6,380 research outputs found
Dynamic Spectrum Allocation and Sharing in Cognitive Cooperative Networks
The dramatic increase of service quality and channel capacity in
wireless networks is severely limited by the scarcity of energy
and bandwidth, which are the two fundamental resources for
communications. New communications and networking paradigms such
as cooperative communication and cognitive radio networks emerged
in recent years that can intelligently and efficiently utilize
these scarce resources. With the development of these new
techniques, how to design efficient spectrum allocation and
sharing schemes becomes very important, due to the challenges
brought by the new techniques. In this dissertation we have
investigated several critical issues in spectrum allocation and
sharing and address these challenges.
Due to limited network resources in a multiuser radio environment,
a particular user may try to exploit the resources for
self-enrichment, which in turn may prompt other users to behave
the same way. In addition, cognitive users are able to make
intelligent decisions on spectrum usage and communication
parameters based on the sensed spectrum dynamics and other users'
decisions. Thus, it is important to analyze the intelligent
behavior and complicated interactions of cognitive users via
game-theoretic approaches. Moreover, the radio environment is
highly dynamic, subject to shadowing/fading, user mobility in
space/frequency domains, traffic variations, and etc. Such
dynamics brings a lot of overhead when users try to optimize
system performance through information exchange in real-time.
Hence, statistical modeling of spectrum variations becomes
essential in order to achieve near-optimal solutions on average.
In this dissertation, we first study a stochastic modeling
approach for dynamic spectrum access. Since the radio spectrum
environment is highly dynamic, we model the traffic variations in
dynamic spectrum access using continuous-time Markov chains that
characterizes future traffic patterns, and optimize access
probabilities to reduce performance degradation due to co-channel
interference. Second, we propose an evolutionary game framework
for cooperative spectrum sensing with selfish users, and develop
the optimal collaboration strategy that has better performance
than fully cooperating strategy. Further, we study user
cooperation enforcement for cooperative networks with selfish
users. We model the optimal relay selection and power control
problem as a Stackelberg game, and consider the joint benefits of
source nodes as buyers and relay nodes as sellers. The proposed
scheme achieves the same performance compared to traditional
centralized optimization while reducing the signaling overhead.
Finally, we investigate possible attacks on cooperative spectrum
sensing under the evolutionary sensing game framework, and analyze
their damage both theoretically and by simulations
Stackelberg Game for Distributed Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks
In this paper, we study the transmission strategy adaptation problem in an
RF-powered cognitive radio network, in which hybrid secondary users are able to
switch between the harvest-then-transmit mode and the ambient backscatter mode
for their communication with the secondary gateway. In the network, a monetary
incentive is introduced for managing the interference caused by the secondary
transmission with imperfect channel sensing. The sensing-pricing-transmitting
process of the secondary gateway and the transmitters is modeled as a
single-leader-multi-follower Stackelberg game. Furthermore, the follower
sub-game among the secondary transmitters is modeled as a generalized Nash
equilibrium problem with shared constraints. Based on our theoretical
discoveries regarding the properties of equilibria in the follower sub-game and
the Stackelberg game, we propose a distributed, iterative strategy searching
scheme that guarantees the convergence to the Stackelberg equilibrium. The
numerical simulations show that the proposed hybrid transmission scheme always
outperforms the schemes with fixed transmission modes. Furthermore, the
simulations reveal that the adopted hybrid scheme is able to achieve a higher
throughput than the sum of the throughput obtained from the schemes with fixed
transmission modes
Coalition Formation Game for Cooperative Cognitive Radio Using Gibbs Sampling
This paper considers a cognitive radio network in which each secondary user
selects a primary user to assist in order to get a chance of accessing the
primary user channel. Thus, each group of secondary users assisting the same
primary user forms a coaltion. Within each coalition, sequential relaying is
employed, and a relay ordering algorithm is used to make use of the relays in
an efficient manner. It is required then to find the optimal sets of secondary
users assisting each primary user such that the sum of their rates is
maximized. The problem is formulated as a coalition formation game, and a Gibbs
Sampling based algorithm is used to find the optimal coalition structure.Comment: 7 pages, 2 figure
A Comprehensive Survey of Potential Game Approaches to Wireless Networks
Potential games form a class of non-cooperative games where unilateral
improvement dynamics are guaranteed to converge in many practical cases. The
potential game approach has been applied to a wide range of wireless network
problems, particularly to a variety of channel assignment problems. In this
paper, the properties of potential games are introduced, and games in wireless
networks that have been proven to be potential games are comprehensively
discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on
Communications, vol. E98-B, no. 9, Sept. 201
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