# Analysis of Proportional Fair Scheduling Under Bursty On-Off Traffic

## TL;DR

This paper provides an analytical model for proportional fair scheduling performance under bursty on-off traffic in wireless networks, using Gaussian approximation and multi-interference analysis to achieve accurate data rate predictions.

## Contribution

It introduces the first analytical solution for PFS under bursty traffic and enhances accuracy with a hybrid approximation method for multi-cell networks.

## Key findings

- Model guarantees low data rate estimation error
- Insensitivity to session duration, traffic load, and user density
- Hybrid approximation improves multi-cell network analysis

## Abstract

Proportional fair scheduling (PFS) has been adopted as a standard solution for fair resource allocation in modern wireless cellular networks. With the emergence of heterogeneous networks with widely varying user loads, it is of great importance to characterize the performance of PFS under bursty traffic, which is the case in most wireless streaming and data transfer services. In this letter, we provide the first analytical solution to the performance of PFS under bursty on-off traffic load. We use the Gaussian approximation model to derive a closed-form expression of the achievable user data rates. In order to further improve the accuracy of our baseline analytical solution for multi-cell networks, we design a hybrid approximation by employing multi-interference analysis. The simulation results verify that our model guarantees extremely low data rate estimation error, which is further insensitive to changes in session duration, traffic load and user density.