5,471 research outputs found
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
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Joint Design and Separation Principle for Opportunistic Spectrum Access in the Presence of Sensing Errors
We address the design of opportunistic spectrum access (OSA) strategies that
allow secondary users to independently search for and exploit instantaneous
spectrum availability. Integrated in the joint design are three basic
components: a spectrum sensor that identifies spectrum opportunities, a sensing
strategy that determines which channels in the spectrum to sense, and an access
strategy that decides whether to access based on imperfect sensing outcomes.
We formulate the joint PHY-MAC design of OSA as a constrained partially
observable Markov decision process (POMDP). Constrained POMDPs generally
require randomized policies to achieve optimality, which are often intractable.
By exploiting the rich structure of the underlying problem, we establish a
separation principle for the joint design of OSA. This separation principle
reveals the optimality of myopic policies for the design of the spectrum sensor
and the access strategy, leading to closed-form optimal solutions. Furthermore,
decoupling the design of the sensing strategy from that of the spectrum sensor
and the access strategy, the separation principle reduces the constrained POMDP
to an unconstrained one, which admits deterministic optimal policies. Numerical
examples are provided to study the design tradeoffs, the interaction between
the spectrum sensor and the sensing and access strategies, and the robustness
of the ensuing design to model mismatch.Comment: 43 pages, 10 figures, submitted to IEEE Transactions on Information
Theory in Feb. 200
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