Machine Learning and Deep Learning Based Atmospheric Duct Interference Detection and Mitigation in TD-LTE Networks

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Muralitharan, R, Jayasinghe, U, Ragel, RG and Lee, GM (2025) Machine Learning and Deep Learning Based Atmospheric Duct Interference Detection and Mitigation in TD-LTE Networks. Future Internet, 17 (6).

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Abstract

The variations in the atmospheric refractivity in the lower atmosphere create a natural phenomenon known as atmospheric ducts. The atmospheric ducts allow the radio signals to travel large distances. This can adversely affect telecommunication systems, as cells with similar frequencies can interfere with each other due to frequency reuse, which is intended to optimize resource allocation. Thus, the downlink signals of one base station will travel a long distance via the atmospheric duct and interfere with the uplink signals of another base station. This scenario is known as atmospheric duct interference. The atmospheric duct interference (ADI) could be mitigated using digital signal processing, machine learning, and hybrid approaches. To address this challenge, we explore machine learning and deep learning techniques for ADI prediction and mitigation in Time Division Long Term Evolution (TD-LTE) networks. Our results show that the random forest algorithm achieves the highest prediction accuracy, while a convolutional neural network demonstrates the best mitigation performance with accuracy. Additionally, we propose optimizing special subframe configurations in TD-LTE networks using machine learning-based methods to effectively reduce ADI.

Item Type: Article
Uncontrolled Keywords: TD-LTE; ADI; Machine Learning; SVM; Random Forest; LSTM; CNN; 46 Information and computing sciences
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science and Mathematics
Publisher: MDPI
Date of acceptance: 21 May 2025
Date of first compliant Open Access: 22 May 2025
Date Deposited: 22 May 2025 15:27
Last Modified: 05 Jun 2025 16:45
DOI or ID number: 10.3390/fi17060237
URI: https://researchonline.ljmu.ac.uk/id/eprint/26400
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