Abstract:
Atmospheric Duct Interference (ADI) causes abnormal long-range radio propagation
due to refractivity inversions in the lower atmosphere, resulting in severe
uplink degradation in LTE-TDD cellular deployments. This research proposes a
data-driven framework to automatically detect, predict, and mitigate ADI while
preserving network coverage and uplink quality. The proposed framework was developed
using two heterogeneous data sources. Network-side measurement data
were obtained from Dialog Axiata PLC, collected from LTE-TDD deployments in
the Jaffna Peninsula, with approximately 100,000 time-indexed records from over
580 cells across 111 base stations, including uplink interference statistics, symbolwise
uplink power measurements (UL0-UL13), antenna configuration parameters,
and geographical coordinates. Environmental and atmospheric parameters were
obtained from the Copernicus Climate Data Store, including temperature, pressure,
and humidity profiles used to characterize ducting conditions. After preprocessing
and labeling, numerical and categorical features were standardized and
encoded using a Python-based pipeline. Class imbalance was addressed using
random undersampling, and the data were split into 70% training, 15% validation,
and 15% testing sets. Three supervised classifiers—Random Forest, Support
Vector Machine, and k-Nearest Neighbors—were trained and evaluated using accuracy,
confusion matrices, and cross-validation. Among the evaluated models,
the Random Forest classifier demonstrated the most stable performance, achieving
the highest test accuracy of 97.8% in distinguishing normal and interferenceaffected
conditions. Feature analysis confirmed that uplink interference levels,
symbol-wise power ramping behavior, and spatial indicators are dominant predictors.
Upon ADI detection, a coordinated mitigation strategy involving adaptive
antenna down-tilting, transmit power adjustments, and coverage compensation
from neighboring cells was applied using radio propagation modeling. Simulation
results demonstrate that over 70% of ADI-induced coverage loss can be recovered
without introducing additional spectrum usage. This research presents an original,
scalable solution for intelligent ADI management in LTE-TDD networks and
provides a practical foundation for future ADI-aware optimization.
