Dynamic Spectrum Allocation and Sharing in Cognitive Cooperative Networks

Abstract

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.