Efficient Interference Management Policies for Femtocell Networks

TL;DR

This paper introduces a novel interference management policy for femtocell networks that optimizes network performance for delay-sensitive applications, outperforming existing cyclic scheduling methods and achieving up to 130% improvement.

Contribution

It proposes a non-cyclic scheduling policy with an optimal interference graph construction, providing a low-complexity, decentralized solution with proven near-optimal performance.

Findings

Achieves up to 130% performance improvement over existing policies.

Provides a low-complexity algorithm with constant competitive ratio.

Enables decentralized implementation suitable for large networks.

Abstract

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.