4,639 research outputs found
Signal Detection for QPSK Based Cognitive Radio Systems using Support Vector Machines
Cognitive radio based network enables opportunistic dynamic spectrum access by sensing, adopting and utilizing the unused portion of licensed spectrum bands. Cognitive radio is intelligent enough to adapt the communication parameters of the unused licensed spectrum. Spectrum sensing is one of the most important tasks of the cognitive radio cycle. In this paper, the auto-correlation function kernel based Support Vector Machine (SVM) classifier along with Welch's Periodogram detector is successfully implemented for the detection of four QPSK (Quadrature Phase Shift Keying) based signals propagating through an AWGN (Additive White Gaussian Noise) channel. It is shown that the combination of statistical signal processing and machine learning concepts improve the spectrum sensing process and spectrum sensing is possible even at low Signal to Noise Ratio (SNR) values up to -50 dB
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
Analysis of Spectrum Occupancy Using Machine Learning Algorithms
In this paper, we analyze the spectrum occupancy using different machine
learning techniques. Both supervised techniques (naive Bayesian classifier
(NBC), decision trees (DT), support vector machine (SVM), linear regression
(LR)) and unsupervised algorithm (hidden markov model (HMM)) are studied to
find the best technique with the highest classification accuracy (CA). A
detailed comparison of the supervised and unsupervised algorithms in terms of
the computational time and classification accuracy is performed. The classified
occupancy status is further utilized to evaluate the probability of secondary
user outage for the future time slots, which can be used by system designers to
define spectrum allocation and spectrum sharing policies. Numerical results
show that SVM is the best algorithm among all the supervised and unsupervised
classifiers. Based on this, we proposed a new SVM algorithm by combining it
with fire fly algorithm (FFA), which is shown to outperform all other
algorithms.Comment: 21 pages, 6 figure
Location-free Spectrum Cartography
Spectrum cartography constructs maps of metrics such as channel gain or
received signal power across a geographic area of interest using spatially
distributed sensor measurements. Applications of these maps include network
planning, interference coordination, power control, localization, and cognitive
radios to name a few. Since existing spectrum cartography techniques require
accurate estimates of the sensor locations, their performance is drastically
impaired by multipath affecting the positioning pilot signals, as occurs in
indoor or dense urban scenarios. To overcome such a limitation, this paper
introduces a novel paradigm for spectrum cartography, where estimation of
spectral maps relies on features of these positioning signals rather than on
location estimates. Specific learning algorithms are built upon this approach
and offer a markedly improved estimation performance than existing approaches
relying on localization, as demonstrated by simulation studies in indoor
scenarios.Comment: 14 pages, 12 figures, 1 table. Submitted to IEEE Transactions on
Signal Processin
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