• Journal of Internet Computing and Services
    ISSN 2287 - 1136(Online) / ISSN 1598 - 0170 (Print)
    http://jics.or.kr/

Deep Learning-Based Prediction of the Quality of Multiple Concurrent Beams in mmWave Band


Jun-Hyeok Choi, Mun-Suk Kim, Journal of Internet Computing and Services, Vol. 23, No. 3, pp. 13-20, Jun. 2022
10.7472/jksii.2022.23.3.13, Full Text:
Keywords: mmWave, 802.11ay, beamforming, MU-MIMO, Deep Learning

Abstract

IEEE 802.11ay Wi-Fi is the next generation wireless technology and operates in mmWave band. It supports the MU-MIMO (Multiple User Multiple Input Multiple Output) transmission in which an AP (Access Point) can transmit multiple data streams simultaneously to multiple STAs (Stations). To this end, the AP should perform MU-MIMO beamforming training with the STAs. For efficient MU-MIMO beamforming training, it is important for the AP to estimate signal strength measured at each STA at which multiple beams are used simultaneously. Therefore, in the paper, we propose a deep learning-based link quality estimation scheme. Our proposed scheme estimates the signal strength with high accuracy by utilizing a deep learning model pre-trained for a certain indoor or outdoor propagation scenario. Specifically, to estimate the signal strength of the multiple concurrent beams, our scheme uses the signal strengths of the respective single beams, which can be obtained without additional signaling overhead, as the input of the deep learning model. For performance evaluation, we utilized a Q-D (Quasi-Deterministic) Channel Realization open source software and extensive channel measurement campaigns were conducted with NIST (National Institute of Standards and Technology) to implement the millimeter wave (mmWave) channel. Our simulation results demonstrate that our proposed scheme outperforms comparison schemes in terms of the accuracy of the signal strength estimation.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[APA Style]
Jun-Hyeok Choi and Mun-Suk Kim (2022). Deep Learning-Based Prediction of the Quality of Multiple Concurrent Beams in mmWave Band. Journal of Internet Computing and Services, 23(3), 13-20. DOI: 10.7472/jksii.2022.23.3.13.

[IEEE Style]
J. Choi and M. Kim, "Deep Learning-Based Prediction of the Quality of Multiple Concurrent Beams in mmWave Band," Journal of Internet Computing and Services, vol. 23, no. 3, pp. 13-20, 2022. DOI: 10.7472/jksii.2022.23.3.13.

[ACM Style]
Jun-Hyeok Choi and Mun-Suk Kim. 2022. Deep Learning-Based Prediction of the Quality of Multiple Concurrent Beams in mmWave Band. Journal of Internet Computing and Services, 23, 3, (2022), 13-20. DOI: 10.7472/jksii.2022.23.3.13.