Interference alignment and scheduling in clustered partially connected small cell networks

Interference alignment (IA) is a beneficial interference management technique in heterogeneous networks (HetNets). Bearing this in mind, we target partially connected networks of small cells that are allocated randomly in a macrocell, whereas IA feasibility conditions may not be satisfied. To address this issue, we propose a new scheme with novel clustering and scheduling algorithms that is accomplished in three stages. The proposed scheme is accomplished by knowing the connection matrix of the established links that is obtained by different threshold values in the first stage. In the second stage, a new clustering algorithm is proposed to separate the cells to groups in which IA is feasible, and the feedback overhead is reduced. In the last stage, cluster scheduling method is proposed to efficiently schedule the clusters. The cluster scheduling allows the selected clusters with trivial intercluster interferences to transmit at the same time slot. The simulation results indicate the improvement of achievable averaged sum rate by the proposed clustering and cluster scheduling.