TITLE:
A Link Quality Prediction Method Based on External Attention and Prior Probability
AUTHORS:
Meng Xie, Weibin Shi, Yulai Lie, Wenfeng Xu
KEYWORDS:
Transformer, Link Quality Prediction, External Attention, Prior Probability
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.16 No.4,
April
10,
2026
ABSTRACT: Link quality estimation is a critical foundation for path selection in routing protocols of wireless sensor networks. Affected by multipath fading, noise, and interference in wireless channels, wireless links typically exhibit nonlinear and non-stationary characteristics, which pose challenges to efficient and accurate link quality prediction. To address the issues of error accumulation and lack of parallel computing in existing autoregressive methods, a Transformer-based link quality prediction method named LEAPP is proposed. A multi-head external attention mechanism is introduced in the encoder to reduce computational complexity and enhance global modeling capability. The decoder uses packet success rate (PSR) as input and constructs a non-autoregressive prediction model, achieving effective integration of prior probability and deep learning models. Experimental results show that compared with baseline models, the MAE and RMSE of LEAPP are reduced by 22.1% and 16.3%, respectively, and the MAE drops to 0.0092 on the public dataset. Meanwhile, the non-autoregressive inference mode reduces the inference delay by approximately 82% compared with traditional methods, significantly improving the real-time performance and practicality of online link quality prediction.