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A dataset with RAN KPI data, collected from over 100 5G and 4G NEs, will be provided to train machine-learning models. For each NE in the network, it provides 7 key KPIs, the duration of a fault, and the “distance” of the NE to the fault in an hourly basis. Specifically, each row includes:

• NE ID, which is a unique identifier for each NE;
• Hourly timestamp of the data;
• 6 RAN KPIs that is related to data rate, including Access Success Rate, Resource

Utilizing Rate, TA (Time Advanced), BLER (Block Error Rate), CQI (Channel Quality Indicator), MCS (Modulation and Coding Scheme);

• Data rate of the hour;
• Fault duration (in seconds) during the hour.
• “Distance” (Relation) of the NE to the fault, where

1. If the value is 0, it means that there is no fault during the hour;
2. If the value is 1, it means that the fault occurred exactly at the NE;
3. If the value is between 0 and 1, it means that the fault occurred at a neighboring NE, and that a larger value indicates a higher adjacency between the 2 NEs.

Specifically, for the submitted model, its input and output are:

Input:

1. Data rate and other features collected before the fault occurs, where fault duration is 0;
2. Fault duration and relation in the first hour during which the fault appears, i.e., the fault duration is larger than 0;

Output:

1. Status of data rate in the first hour during which the fault appears, i.e., the fault duration is larger than 0.

1. For NE 213172-1, the fault occurred exactly at the NE from 2022-12-6 18:00 to 2022-12-6 19:00.

2. For NE 211172-1, the fault occurred at a neighboring NE from 2022-12-6 8:00 to 2022-12-6 10:00, and the adjacency between the 2 NEs is relatively high.

3. For NE 211182-2, the fault occurred at a neighboring NE at 2022-12-7 0:00, and the adjacency between the 2 NEs is relatively low.