Performance evaluation of throughput averaging methods for the proportional fair algorithm in indoor mobile networks

Abstract

Intelligent radio resource distribution techniques play an important role in improving the overall performance of next-generation networks. An efficient scheduling algorithm can help to achieve higher data rates, lower latency and increased fairness for users. This paper evaluates the performance of an indoor network, simulated using the Realistic Indoor Environment Generator (RIEG), when 11 different Throughput Averaging Methods (TAMs) for the Proportional Fair (PF) algorithm are used. The TAMs are compared by using a Comparative Factor (CF) that simultaneously takes into account the average throughput of the indoors users, the fairness and the outage ratio. Simulation results show that when improvement in fairness is the main goal, the recently-proposed low-complexity Median Method (MM) and Quartile Mean Method (QMM) are major rivals to the widely-used Arithmetic Mean Method (AMM) and Original Mean Method (OMM), while the Modified Quartile Mean Method (MQMM) is the leader when the average throughput of indoor users is the most important factor.