Dynamic, Data-Driven Spectrum Management in Cognitive Small Cell Networks

Abstract

Recently, the deployment of small cells is considered as an effective solution to enhance the capacity in existing cellular networks. However, massive deployment of small cells also incurs severe interference and increased energy consumption, which degrades the energy efficiency of the system. In this paper, we analyze the energy efficiency in cognitive small cell network, and propose a traffic-aware distributed sensing and access scheme for cognitive small cells base stations (SBSs). The proposed scheme adopts the concept of dynamic data driven applications systems (DDDAS). In the DDDAS paradigm, a model of the underlying design space is managed dynamically, updated periodically based on measurements of data, and used to drive measurement functions and adaptation of system configurations. Through careful integration of DDDAS-based design principles, SBSs have the ability to configure their sensing and access parameters according to the traffic patterns that are actually encountered. Simulation results show that our proposed DDDAS-based scheme can achieve significantly higher energy efficiency compared to conventional spectrum sharing schemes in cognitive small cell networks.