QoS-Based Interference Alignment With Similarity Clustering for Efficient Subchannel Allocation in Dense Small Cell Networks

Abstract

Interference alignment (IA) can remarkably improve the spectral efficiency of dense small cell networks (SCNs) underlaying a macrocell, but its feasibility condition and implementation complexity are restricted by the number of small cell equipments (SUEs). Moreover, the SUEs performing IA may have unsatisfactory quality of service (QoS) requirements as IA only eliminates interference while neglecting the gain of desired signals. In this paper, we propose a centralized efficient subchannel allocation scheme based on IA with similarity clustering in dense SCNs underlaying a macrocell, which aims at maximizing the number of QoS guaranteed SUEs performing IA. The corresponding problem is formulated as a combinatorial optimization problem which is NP-hard. So a low-complexity solution is proposed which includes three phases: similarity clustering for SUEs through graph partitioning, further adjustments of cluster sizes to make IA feasible in each cluster, and subchannel allocation for the formed clusters, each of which is performed with a notably reduced computational complexity. Moreover, the proposed solution greatly reduces the signaling overhead incurred by channel state information estimation. Numerical results show that the proposed solution not only outperforms other related schemes, but also achieves a performance close to the optimal solution.