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A Long Short-Term Memory Network-Based Radio Resource Management for 5G Network

Author

Listed:
  • Kavitha Rani Balmuri

    (Department of Information Technology, CMR Technical Campus, Hyderabad 501401, Telangana, India)

  • Srinivas Konda

    (Department of Computer Science and Engineering (DS), CMR Technical Campus, Hyderabad 501401, Telangana, India)

  • Wen-Cheng Lai

    (Department of Electronic Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan)

  • Parameshachari Bidare Divakarachari

    (Department of TCE, GSSS Institute of Engineering and Technology for Women, Mysuru 570016, Karnataka, India)

  • Kavitha Malali Vishveshwarappa Gowda

    (Department of Electronics and Communication Engineering, Gopalan College of Engineering and Management, Bengaluru 560048, Karnataka, India)

  • Hemalatha Kivudujogappa Lingappa

    (Department of ISE, Sri Krishna Institute of Technology, Bengaluru 560090, Karnataka, India)

Abstract

Nowadays, the Long-Term Evolution-Advanced system is widely used to provide 5G communication due to its improved network capacity and less delay during communication. The main issues in the 5G network are insufficient user resources and burst errors, because it creates losses in data transmission. In order to overcome this, an effective Radio Resource Management (RRM) is required to be developed in the 5G network. In this paper, the Long Short-Term Memory (LSTM) network is proposed to develop the radio resource management in the 5G network. The proposed LSTM-RRM is used for assigning an adequate power and bandwidth to the desired user equipment of the network. Moreover, the Grid Search Optimization (GSO) is used for identifying the optimal hyperparameter values for LSTM. In radio resource management, a request queue is used to avoid the unwanted resource allocation in the network. Moreover, the losses during transmission are minimized by using frequency interleaving and guard level insertion. The performance of the LSTM-RRM method has been analyzed in terms of throughput, outage percentage, dual connectivity, User Sum Rate (USR), Threshold Sum Rate (TSR), Outdoor Sum Rate (OSR), threshold guaranteed rate, indoor guaranteed rate, and outdoor guaranteed rate. The indoor guaranteed rate of LSTM-RRM for 1400 m of building distance improved up to 75.38% compared to the existing QOC-RRM.

Suggested Citation

  • Kavitha Rani Balmuri & Srinivas Konda & Wen-Cheng Lai & Parameshachari Bidare Divakarachari & Kavitha Malali Vishveshwarappa Gowda & Hemalatha Kivudujogappa Lingappa, 2022. "A Long Short-Term Memory Network-Based Radio Resource Management for 5G Network," Future Internet, MDPI, vol. 14(6), pages 1-20, June.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:6:p:184-:d:838080
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    References listed on IDEAS

    as
    1. Muyang Ge & Shen Zhou & Shijun Luo & Boping Tian, 2021. "3D Tensor-based Deep Learning Models for Predicting Option Price," Papers 2106.02916, arXiv.org, revised Sep 2021.
    2. Yanjing Zhu & Ruiqi Huang & Zhourui Wu & Simin Song & Liming Cheng & Rongrong Zhu, 2021. "Deep learning-based predictive identification of neural stem cell differentiation," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
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