Research On Specturm Sharing For Cognitive Radio Networks Based On Game Theory

Frequency spectrum is the scarcest radio resource in wireless communication networks. The concept of cognitive radio was introduced to improve the frequency spectrum utilization in wireless networks. Traditionally, radio spectrum is statically allocated to licensed wireless users. However, it is observed that some frequency bands in the radio spectrum are largely unused in any time and location. These are referred to as spectrum holes (or spectrum opportunities). Cognitive radio takes advantage of these spectrum opportunities to improve spectrum utilization and network performance. Developed based on software-defined radio, a cognitive radio transmitter can adaptively and intelligently change the transmission parameters in a dynamic environment. With cognitive radio, frequency spectrum can be efficiently shared among multiple users to improve spectrum usage. From an economic viewpoint, it can generate more revenue for the spectrum owner and also enhance the satisfaction of cognitive radio users.While most of the work in the area of cognitive radio emphasized the technical aspect of spectrum sharing (e.g., spectrum sensing, protocol for dynamic spectrum access, dynamic radio resource control), in this article we focus on the economic aspect of spectrum sharing. We use the term spectrum trading to refer to the process of selling and buying radio resource (e.g., spectrum) in a cognitive radio environment.The work in this dissertation is concluded as following points.Firstly, a brief overview of the CR network architecture is provided. Then four main functions of spectrum management are discussed:spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility. We describe the different network architectures and protocol behaviors for cognitive radio as well as the different spectrum sharing models. The motivation and necessity for spectrum trading are stated. Also, the scope of spectrum trading in the context of dynamic spectrum access is discussed. Different structures of spectrum trading, the related research issues, and the possible solution approaches are presented. This article provides a game theoretical overview of dynamic spectrum sharing from two aspects: analysis of network users'behaviors, efficient dynamic distributed design.Secondary, in chaper 3, we study the pricing issue in a competitive cognitive radio network in which the secondary users strategically adjust their uplink transmission power levels to maximize their own utilities, and the primary service provider (e.g., base station) charges the secondary users on their transmitted power levels to enhance its own revenue. We model the competitive behavior of the secondary users as a non-cooperative game and address the existence and uniqueness of Nash equilibrium. Based on the unique equilibrium, we formulate the pricing problem for the primary service provider as a non-convex optimization problem. We propose a sub-optimal pricing scheme in terms of revenue maximization of the primary service provider, and we claim that this scheme is fair in terms of power allocation among secondary users.Thirdly, in chaper 4, we propose two oligopolistic models for price competition among primary service providers. A non-cooperative game is formulated to obtain the price. The Nash and Stackelberg equilibrium are considered as the solutions of the simultaneous-move and leader-follower price competitions, respectively. Furthermore, we have considered cooperative pricing model where all of the service providers can cooperate to achieve the highest total revenue. Different fairness criteria are chosen for apportioning the coalition worth among its members.Fourthly, in chaper 5, A hierarchical spectrum trading model is presented to analyze the interaction among TV broadcasters, WRAN service providers, and WRAN users. In this model a double auction is established among multiple TV broadcasters and WRAN service providers who sell and buy the radio spectrum, respectively. In particular, the theory of evolutionary games is used to investigate the dynamics of WRAN user behavior and solution in network selection. A centralized algorithm is proposed to implement the proposed evolutionary game model for network selection. Then, multiple WRAN service providers compete with each other by adjusting the service price charged to WRAN users. To model the competition, a non-cooperative game is formulated. In order to maximize their own profits, every WRAN service provider should seek the optimal spectrum bidding and service pricing strategy.Finally, a conclusion is drawn for the dissertation, and valuable research directions in the future are presented.