Wireless Link Quality Estimation Using LSTM Model

TL;DR

This paper introduces an LSTM-based model for wireless link quality estimation that outperforms traditional autoencoder-based methods by leveraging sequential data for more accurate predictions in real-world environments.

Contribution

The paper presents a novel LSTM-based WLQE model that effectively utilizes sequential information to improve prediction accuracy over existing models.

Findings

LSTM model achieves 4.0% higher accuracy than LQE-SAE.

LSTM model attains 4.6% higher macro-F1 score.

Model demonstrates superior performance in real-world conditions.

Abstract

In recent years, various services have been provided through high-speed and high-capacity wireless networks on mobile communication devices, necessitating stable communication regardless of indoor or outdoor environments. To achieve stable communication, it is essential to implement proactive measures, such as switching to an alternative path and ensuring data buffering before the communication quality becomes unstable. The technology of Wireless Link Quality Estimation (WLQE), which predicts the communication quality of wireless networks in advance, plays a crucial role in this context. In this paper, we propose a novel WLQE model for estimating the communication quality of wireless networks by leveraging sequential information. Our proposed method is based on Long Short-Term Memory (LSTM), enabling highly accurate estimation by considering the sequential information of link quality. We conducted a comparative evaluation with the conventional model, stacked autoencoder-based link quality estimator (LQE-SAE), using a dataset recorded in real-world environmental conditions. Our LSTM-based LQE model demonstrates its superiority, achieving a 4.0% higher accuracy and a 4.6% higher macro-F1 score than the LQE-SAE model in the evaluation.