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A machine learning framework for predicting downlink throughput in 4G-LTE/5G cellular networks

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Abstract

The current and next generations of cellular networks produce a massive amount of data. With this vast parameter increase, cellular communication networks have grown incredibly complicated. In addition, these cellular networks are unmanaged with conventional techniques, and a more advanced design and optimization methodology that depends on Machine Learning (ML) models is necessary. This work proposes a framework model for predicting downlink throughput (DL-Throughput) using ML models in fourth and fifth generations (4G/5G) cellular networks. The important parameters are selected from data measurements based on the correlation coefficient. The critical and effective parameters such as Reference Signal Received Power (RSRP), Signal to Interference and Noise Ratio(SINR), Received Signal Strength Indicator (RSSI), and Reference Signal Receive Quality (RSRQ) have been applied for the training model to predict the DL-Throughput in cellular networks. The prediction accuracy of the determination coefficient ranges between 89% and 96% from three different operators.

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Data availability

The data supporting the findings of this study are not currently available due to future work. We anticipate making the data openly accessible after finishing our works. In the meantime, for any inquiries regarding the data or requests for access, please contact corresponding author.

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Authors and Affiliations

  1. Al-Muthanna University, Samawah, Iraq

    Abbas Al-Thaedan & Zaenab Shakir

  2. Information Technology Research and Development Center (ITRDC), University of Kufa, Najaf, Iraq

    Ahmed Yaseen Mjhool

  3. Information Technology Department, College of Information Technology and Computer Sciences, University of Kerbala, Kerbala, Iraq

    Ruaa Alsabah

  4. Information Technology Department, Al-Taff University College, Kerbala, Iraq

    Ali Al-Sabbagh

  5. Ministry of Communication, ITPC, Kerbala, Iraq

    Ali Al-Sabbagh

  6. L3Harris Institute for Assured Information, Florida Institute of Technology, Melbourne, FL, USA

    Fitzroy Nembhard

  7. TeleWorld Solutions, Chantilly, VA, USA

    Monera Salah

Authors
  1. Abbas Al-Thaedan
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  2. Zaenab Shakir
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  3. Ahmed Yaseen Mjhool
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  4. Ruaa Alsabah
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  5. Ali Al-Sabbagh
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  6. Fitzroy Nembhard
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  7. Monera Salah
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Correspondence to Abbas Al-Thaedan.

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Al-Thaedan, A., Shakir, Z., Mjhool, A.Y. et al. A machine learning framework for predicting downlink throughput in 4G-LTE/5G cellular networks. Int. j. inf. tecnol. 16, 651–657 (2024). https://doi.org/10.1007/s41870-023-01678-w

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  • DOI: https://doi.org/10.1007/s41870-023-01678-w

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