Resource allocation scheme for future user-centric wireless network

Abstract

Current communication landscape focuses on the integration of networks, where these networks exhibit heterogeneous characteristics. The motivation for such network integration arises the advent of the smart end-user devices and revolutionary advances in other network components, the telecommunication business models shift focus from network-centric to the user-centric paradigm. This further dictates that operators will find the profit windows in increasing the satisfied user pool, which intuitively may be translated into meeting the user requirements. The shift towards new paradigm gives birth to several major issues and suitability of the current bandwidth sharing algorithms is one of them. The research work for the new paradigm is confined to inter operator and intra operator levels only. The bandwidth allocation at last mile, i.e. at user-end is not widely addressed. There is a need for an optimal solution that considers user perceived Quality of Experience (QoE) in bandwidth allocation at user-end. An optimal solution of this problem will maximize user satisfaction and increase operator’s revenue. Adapting the economic components of the existing user satisfaction function will capture the user behaviour more realistically against multiple parameters. A user perceived QoE-based distributed decision making algorithm at network level will be deployed at base station and the interdependency would be captured. The characteristics of involved access technologies i.e. (WLAN, UMTS) will be confined to capacity and coverage only. MATLAB implementation is used to proof the concept. The performance gain of the proposed work was investigated by using various performance evaluation criteria, such as call blocking probability, operator’s revenue maximization and user satisfaction. The proposed optimum bandwidth allocation algorithm is compared with non-optimum bandwidth allocation algorithm in single radio interface as well as in heterogeneous environment. The obtained results show that optimum bandwidth allocation model maximizes user satisfaction and operator’s revenue and minimizes number of calls rejection.