Distributed Interference Management Policies for Heterogeneous Small Cell Networks

We study the problem of distributed interference management in a network of heterogeneous small cells with different cell sizes, different numbers of user equipments (UEs) served, and different throughput requirements by UEs. We consider the uplink transmission, where each UE determines when and at what power level it should transmit to its serving small cell base station (SBS). We propose a general framework for designing distributed interference management policies, which exploits weak interference among non-neighboring UEs by letting them transmit simultaneously (i.e., spatial reuse), while eliminating strong interference among neighboring UEs by letting them transmit in different time slots. The design of optimal interference management policies has two key steps. Ideally, we need to find all the subsets of non-interfering UEs i.e., the maximal independent sets (MISs) of the interference graph, but this is computationally intractable even when solved in a centralized manner. Then, to maximize some given network performance criterion subject to UEs' minimum throughput requirements, we need to determine the optimal fraction of time occupied by each MIS, which requires global information (e.g., all the UEs' throughput requirements and channel gains). In our framework, we first propose a distributed algorithm for the UE-SBS pairs to find a subset of MISs in logarithmic time (with respect to the number of UEs). Then we propose a novel problem reformulation which enables UE-SBS pairs to determine the optimal fraction of time occupied by each MIS with only local message exchange among the neighbors in the interference graph. Despite the fact that our interference management policies are distributed and utilize only local information, we can analytically bound their performance under a wide range of heterogeneous deployment scenarios in terms of the competitive ratio with respect to the optimal network performance, which can only be obtained in a centralized manner with NP complexity. Remarkably, we prove that the competitive ratio is independent of the network size. Through extensive simulations, we show that our proposed policies achieve significant performance improvements (ranging from 160% to 700%) over state-of-the-art policies.