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Deep Neural Network for Beam and Blockage Prediction in 3GPP-Based Indoor Hotspot Environments

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Abstract

The application of millimeter-wave (mm-wave) frequencies in communication has the potential to address the ever-growing data traffic requirements of next-generation wireless communication devices. However, owing to their high directivity and high penetration loss, directional mm-wave beams are vulnerable to blockages caused by users’ bodies and ambient obstacles. Further development of indoor mm-wave communication is essential, as the majority of data traffic is generated in indoor environments. In previous studies, the mm-wave blockage problem was primarily considered in outdoor scenarios, whereas in the present study, online learning-based beam and blockage prediction in an indoor hotspot (InH) scenario was investigated. During an offline training phase, we constructed a fingerprinting database consisting of user locations along with their respective data traffic demands and corresponding blockage statuses with optimal beam indices. Following its creation, the fingerprinting database was used to train the weights and bias of a properly designed deep neural network (DNN). During a subsequent online learning phase, the trained DNN was fed user locations and corresponding data traffic demands at the served user equipment to output optimal beam indices and blockage statuses. System-level simulations were conducted to assess the accuracy of blockage prediction based on 3GPP’s new radio channel and blockage models in InH environments. Simulation results revealed that the proposed scheme was capable of predicting mm-wave blockages with an accuracy of > 90%. These results confirmed the viability of the proposed DNN model for predicting optimal mm-wave beams and spectral efficiencies.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT: Ministry of Science and ICT) (2020R1I1A1A01073948 and 2021R1A2C1005058). This research was supported by the BK21 FOUR Program (Fostering Outstanding Universities for Research, 5199991714138) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF).

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Author notes

  1. Sangmi Moon, Hyeonsung Kim, Young-Hwan You and Cheol Hong Kim have contributed equally to this work.

Authors and Affiliations

  1. Department of IT Artificial Intelligence, Korea Nazarene University, 48, Wolbong-ro, Seobuk-gu, Cheonan, 31172, Republic of Korea

    Sangmi Moon

  2. Department of Electronic Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju, 61186, Republic of Korea

    Hyeonsung Kim & Intae Hwang

  3. Department of Computer Engineering, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul, 05006, Republic of Korea

    Young-Hwan You

  4. School of Computer Science and Engineering, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978, Republic of Korea

    Cheol Hong Kim

  5. Department of Electronic Engineering & ICT Convergence System Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju, 61186, Republic of Korea

    Intae Hwang

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  1. Sangmi Moon
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Correspondence to Intae Hwang.

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Moon, S., Kim, H., You, YH. et al. Deep Neural Network for Beam and Blockage Prediction in 3GPP-Based Indoor Hotspot Environments. Wireless Pers Commun 124, 3287–3306 (2022). https://doi.org/10.1007/s11277-022-09513-4

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