Average effective degrees of freedom (AEDoF) maximization with interference alignment in small cell networks

The emergence of small cells provides a cost-effective way to satisfy users’ explosive traffic requirements. The massive deployment of small cells, nevertheless, causes severe inter-cell interference in Orthogonal Frequency Division Multiple-Access -based cellular networks. As such, conventional interference management strategies may be inefficient and interference alignment (IA) has been proposed as a promising technology to cope with inter-cell interference. To perfectly align all interference in a reduced-dimensional subspace, IA transmitters generally call for global channel state information (CSI) across small cell networks through receivers’ feedback. However, the number of total feedback bits scales as the square of the number of small cells. Hence, IA achieves a greater multiplexing gain at the cost of substantial overhead. To enable a tradeoff between multiplexing gain and overhead reduction, in this paper we present a new metric termed average effective degrees of freedom (AEDoF), which embodies the average degrees of freedom of small cell networks with CSI overhead considered. Furthermore, for reducing the computational complexity, we propose a graph-based clustering algorithm to solve the formulated AEDoF maximization problem. Simulation results verify that our proposed algorithm is of low complexity and achieves the maximum spectrum efficiency among several existing clustering methods.