Analytical Evaluation of Fractional Frequency Reuse for OFDMA Cellular Networks

TL;DR

This paper analytically evaluates fractional frequency reuse (FFR) techniques in OFDMA cellular networks using stochastic geometry, providing insights into system design, performance trade-offs, and resource allocation strategies.

Contribution

It introduces an analytical framework for FFR evaluation using Poisson point processes, contrasting with traditional grid-based models, and offers design guidelines and performance insights.

Findings

FFR increases sum-rate and coverage for cell-edge users.

Strict FFR outperforms SFR at low traffic loads in throughput.

SFR better balances interference reduction and resource efficiency at high traffic loads.

Abstract

Fractional frequency reuse (FFR) is an interference management technique well-suited to OFDMA-based cellular networks wherein the cells are partitioned into spatial regions with different frequency reuse factors. To date, FFR techniques have been typically been evaluated through system-level simulations using a hexagonal grid for the base station locations. This paper instead focuses on analytically evaluating the two main types of FFR deployments - Strict FFR and Soft Frequency Reuse (SFR) - using a Poisson point process to model the base station locations. The results are compared with the standard grid model and an actual urban deployment. Under reasonable special cases for modern cellular networks, our results reduce to simple closed-form expressions, which provide insight into system design guidelines and the relative merits of Strict FFR, SFR, universal reuse, and fixed frequency reuse. We observe that FFR provides an increase in the sum-rate as well as the well-known benefit of improved coverage for cell-edge users. Finally, a SINR-proportional resource allocation strategy is proposed based on the analytical expressions, showing that Strict FFR provides greater overall network throughput at low traffic loads, while SFR better balances the requirements of interference reduction and resource efficiency when the traffic load is high.