## 5 Conclusions  
In this study, we developed and validated an integrated framework for both predicting and mitigating atmospheric duct interference (ADI) in TD-LTE networks--thereby directly addressing two critical research gaps identified in the literature: low prediction accuracy and limited mitigation efficiency. For the prediction component, we implemented and compared four machine learning algorithms trained on atmospheric and network-side features. The Random Forest model outperformed its peers, achieving a \(72.3\%\) accuracy rate--representing a marked improvement over previously reported benchmarks in ADI forecasting. By demonstrating that ensemble methods can robustly capture the complex relationships between meteorological variables and interference events, we have closed a key gap in reliable ADI prediction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Type** & **Best Accuracy** & **Best F1 Score** & **Lowest BER** & **Highest SNR** & **Best Ensemble** \\ \hline LSTM & 0.68 (M2 + M3) & 0.6 & 0.002 & \(-13.5\) dB & M2 + M3 \\ \hline CNN & 0.67 (M1) & 0.56 (M2 + M3) & 0.003 & \(-13.7\) dB & M2 + M3 \\ \hline GB & 0.66 (M2) & 0.36 (M2) & 0.002 & \(-13.5\) dB & M1 + M2 \\ \hline ODGB & 0.68 (M1) & 0.7 & 0.002 & \(-11.8\) dB & Mixed \\ \hline SGD & 0.69 (M1 + M2) & 0.34 & 0.002 & \(-13.2\) dB & M1 + M2 \\ \hline HGB & 0.70 (M2 + M3) & 0.70 (M3) & 0.002 & \(-10.3\) dB & M2 + M3 \\ \hline \hline \end{tabular}
\end{table} 
Table 31: The comparative analysis of the ADI mitigation models. However, to further improve predictive accuracy--particularly under highly variable conditions--future work could explore the integration of temporal modeling techniques such as attention mechanisms or transformer-based architectures, which may offer a more nuanced understanding of the sequential nature of atmospheric phenomena. On the mitigation side, we introduced a novel, prediction-driven strategy that dynamically configures and de-configures special TD-LTE subframes in real time. Six classification models informed these subframe adjustments, and the LSTM-based approach achieved the highest F1 score (0.60), while the CNN model delivered the highest signal quality, reaching an SNR of \(-13.7\) dB and a BER as low as 0.003. The HGB model further attained the highest classification accuracy (0.70) among all models. These results not only surpass the efficiency of earlier, static mitigation schemes but also highlight the power of combining deep learning with protocol-level adaptations--a hybrid solution that effectively bridges the divide between prediction and action. While our framework substantially advances the state of the art, we acknowledge that inter-cell and intra-cell interference measurements at the receiver remain unavailable. Incorporating these additional interference metrics into future model feature sets promises to further boost both prediction fidelity and mitigation precision. Overall, by elevating both predictive performance and mitigation effectiveness, this work lays a solid foundation for next-generation ADI management in wireless systems. The methodologies and promising results detailed here are directly applicable to ongoing research and can inform practical deployments within academic and telecommunications industry contexts. For future work, we recommend analyzing the performance of the ADI prediction and mitigation models using advanced software tools or APIs. Additionally, we aim to implement these models in hardware, utilizing FPGA or ASIC technologies to bring the framework closer to real-time, scalable deployment in operational networks. Conceptualization, U.J. and R.G.R. ; methodology, U.J. and R.M. ; software, R.M. ; validation, R.M., U.J. and R.G.R. ; formal analysis, R.M. ; investigation, R.M. and U.J. ; resources, U.J. and R.G.R. ; data curation, R.M. ; writing--original draft preparation, R.M. and U.J. ; writing--review and editing, U.J., G.M.L. and R.G.R. ; visualization, R.M. ; supervision, U.J. ; project administration, G.M.L. ; funding acquisition, G.M.L.