Adaptive and Sustainable Deep Learning Approach for mm-Wave Window Frequency Prediction in Diverse Atmospheric Conditions

Introduction: Millimetre-wave (mm-Wave) communication, spanning 30-300 GHz, suffers from strong atmospheric attenuation due to water vapour and oxygen absorption. These effects are highly variable, influenced by temperature and humidity, which determine the location of atmospheric “window frequencies (fwindows)”, regions of reduced attenuation essential for high-capacity wireless links. Methodology: This study examines the seasonal and geographical variability of these windows across 14 Pacific tropical countries during July–August and January–February, using a refined Millimetre Wave Propagation Model (MPM) up to 200 GHz. Three deep learning models, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) were tested to predict window frequencies at 30 GHz, 94 GHz, and 140 GHz from temperature and water vapour density data. Results: Results show that the RNN consistently achieved the highest accuracy (R² > 0.98, RMSE < 0.015 dB/km), outperforming CNN and LSTM. While CNN proved competitive in cooler conditions, LSTM exhibited greater sensitivity to seasonal shifts. The findings highlight that atmospheric windows are dynamic rather than static, with significant seasonal and spatial variations. Discussion: The study confirms that window frequencies are not fixed but dynamically modulated by climatic parameters, especially in humid tropical zones. The RNN model’s superior performance is attributed to its ability to capture temporal dependencies in meteorological inputs. Larger training sets (80–20 split) enhanced generalization and reduced prediction errors across all models. Conclusion: The proposed RNN-based framework offers a robust, climate-adaptive solution for real-time spectrum allocation, supporting energy efficiency, resilience, and sustainability in future mm-Wave communication networks.