Efficient Interference Management Policies for Femtocell Networks

Managing interference in a network of macrocells underlaid with femtocells presents an important, yet challenging problem. A majority of spatial (frequency/time) reuse based approaches partition the users based on coloring the interference graph, which is shown to be suboptimal. Some spatial time reuse based approaches schedule the maximal independent sets (MISs) in a cyclic, (weighted) round-robin fashion, which is inefficient for delay-sensitive applications. Our proposed policies schedule the MISs in a non-cyclic fashion, which aim to optimize any given network performance criterion for delay-sensitive applications while fulfilling minimum throughput requirements of the users. Importantly, we do not take the interference graph as given as in existing works; we propose an optimal construction of the interference graph. We prove that under certain conditions, the proposed policy achieves the optimal network performance. For large networks, we propose a low-complexity algorithm for computing the proposed policy. We show that the policy computed achieves a constant competitive ratio (with respect to the optimal network performance), which is independent of the network size, under wide range of deployment scenarios. The policy can be implemented in a decentralized manner by the users. Compared to the existing policies, our proposed policies can achieve improvement of up to 130 % in large-scale deployments.

Distributed Interference Management Policies for Heterogeneous Small Cell Networks

We study the problem of interference management in large-scale small cell networks, where each user equipment (UE) needs to determine in a distributed manner when and at what power level it should transmit to its serving small cell base station (SBS) such that a given network performance criterion is maximized subject to minimum quality of service (QoS) requirements by the UEs. We first propose a distributed algorithm for the UE-SBS pairs to find a subset of weakly interfering UE-SBS pairs, namely the maximal independent sets (MISs) of the interference graph in logarithmic time (with respect to the number of UEs). Then we propose a novel problem formulation which enables UE-SBS pairs to determine the optimal fractions of time occupied by each MIS in a distributed manner. We analytically bound the performance of our distributed policy in terms of the competitive ratio with respect to the optimal network performance, which is obtained in a centralized manner with NP (non-deterministic polynomial time) complexity. Remarkably, the competitive ratio is independent of the network size, which guarantees scalability in terms of performance for arbitrarily large networks. Through simulations, we show that our proposed policies achieve significant performance improvements (from 150% to 700%) over the existing policies.