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Original Research
Received: 14 Apr 2024, Accepted: 12 Jun 2024,
 


A Convolutional Neural Network for Beam Prediction in 5G and Beyond Massive MIMO Systems

Abdullah Al-ahmadi.

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  • Abstract
    This paper presents an innovative Convolutional Neural Network (CNN) architecture designed for the prediction of beamforming vectors in Massive Multiple-Input Multiple-Output (MIMO) systems, a cornerstone technology in 5G and emerging wireless communication networks. As mmWave frequencies become central to modern MIMO systems, ensuring precise beam alignment amid susceptibility to blockages is imperative for maintaining robust communication links. Traditional beamforming algorithms, challenged by high complexity and sluggish adaptation to dynamic environmental conditions, are outperformed by the proposed CNN, which effectively captures complex channel patterns for accurate beam prediction. Leveraging a rich dataset from the DeepSense 6G scenarios, the study meticulously preprocesses the data through normalization and scaling before dividing it into training and testing subsets. The CNN, evaluated against real-world signal propagation behaviors, demonstrates a high prediction accuracy in beam indices, particularly in clear line-of-sight scenarios. The paper concludes with an assessment of the model's predictive performance, using Mean Absolute Error as a key metric, and proposes future enhancements to the model's architecture and training process. The implications of this work are significant, heralding a new era of intelligent, self-optimizing wireless networks that can adapt autonomously to environmental changes and user mobility

    Key words: Massive MIMO; CNN; Beamforming; 5G


     
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    How to Cite this Article
    Pubmed Style

    Abdullah Al-Ahmadi. A Convolutional Neural Network for Beam Prediction in 5G and Beyond Massive MIMO Systems. Journal of Engineering and Applied Sciences. 2024; 11(1): 141-151. doi:10.5455/jeas.2024010509


    Web Style

    Abdullah Al-Ahmadi. A Convolutional Neural Network for Beam Prediction in 5G and Beyond Massive MIMO Systems. https://jecasmu.org/?mno=197653 [Access: September 15, 2025]. doi:10.5455/jeas.2024010509


    AMA (American Medical Association) Style

    Abdullah Al-Ahmadi. A Convolutional Neural Network for Beam Prediction in 5G and Beyond Massive MIMO Systems. Journal of Engineering and Applied Sciences. 2024; 11(1): 141-151. doi:10.5455/jeas.2024010509



    Vancouver/ICMJE Style

    Abdullah Al-Ahmadi. A Convolutional Neural Network for Beam Prediction in 5G and Beyond Massive MIMO Systems. Journal of Engineering and Applied Sciences. (2024), [cited September 15, 2025]; 11(1): 141-151. doi:10.5455/jeas.2024010509



    Harvard Style

    Abdullah Al-Ahmadi (2024) A Convolutional Neural Network for Beam Prediction in 5G and Beyond Massive MIMO Systems. Journal of Engineering and Applied Sciences, 11 (1), 141-151. doi:10.5455/jeas.2024010509



    Turabian Style

    Abdullah Al-Ahmadi. 2024. A Convolutional Neural Network for Beam Prediction in 5G and Beyond Massive MIMO Systems. Journal of Engineering and Applied Sciences, 11 (1), 141-151. doi:10.5455/jeas.2024010509



    Chicago Style

    Abdullah Al-Ahmadi. "A Convolutional Neural Network for Beam Prediction in 5G and Beyond Massive MIMO Systems." Journal of Engineering and Applied Sciences 11 (2024), 141-151. doi:10.5455/jeas.2024010509



    MLA (The Modern Language Association) Style

    Abdullah Al-Ahmadi. "A Convolutional Neural Network for Beam Prediction in 5G and Beyond Massive MIMO Systems." Journal of Engineering and Applied Sciences 11.1 (2024), 141-151. Print. doi:10.5455/jeas.2024010509



    APA (American Psychological Association) Style

    Abdullah Al-Ahmadi (2024) A Convolutional Neural Network for Beam Prediction in 5G and Beyond Massive MIMO Systems. Journal of Engineering and Applied Sciences, 11 (1), 141-151. doi:10.5455/jeas.2024010509