THESIS
2015
xii, 174 pages : illustrations ; 30 cm
Abstract
Recent years have witnessed great developments in communication techniques.
By the year of 2020, it is forecasted that the total mobile data traffic will have
a nine-fold increase, and the mobile broadband subscriptions are expected to
reach 7.7 billion, which will be more than doubled compared to 2014. Such unprecedented
increase brings two crucial challenges to the future communication
networks: providing sufficient capacity to meet the exploding traffic volume,
and handling the dramatic increase in related energy consumption. With its
agility and intelligence, CR technology is regarded as a promising solution
to tackle the spectrum efficiency and energy efficiency challenges mentioned
above. In this thesis, we design a set of policies, including the cooperative
sensing assi...[
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Recent years have witnessed great developments in communication techniques.
By the year of 2020, it is forecasted that the total mobile data traffic will have
a nine-fold increase, and the mobile broadband subscriptions are expected to
reach 7.7 billion, which will be more than doubled compared to 2014. Such unprecedented
increase brings two crucial challenges to the future communication
networks: providing sufficient capacity to meet the exploding traffic volume,
and handling the dramatic increase in related energy consumption. With its
agility and intelligence, CR technology is regarded as a promising solution
to tackle the spectrum efficiency and energy efficiency challenges mentioned
above. In this thesis, we design a set of policies, including the cooperative
sensing assignments guiding the Secondary Users (SUs) in sensing the individual
Primary User (PU) channels, the duration for carrying out spectrum
sensing and the channel access behavior, which achieve the spectrum efficiency
and energy efficiency in Cognitive Radio Networks (CRNs).
First, we study the dynamic scheduling for cooperative sensing under time-varying
spectrum environment. We focus on the tradeoff between achieving
better sensing accuracy on one channel and exploring more transmission opportunities
on the other channels by formulating the dynamic sensing scheduling
problem with the Partially Observable Markov Decision Process (POMDP).
We analyze the solution structure for the myopic and the optimal policies.
The theoretical results show some interesting properties and lead to a simple
structural policy.
Second, we consider the problem of optimal Cooperative Sensing Scheduling
(CSS) and parameter design to achieve energy efficiency in CRNs using
the framework of POMDP. We investigate the combinatorial CSS problem under
the framework of discrete convex analysis theory and obtain the optimal
solution in an analytical way. We also show that the myopic CSS policy is the
optimal CSS under certain network architectures, and the relationship between
optimal and myopic sensing durations is discussed. Furthermore, we introduce
a punishment parameter for the unsuccessful transmissions to help the CRN
to cause less collision to the PUs so as to achieve higher energy efficiency by
saving the power for retransmission. The myopic performance upper bound
has also been derived.
Third, we investigate the fundamental CSS problem in a thorough way,
by considering various aspects related to the energy consumption in CRN. A
theoretical framework is established to analyze the structures of the homogeneous
network scenario and algorithms guaranteeing the optimal solutions
to be found are developed. With the insights gained, we extend the study
to the heterogeneous case. We further design policies including the optimal
assignments for the sensors and the optimal sensing duration, which jointly
maximize the network utilities reflecting the energy efficiency.
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