Distributed Interference Management Policies for Heterogeneous Small Cell Networks

TL;DR

This paper introduces distributed interference management policies for large-scale heterogeneous small cell networks, enabling scalable, near-optimal performance through local algorithms for interference graph analysis and time resource allocation.

Contribution

It presents a novel distributed algorithm for identifying interference sets and a new formulation for time sharing, with proven scalability and performance bounds.

Findings

Achieves 150% to 700% performance improvements over existing policies.

Provides a scalable distributed solution with performance guarantees independent of network size.

Demonstrates effectiveness through extensive simulations.

Abstract

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.