A Study on the Effect of Phase Shifter Quantization Error on the Spectral Efficiency Using Neural Network
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A4 Artikkeli konferenssijulkaisussa
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Date
2022-07-11
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Language
en
Pages
6
626-631
626-631
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Proceedings - 2022 IEEE 4th Global Power, Energy and Communication Conference, GPECOM 2022
Abstract
Beamforming (BF) is the inevitable component of the recent communication systems, especially Millimeter wave (mmWave) systems. Thanks to the radio frequency (RF) and digital technologies, BF techniques are implemented in the both digital and analogue domains by using phase shifters (PS) networks. Adopting the digital PS, which has the finite resolution bits, leads to loss in the spectral efficiency (SE). Accordingly, in this paper, we extract the SE loss in a multi-user multiple inputs single output (MISO) system, which would be useful for practical prospective. To this end, we apply machine learning (ML) to extract the SE loss. Simulation results show that the extracted models have the desirable accuracy in the SE loss prediction.Description
Funding Information: ACKNOWLEDGMENT This work has been funded in part by Academy of Finland ULTRA (n:o 328215) project. Publisher Copyright: © 2022 IEEE.
Keywords
Neural Networks, Phase shifter resolution bits, Spectral efficiency loss model
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Citation
Ghazalian, R & Golipoor, S 2022, A Study on the Effect of Phase Shifter Quantization Error on the Spectral Efficiency Using Neural Network . in Proceedings - 2022 IEEE 4th Global Power, Energy and Communication Conference, GPECOM 2022 . Proceedings - IEEE Global Power, Energy and Communication Conference, IEEE, pp. 626-631, IEEE Global Power, Energy and Communication Conference, Cappadocia, Türkiye, 14/06/2022 . https://doi.org/10.1109/GPECOM55404.2022.9815775
