Lossy Compression of Cellular Network KPIs

Network Key Performance Indicators (KPIs) are a fundamental component of mobile cellular network monitoring and optimization. Their massive volume, resulting from fine-grained measurements collected across many cells over long time horizons, poses significant challenges for storage, transport, and large-scale analysis. In this letter, we show that common cellular KPIs can be efficiently compressed using standard lossy compression schemes based on prediction, quantization, and entropy coding, achieving substantial reductions in reporting overhead. Focusing on traffic volume KPIs, we first characterize their intrinsic compressibility through a rate-distortion analysis, showing that signal-to-noise ratios around 30 dB can be achieved using only 3-4 bits per sample, corresponding to an 8-10x reduction with respect to 32-bit floating-point representations. We then assess the impact of KPI compression on representative downstream analytics tasks. Our results show that aggregation across cells mitigates quantization errors and that prediction accuracy is unaffected beyond a moderate reporting rate. These findings indicate that KPI compression is feasible and transparent to network-level analytics in cellular systems.

💡 Research Summary

The paper addresses the growing challenge of storing, transporting, and analyzing massive volumes of cellular network key performance indicators (KPIs). In modern 4G and 5G deployments, thousands of cells generate hourly measurements of several KPIs (downlink traffic volume, physical resource block (PRB) occupancy, and number of active users). Over a month, this results in data sizes that strain conventional storage and network monitoring pipelines, yet operators typically rely on lossless compression or overly conservative lossy schemes because the impact of compression on downstream analytics is not well understood.

The authors adopt a task‑centric approach: they first quantify the intrinsic compressibility of the KPI time series through a rate‑distortion analysis, then evaluate how compression affects two representative downstream tasks—aggregation of traffic across cells (a core‑network level metric) and short‑term traffic forecasting using the Median Weekly Signature (MWS) predictor.

Dataset and preprocessing
Operational measurements from ~3,000 LTE cells in a mid‑size European city are used. The data cover one month (October 2023) with hourly sampling. Missing values are discarded. For each KPI, the per‑cell series (x_c