Research On Spectrum Sharing Strategies For Wireless Network Virtualization

At present, the wireless network architecture is static and closed, which allows problems in many areas of wireless network. In terms of the use of resources, the use of wireless network resources is not flexible and the allocation of spectrum is static, so that the resource utilization is very low. This static and closed wireless network infrastructure needs to be improved to adapt to the evolving Internet for wider coverage and higher capacity requirements. Nevertheless, wireless network virtualization architecture can achieve efficient and rational utilization of network resources, improve the user experience while reducing the operators’ costs of management and maintenance. The common spectrum sharing strategies of network virtualization such as game theory algorithm, Lokta-Volterra model and auction model are summarized in the thesis. This thesis has mainly studied the spectrum sharing strategies between operators.To maximize the revenue of network operator and virtual network operator simultaneously in network virtualization environment, a spectrum allocation algorithm based on Stackelberg game is presented in this thesis. In this model, the revenue function of network operator and virtual network operator which consists of the utility function and the cost function is introduced. Also, the pricing strategy of the network operator under static game and the spectrum allocation algorithm under static and dynamic game are presented respectively. Simulation results show the effectiveness of the algorithm and the existence of Nash equilibrium point.To model and analyze the dynamics of interactive decision making for pricing, spectrum sharing, and service selection between users and service providers, a synergetics-based quantification model is presented in this thesis. We establish the motion equation of population state which builds the relationship between the micro-level and macro-level. One service provider shares a portion of its spectrum with other service providers at a certain price to allow better utilization of the available spectrum. To this end, users can make service selection decisions dynamically according to the performance satisfaction level and cost, depending on the pricing plans and spectrum sharing strategy. Based on this equation, the SP renting the spectrum dynamically optimize the price of the spectrum and the SP sharing its spectrum dynamically adjusts the open access ratio according to the given price, to maximize their respective benefit. Simulation results have showed the effectiveness of dynamic controls and higher payoffs it compared with the strategy without spectrum sharing.