Cluster-Specific Predictive Modeling: A Scalable Solution for Resource-Constrained Wi-Fi Controllers

Gianluca Fontanesi; Luca Barbieri; Lorenzo Galati Giordano; Alfonso Fernandez Duran; Thorsten Wild

eess.SPMar 23, 2026

Cluster-Specific Predictive Modeling: A Scalable Solution for Resource-Constrained Wi-Fi Controllers

Authors: Gianluca Fontanesi, Luca Barbieri, Lorenzo Galati Giordano, Alfonso Fernandez Duran, Thorsten Wild

Organizations: Radio Systems Research, Nokia Bell Labs, Stuttgart, Germany · Nokia Spain, Madrid

Abstract

This manuscript presents a comprehensive analysis of predictive modeling optimization in managed Wi-Fi networks through the integration of clustering algorithms and model evaluation techniques. The study addresses the challenges of deploying forecasting algorithms in large-scale environments managed by a central controller constrained by memory and computational resources. Feature-based clustering, supported by Principal Component Analysis (PCA) and advanced feature engineering, is employed to group time series data based on shared characteristics, enabling the development of cluster-specific predictive models. Comparative evaluations between global models (GMs) and cluster-specific models demonstrate that cluster-specific models consistently achieve superior accuracy in terms of Mean Absolute Error (MAE) values in high-activity clusters. The trade-offs between model complexity (and accuracy) and resource utilization are analyzed, highlighting the scalability of tailored modeling approaches. The findings advocate for adaptive network management strategies that optimize resource allocation through selective model deployment, enhance predictive accuracy, and ensure scalable operations in large-scale, centrally managed Wi-Fi environments.

Figures & tables

Fig. 1 : Proposed clustering and prediction analysis framework.

Feature Category	Count
Bytes Statistics	3
Mean, Std, Quantiles (per AP)
Active Users Statistics	2
Mean, Std (per AP)
Temporal Features	30
Bytes: Mean, Std $\times$ 3 periods † $\times$ 2 day types ‡	12

TABLE I : Extracted Features for Time Series Clustering (35 total)

Panel of Figure 4 — Fig. 3 : 10 min (a) and 60 min (b) future prediction for different clusters of data in a Wi-Fi campus network. The figure shows the results for a global and specialized model.

Fig. 4 : Improvement on MAE using $L_{kv2}$ (LSTMv2) model vs $L_{k}$ (LSTM) (10 min case only).

KPI	C0	C1	C2	C3	C4
MAE GM(10 min)	0.009	0.0028	0.00085	0.00327	0.00064
MAE GM(60 min)	0.018	0.010	0.0039	0.0073	0.00043
MAE (10 min)	0.0050	0.0021	0.0008	0.0013	0.0003
MAE (60 min)	0.014	0.006	0.004	0.0044	0.00044
99%tile (10 min)	0.05	0.036	0.005	0.018	0.01
99%tile (60 min)	0.22	0.16	0.028	0.1	0.02

TABLE II: KPI results for GM and

L_{k}

across all clusters. The error in the

99\%

tile is in MB.

Models	Deployed	Storage	Average
Deployed	Models		Accuracy
One Global Model	1	1 MB	0.008
All Cluster-Specific ( $L_{kv2}$ )	5	17.5 MB	0.004
Scalable Deployment Strategy	3	5.5 MB	0.0044

TABLE III : Summary of the models cost in term of memory and computations.

Time Series Forecasting

Time series forecasting predicts future values from ordered observations using statistical, neural, and probabilistic models to support decisions under uncertainty.

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An Informativeness-based Clustered Federated Learning Method for Reliable Traffic Prediction in Managed Wi-Fi Networks

Luca Barbieri, Gianluca Fontanesi, Lorenzo Galati Giordano, Alfonso Fernandez Duran+1

Radio Systems Research, Nokia Bell Labs, Stuttgart, Germany · Nokia Spain, Madrid

Wireless Local Area Networks Clustering Wireless Communications Traffic Forecasting Clustered Federated Learning

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Centrally-managed Wi-Fi solutions are increasingly leveraging Distributed Artificial Intelligence (AI) to predict key operational statistics of Access Points (APs) and proactively optimize network performance. In this context, Clustered Federated Learning (CFL) represents a fitting methodology, enabling the generation of multiple AI models that account for diverse statistical properties of the APs data distribution. However, identifying informative clusters for grouping APs models remains a significant challenge. In this paper, we address this problem by proposing a novel CFL tool integrating a two step clustering procedure. Initially, multiple clustering solutions are generated and filtered based on a minimum set of desired clustering criteria. Subsequently, if no solutions meet sufficient quality metrics, a global model is produced by aggregating all AP models. Otherwise, the final clustering solution is selected as the one that maximizes the informativeness (quantified via differential entropy) for the smallest cluster. Our results, focusing on a Wi-Fi traffic prediction problem, demonstrate that the developed CFL tool achieves the best predictive performance among all evaluated distributed strategies and the lowest communication and energy footprint among the clustered ones, exceeding the cost of single-model FL only in the regimes where it markedly improves accuracy.

Abstract

Time Series Forecasting

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