Non-Stationary Wireless Channel Modeling Approach Based on Extreme Value Theory for Ultra-Reliable Communications

TL;DR

This paper introduces a novel EVT-based methodology for modeling extreme events in non-stationary wireless channels, crucial for designing ultra-reliable communication systems with stringent error rate requirements.

Contribution

It develops a comprehensive EVT framework that accounts for non-stationarity by segmenting data, fitting GPDs, and selecting optimal thresholds, improving extreme event modeling accuracy.

Findings

Accurately models extreme channel events under non-stationarity.

Outperforms stationary models in fitting extreme data.

Demonstrates effectiveness with real vehicle engine data.

Abstract

A proper channel modeling methodology that characterizes the statistics of extreme events is key in the design of a system at an ultra-reliable regime of operation. The strict constraint of ultra-reliability corresponds to the packet error rate (PER) in the range of $10^{-9}-10^{-5}$ within the acceptable latency on the order of milliseconds. Extreme value theory (EVT) is a robust framework for modeling the statistical behavior of extreme events in the channel data. In this paper, we propose a methodology based on EVT to model the extreme events of a non-stationary wireless channel for the ultra-reliable regime of operation. This methodology includes techniques for splitting the channel data sequence into multiple groups concerning the environmental factors causing non-stationarity, and fitting the lower tail distribution of the received power in each group to the generalized Pareto distribution (GPD). The proposed approach also consists of optimally determining the time-varying threshold over which the tail statistics are derived as a function of time, and assessing the validity of the derived Pareto model. Finally, the proposed approach chooses the best model with minimum complexity that represents the time variation behavior of the non-stationary channel data sequence. Based on the data collected within the engine compartment of Fiat Linea under various engine vibrations and driving scenarios, we demonstrate the capability of the proposed methodology in providing the best fit to the extremes of the non-stationary data, significantly outperforming the channel modeling approach in characterizing the extreme events with the stationary channel assumption.