Lossy Compression of Cellular Network KPIs

TL;DR

This paper demonstrates that cellular network KPIs can be effectively compressed using lossy schemes, significantly reducing data volume while maintaining analytical accuracy, thus easing storage and transmission challenges.

Contribution

It introduces a rate-distortion framework for KPI compression, showing that high compression ratios are achievable without impairing downstream analytics.

Findings

Achieves 8-10x reduction in KPI data size with 3-4 bits per sample.

Aggregation across cells reduces quantization errors.

Prediction accuracy remains stable at moderate reporting rates.

Abstract

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.