxonomy of the existing
Byzantine attack behaviors and elaborate on the corresponding attack
parameters, which determine where, who, how, and when to launch attacks. Next,
from the perspectives of homogeneous or heterogeneous scenarios, we classify
the existing defense algorithms, and provide an in-depth tutorial on the
state-of-the-art Byzantine defense schemes, commonly known as robust or secure
CSS in the literature. Furthermore, we highlight the unsolved research
challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral
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