Efficient Subchannel Allocation Based on Partial Clustered Interference Alignment and Clusters Merging in Dense Small Cell Networks

Abstract

This article investigates efficient subchannel allocation based on the proposed partial clustered interference alignment (PCIA) and clusters merging algorithms in dense small cell networks (SCNs), which well adapts to the randomly deployed dense SCNs and can further improve the achievable data streams. Specifically, we aim at maximizing the data streams achieved by all small cell user equipments (SUEs) while satisfying the requirement for data streams of each SUE. Due to the NP-hardness of the corresponding problem, we propose an efficient solution consisting of three phases: partial clustering, clusters merging, and subchannel allocation. Each phase requires much lower complexity and reduced feedback overhead. Simulations show that the proposed solution not only outperforms other related schemes but also has a smaller performance gap with the approximate optimal solution.