Balancing High-Load Scenarios with Next Cell Predictions and Mobility Pattern Recognition
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Date
2012-02-21
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Abstract
Knowing where a mobile user will be next can
deliver a tremendous increase in network performance under
high load, as this knowledge enables pro-active load balancing.
To derive this information, sequences of traversed cells are fed
into pattern detection algorithms. After the training phase the
learned model predicts each user’s next cell. Even for complex
scenarios, the prediction accuracy can exceed 90%. Predictions
are used to rearrange mobile connections in a simulated high-
load scenario centered around an event at a soccer stadium.
To prevent call drops for mobile users targeting the stadium,
apropriate resources in the predicted next cell are reserved. The
results exceed 20% in improvements for throughput and call
drop rates, enabling the network to bear a much higher load
before stalling.
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Keywords
Handoff Optimization, Load Balancing, Mobility Prediction