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.

翻译：网络关键性能指标（KPIs）是移动蜂窝网络监控与优化的基础组成部分。由于需在长时间跨度内对众多蜂窝小区进行细粒度测量，其产生的海量数据对存储、传输和大规模分析构成了重大挑战。本文通过研究表明，常见的蜂窝网络KPIs可采用基于预测、量化和熵编码的标准有损压缩方案实现高效压缩，从而显著降低上报开销。聚焦于流量类KPI，我们首先通过率失真分析刻画其内在可压缩性，证明仅需每样本3-4比特即可实现约30 dB的信噪比，相当于在32位浮点数表示基础上实现8-10倍的压缩率。随后我们评估了KPI压缩对典型下游分析任务的影响。结果表明：跨蜂窝小区的聚合操作可有效抑制量化误差，且在适度上报速率下预测精度不受影响。这些发现表明，KPI压缩在蜂窝系统中具有可行性，且对网络层面分析具有透明性。