## 1 Introduction  
Variations in atmospheric weather conditions in the low atmosphere cause changes in atmospheric refractivity. These changes create a phenomenon known as atmospheric ducts in the lower atmosphere. The atmospheric duct allows radio frequency signals to travel long distances. Mobile signals could travel through atmospheric ducts and reach large propagation distances. The mobile operators use a frequency reuse pattern among the cells to increase the spectral efficiency of the mobile networks. Atmospheric duct interference (ADI) occurs when downlink mobile signals from one base station propagate over long distances through atmospheric ducts and disrupt the uplink mobile signals of another base station with the same frequency [1, 2]. The formation of the atmospheric duct depends on the weather conditions, such as atmospheric temperature, atmospheric pressure, and atmospheric humidity [3]. The length of the atmospheric duct will vary from 100 km to 400 km, depending on the atmospheric conditions. We can classify the atmospheric duct into three classes, surface duct, elevated duct, and evaporation duct, based on the characteristics of the atmospheric ducts in the lower atmosphere [4, 5]. Mitigating ADI is crucial for enhancing the quality of service (QoS) in mobile networks and maintaining reliable service level agreements. While traditional signal processing techniques have been employed, machine learning and hybrid approaches offer more efficient solutions. This study explores the relationship between uplink power, weather conditions, and uplink interference in physical resource blocks (PRBs) within 4G Long-Term Evolution (LTE) networks. By analyzing samples of network data and corresponding weather data, we demonstrate how machine learning models can effectively detect ADI and help optimize guard period adjustments. However, modifying the guard period may lead to frequency overlap with adjacent base stations, making precise synchronization of uplink and downlink signals essential for effective ADI mitigation [2; 6]. Our research focuses on developing a machine learning-based ADI prediction and mitigation system for Time-Division Long-Term Evolution (TD-LTE) networks. The proposed models incorporate both atmospheric and network-related features to enhance accuracy. Specifically, we utilize three atmospheric parameters--temperature, humidity, and pressure--alongside fifteen network-side features obtained from the mobile operator. All features are normalized between 0 and 1 for consistency. Atmospheric data are sourced from the Visual Crossing weather monitoring platform, while network data are collected from Dialog Axiata PLC in Sri Lanka [7]. The dataset spans two years, covering 2021 to 2023, with 56,000 entries collected from the Jaffna district. For ADI prediction, we implement four machine learning algorithms: Support Vector Machine (SVM), Random Forest, Long Short-Term Memory (LSTM), and a convolutional neural network (CNN). Among these, the Random Forest model achieves the highest test accuracy of 72.3%. For ADI mitigation, we employ five classifiers: Stochastic Gradient Descent, Gradient Boosting, Optimized Distributed Gradient Boosting, LSTM, and a CNN, with the CNN delivering the best performance at 75% accuracy. In TD-LTE networks, the time interval between uplink and downlink frames is managed through special subframes, consisting of an uplink pilot time slot, a downlink pilot time slot, and a guard period. Our mitigation strategy dynamically configures the guard period based on machine learning predictions to minimize ADI while ensuring seamless network synchronization [8; 9]. We are unable to collect the inter-cell and intra-cell interference values at the receiver side. If we consider the values in the features of the models, then we can improve the performance of the models. This paper is structured into seven sections. The first section provides an introduction to the research, outlining its objectives and significance.