Prediction of Packet Loss Rate in Non-Stationary Networks Based on Time-Varying Autoregressive Sequences

Abstract

Currently, most of the existing link parameter prediction schemes assume that the link state remains constant during the measurement period, making it difficult to capture their time-varying characteristics. To solve this problem, this paper proposes a prediction problem for packet loss rate in a non-stationary network environment. The measurement period is divided into several adjacent time windows, and the packet loss rates measured passively in each time window are regarded as non-stationary time sequences for real-time tracking to obtain the changes in link packet loss rate at a small cost. By analyzing time-varying autoregressive (TVAR) sequences, a scheme for estimating the time-varying coefficient was presented. In addition, a prediction scheme for the packet loss rate in a non-stationary network was proposed based on TVAR sequences. Finally, this paper conducts experiments based on a non-stationary network simulation environment established by the improved Gilbert–Elliot model and a small wireless multi-hop network experiment platform built in reality. Simulation and experimental results show that the prediction scheme of the packet loss rate based on the TVAR sequence can accurately predict the packet loss rate.

CLPREM: A real-time traffic prediction method for 5G mobile network

“…With the same number of model parameters, accuracy also improves relative to the linear model capturing more nonlinear relationships between network traffic. Unfortunately, accurately estimating model parameters for nonlinear models requires significant computational resources [ 12 ]. Although researchers can often mathematically approximate model parameters when certain conditions are true, this approximate parameter estimation method does not always hold when estimating complex 5G mobile traffic.…”