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Moving on to slide 9, we dive into the details of our ping-pong detection algorithm. This algorithm is designed to identify historical ping-pong events for labeling our dataset.
The key criteria for detecting a ping-pong are:
The UE returns to the previous cell within a short time interval, which we set as 1 second.
The cell identifier pattern is Cell_i, followed by Cell_i+2, then back to Cell_i.
The algorithm takes in a timestamp, source cell, and target cell for each handover event. It then scans through the handover history for each UE, looking for the specific pattern of cell changes that indicate a ping-pong.
The outputs of the algorithm include the ping-pong count, ping-pong rate, and labels for training our machine learning model. By running this detection algorithm on our historical data, we can generate a labeled dataset for building our predictive model.
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IEEE/ICACT20260091 Slide.09
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