WLAN Traffic Prediction Model for Smooth AP Roaming Control

The patent segments the traffic prediction system into multiple independent modules: data collection module (gathering RSSI, negotiation rate, topology data), data processing module (cleaning and preparing data), prediction model module (using random forest or neural networks), and application module (roaming decisions, power adjustment). This segmentation allows each module to handle specific tasks independently, improving prediction accuracy while maintaining manageable system complexity through modular architecture.

The patent transitions from conventional single-parameter traffic prediction to multi-dimensional prediction by incorporating diverse data dimensions: signal quality (RSSI), negotiation parameters, network topology structure, application type, and temporal patterns. This dimensional expansion enables the system to capture comprehensive traffic characteristics, significantly improving prediction accuracy without overwhelming complexity due to structured data organization.

The patent implements preliminary data processing actions during the data collection phase, including real-time filtering of invalid RSSI values, normalization of negotiation rates, and pre-processing of topology information. By performing these actions in advance before the prediction phase, the system reduces computation complexity during actual prediction while maintaining high prediction accuracy through comprehensive data preparation.

The patent applies parameter changes by transforming raw communication data into standardized features: converting RSSI values to signal quality indices, normalizing negotiation rates to standard units, and encoding topology structures into categorical representations. These parameter transformations simplify the data structure and reduce computation complexity while preserving the essential information needed for accurate traffic prediction.

The patent applies partial action by selectively processing only the necessary features for prediction at each time step rather than processing all historical data. The system uses a subset of most relevant features (current RSSI, recent negotiation rates, immediate topology changes) for real-time prediction, reducing computation resource consumption while maintaining fast response speed. This partial processing approach balances accuracy and efficiency.

The patent implements feedback mechanisms where prediction results are used to adjust network parameters (power, bandwidth, roaming decisions), and these adjustments feed back into the prediction model for continuous improvement. The feedback loop allows the system to learn from actual network behavior and refine predictions, improving accuracy over time while the model adapts to reduce computational requirements through experience.