用LSTM预测无线链路质量，提前保障通信稳定
Wireless Link Quality Estimation Using LSTM Model
- 基于LSTM捕捉链路质量的时序特征进行预测
- 相比传统方法准确率高4.0%，宏平均F1提升4.6%
- 适合需要实时稳定通信的移动网络应用
近年来，移动通信设备通过高速大容量无线网络提供各类服务，无论室内或室外环境均需保障通信稳定。为此，需采取主动措施，如切换路径和提前缓冲数据，以防止通信质量下降。无线链路质量估计（WLQE）技术通过预先预测无线网络通信质量，在此背景下至关重要。本文提出一种新型WLQE模型，利用长短期记忆网络（LSTM）捕捉链路质量的序列信息，实现高精度估计。我们在真实环境条件下采集的数据集上，与基于堆叠自编码器的链路质量估计器（LQE-SAE）进行对比评估。结果表明，所提LSTM-LQE模型在准确率上高出4.0%，宏平均F1分数提高4.6%，验证了其优越性。
原文摘要 · Abstract (English)
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.
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