Uncovering Temporal Regimes and Spatial Temporal Patterns in Antenna Structure Registrations
Hsing-Cheng Liu, Yao-Liang Chung
Progress in Signals and Telecommunication Engineering, Vol. 15, No. 2, 2026.
DOI: https://doi.org/10.6025/pste/2026/15/2/64-87
Link: https://www.dline.info/pste/fulltext/v15n2/pstev15n2_2.pdf
Abstract:
The rapid expansion of wireless communication networks has necessitated a massive deployment of antenna
infrastructure, yet the longitudinal and spatial temporal dynamics of antenna structure registrations remain
poorly understood. This study addresses this gap by analyzing 124,811 Antenna Structure Registration
(ASR) records in the United States from 1996 to 2012. Moving beyond simple aggregate counts, we employ
an integrated analytical framework combining Seasonal Trend decomposition using Loess (STL), nonparametric
trend testing (Mann Kendall, Sen's slope), multiple structural break detection algorithms (PELT,
Bai Perron, Joinpoint, Bayesian), Markov switching regime analysis, and state level spatial temporal modeling.
Results show a strong, statistically significant monotonic upward trend (+3.66 registrations/month) with
weak seasonality. Crucially, the registration process is highly non-stationary, characterized by multiple
structural breaks converging around 2011-2012 and two persistent latent regimes (low activity and high
activity) with transition persistences exceeding 94%. We also identified 33 temporal anomalies, mostly
clustered in the later observation period, indicating episodic acceleration rather than smooth growth.
Conventional SARIMA forecasting demonstrated significant limitations (MAPE = 76.8%) in capturing this
structural instability. Spatially, infrastructure deployment is highly concentrated, with Texas, California,
and Florida accounting for over 20% of all registrations, though late period expansion was broadly distributed
across leading states. Ultimately, this study demonstrates that antenna infrastructure evolution is a complex,
regime dependent, and geographically heterogeneous process, establishing a foundational framework for
future spatial temporal and regime aware infrastructure modeling.