Machine Learning and Deep Learning-Based Atmospheric Duct Interference Detection and Mitigation in TD-LTE Networks
The variations in the atmospheric refractivity in the lower atmosphere create a natural phenomenon known as atmospheric ducts. The atmospheric ducts allow radio signals to travel long distances. This can adversely affect telecommunication systems, as cells with similar frequencies can interfere with...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/17/6/237 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849432667404107776 |
|---|---|
| author | Rasendram Muralitharan Upul Jayasinghe Roshan G. Ragel Gyu Myoung Lee |
| author_facet | Rasendram Muralitharan Upul Jayasinghe Roshan G. Ragel Gyu Myoung Lee |
| author_sort | Rasendram Muralitharan |
| collection | DOAJ |
| description | The variations in the atmospheric refractivity in the lower atmosphere create a natural phenomenon known as atmospheric ducts. The atmospheric ducts allow radio signals to travel long 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 (ADI). 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. |
| format | Article |
| id | doaj-art-915c2ec120af4f6e8ad22245f53dd31f |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-915c2ec120af4f6e8ad22245f53dd31f2025-08-20T03:27:18ZengMDPI AGFuture Internet1999-59032025-05-0117623710.3390/fi17060237Machine Learning and Deep Learning-Based Atmospheric Duct Interference Detection and Mitigation in TD-LTE NetworksRasendram Muralitharan0Upul Jayasinghe1Roshan G. Ragel2Gyu Myoung Lee3Department of Computer Engineering, Faculty of Engineering, University of Peradeniya, Peradeniya 20400, Sri LankaDepartment of Computer Engineering, Faculty of Engineering, University of Peradeniya, Peradeniya 20400, Sri LankaDepartment of Computer Engineering, Faculty of Engineering, University of Peradeniya, Peradeniya 20400, Sri LankaFaculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UKThe variations in the atmospheric refractivity in the lower atmosphere create a natural phenomenon known as atmospheric ducts. The atmospheric ducts allow radio signals to travel long 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 (ADI). 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.https://www.mdpi.com/1999-5903/17/6/237TD-LTEADImachine learningSVMrandom forestLSTM |
| spellingShingle | Rasendram Muralitharan Upul Jayasinghe Roshan G. Ragel Gyu Myoung Lee Machine Learning and Deep Learning-Based Atmospheric Duct Interference Detection and Mitigation in TD-LTE Networks Future Internet TD-LTE ADI machine learning SVM random forest LSTM |
| title | Machine Learning and Deep Learning-Based Atmospheric Duct Interference Detection and Mitigation in TD-LTE Networks |
| title_full | Machine Learning and Deep Learning-Based Atmospheric Duct Interference Detection and Mitigation in TD-LTE Networks |
| title_fullStr | Machine Learning and Deep Learning-Based Atmospheric Duct Interference Detection and Mitigation in TD-LTE Networks |
| title_full_unstemmed | Machine Learning and Deep Learning-Based Atmospheric Duct Interference Detection and Mitigation in TD-LTE Networks |
| title_short | Machine Learning and Deep Learning-Based Atmospheric Duct Interference Detection and Mitigation in TD-LTE Networks |
| title_sort | machine learning and deep learning based atmospheric duct interference detection and mitigation in td lte networks |
| topic | TD-LTE ADI machine learning SVM random forest LSTM |
| url | https://www.mdpi.com/1999-5903/17/6/237 |
| work_keys_str_mv | AT rasendrammuralitharan machinelearninganddeeplearningbasedatmosphericductinterferencedetectionandmitigationintdltenetworks AT upuljayasinghe machinelearninganddeeplearningbasedatmosphericductinterferencedetectionandmitigationintdltenetworks AT roshangragel machinelearninganddeeplearningbasedatmosphericductinterferencedetectionandmitigationintdltenetworks AT gyumyounglee machinelearninganddeeplearningbasedatmosphericductinterferencedetectionandmitigationintdltenetworks |