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These features capture key temporal and spatial patterns that indicate a high likelihood of ping-pong events.
We engineer features based on the UEs handover history, such as the time intervals between previous handovers and ping-pongs. We also include boolean flags indicating if the UE has experienced ping-pong in the recent past, at various time scales.
Additionally, we incorporate cell-level features, such as cluster identification and previous ping-pong rates for the specific cells involved in the current handover.
By combining these rich historical and contextual signals, our model can learn complex patterns that presage ping-pong events. The next slide will show how we bring these features together into a cohesive prediction pipeline.
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IEEE/ICACT20260091 Slide.11
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