5GR-DTAD: A Domain and Data-Driven Framework for Diagnosing Abnormal Downlink Throughput in 5G RAN

The advent of 5G wireless technology marks a significant milestone in telecommunications, enhancing consumer and industrial applications through Industry 4.0 technologies. The radio access network (RAN) and its downlink throughput are vital for Internet service providers. However, ensuring 5G RAN downlink throughput reliability requires robust failure diagnosis strategies for quick identification and resolution, posing challenges in cost efficiency, expert trustworthiness, and adaptability. We present 5G RAN Downlink Throughput Abnormality Diagnosis (5GR-DTAD), a diagnostic framework that fusing domain expertise with machine learning techniques, incorporating both feed-forward and long short-term memory (LSTM) neural networks. Experimental results on real-world data from multiple base stations show that 5GR-DTAD outperforms existing methods in precision, recall, and F1 scores by up to 29.85%, 42.28%, and 35.74%, respectively. 5GR-DTAD improves diagnostic accuracy while minimizing reliance on labeled data, offering an adaptable and cost-effective solution for various 5G RAN conditions.