Progress in Signals and Telecommunication Engineering Nguyen Minh Tuan 15 2 2026 https://doi.org/10.6025/pste/2026/15/2/88-113 https://www.dline.info/pste/fulltext/v15n2/pstev15n2_3.pdf Modern telecommunication networks are characterised by multidimensional, dynamically varying performance conditions that no single key performance indicator (KPI) can adequately capture. This study develops and empirically validates the Telecommunication Network Quality Index (TNQI), a composite measure that integrates three heterogeneous network performance indicators signal strength (dBm), latency (ms), and packet loss (%) into a unified 0-100 quality scale. Using a dataset of 3,000 temporally ordered network observations collected between 1 January 2023 and 5 May 2023 across five network technologies (4G LTE, 5G NR, Fibre Optic, Microwave, and Satellite), the study employs directional min-max normalisation, equal weight aggregation, Pearson and Spearman correlation analysis, weighted reliability scoring, and quartile-based classification. The correlation analysis reveals near zero associations among the three KPIs (|r|ï‚£ 0.019), confirming that signal quality, network responsiveness, and packet delivery reliability represent statistically independent dimensions of communication performance. The overall mean TNQI was 50.39 (SD = 16.66; range: 2.12-94.41), indicating moderate average communication quality with substantial heterogeneity. Network-type comparisons yielded relatively modest differences, with Satellite recording the highest mean TNQI (52.19) and 5G NR the lowest (49.34). A complementary weighted network reliability score (30% signal, 35% latency, 35% packet loss) and an inverse network degradation index (NDI = 100 - TNQI) provide operational monitoring perspectives. Quartile-based classification partitions observations into Critical, Poor, Good, and Excellent states. The findings demonstrate that multidimensional integration through TNQI provides a more comprehensive and interpretable representation of network quality than any individual KPI, establishing a reproducible analytical foundation for network monitoring, technology comparison, temporal degradation tracking, and future early warning applications.