University of Oulu

H. Zarini, M. R. Mili, M. Rasti, P. H. J. Nardelli and M. Bennis, "Xavier-Enabled Extreme Reservoir Machine for Millimeter-Wave Beamspace Channel Tracking," 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, TX, USA, 2022, pp. 1683-1688, doi: 10.1109/WCNC51071.2022.9771836.

Xavier-enabled extreme reservoir machine for millimeter-wave beamspace channel tracking

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Author: Zarini, Hosein1; Mili, Mohammad Robat2; Rasti, Mehdi1,3;
Organizations: 1Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran
2Electronics Research Institute, Sharif University of Technology, Tehran, Iran
3Lappeenranta-Lahti University of Technology, Lappeenranta, Finland
4University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.9 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-01-13


In this paper, we propose an accurate two-phase millimeter-Wave (mmWave) beamspace channel tracking mechanism. Particularly in the first phase, we train an extreme reservoir machine (ERM) for tracking the historical features of the mmWave beamspace channel and predicting them in upcoming time steps. Towards a more accurate prediction, we further fine-tune the ERM by means of Xavier initializer technique, whereby the input weights in ERM are initially derived from a zero mean and finite variance Gaussian distribution, leading to 49% degradation in prediction variance of the conventional ERM. The proposed method numerically improves the achievable spectral efficiency (SE) of the existing counterparts, by 13%, when signal-to-noise-ratio (SNR) is 15dB. We further investigate an ensemble learning technique in the second phase by sequentially incorporating multiple ERMs to form an ensembled model, namely adaptive boosting (AdaBoost), which further reduces the prediction variance in conventional ERM by 56%, and concludes in 21% enhancement of achievable SE upon the existing schemes at SNR = 15dB.

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Series: IEEE Wireless Communications and Networking Conference
ISSN: 1525-3511
ISSN-E: 1558-2612
ISSN-L: 1525-3511
Pages: 1683 - 1688
DOI: 10.1109/wcnc51071.2022.9771836
Host publication: 2022 IEEE Wireless Communications and Networking Conference (WCNC)
Conference: IEEE Wireless Communications and Networking Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work is supported by the Academy of Finland: (a) ee-IoT n.319009, (b) EnergyNet n.321265/n.328869, and (c) FIREMAN n.326270/CHISTERA-17-BDSI-003; and by JAES Foundation via STREAM project.
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