부산대학교 도서관

5G networks enable emerging latency and bandwidth critical applications like industrial IoT, AR/VR, or autonomous vehicles, in addition to supporting traditional voice and data communications. In 5G infrastructure, Radio Access Networks (RANs) consist of radio base stations that communicate over wireless radio links. The communication, however, is prone to environmental changes like the weather and can suffer from radio link failure and interrupt ongoing services. The impact is severe in the above-mentioned applications. One way to mitigate such service interruption is to proactively predict failures and reconfigure the resource allocation accordingly. Existing works like the supervised ensemble learning-based model do not consider the spatial-temporal correlation between radio communication and weather changes. This paper proposes a communication link failure prediction scheme based on the LSTM-autoencoder that considers the spatial-temporal correlation between radio communication and weather forecast. We implement and evaluate the proposed scheme over a huge volume of real radio and weather data. The results confirm that the proposed scheme significantly outperforms the existing solutions.