A Study on the Effect of Phase Shifter Quantization Error on the Spectral Efficiency Using Neural Network

Reza Ghazalian; Sahar Golipoor

doi:10.1109/GPECOM55404.2022.9815775

A Study on the Effect of Phase Shifter Quantization Error on the Spectral Efficiency Using Neural Network

Reza Ghazalian^*, Sahar Golipoor

^*Tämän työn vastaava kirjoittaja

Communication Engineering

Babol Noshirvani University of Technology

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

68 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	Proceedings - 2022 IEEE 4th Global Power, Energy and Communication Conference, GPECOM 2022
Kustantaja	IEEE
Sivut	626-631
Sivumäärä	6
ISBN (elektroninen)	978-1-6654-6925-8
DOI - pysyväislinkit	https://doi.org/10.1109/GPECOM55404.2022.9815775
Tila	Julkaistu - 11 heinäk. 2022
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE Global Power, Energy and Communication Conference - Cappadocia, Turkki Kesto: 14 kesäk. 2022 → 17 kesäk. 2022

Julkaisusarja

Nimi	Proceedings - IEEE Global Power, Energy and Communication Conference

Conference

Conference	IEEE Global Power, Energy and Communication Conference
Lyhennettä	GPECOM
Maa/Alue	Turkki
Kaupunki	Cappadocia
Ajanjakso	14/06/2022 → 17/06/2022

YK:n kestävän kehityksen tavoitteet

Tämä tuotos edistää seuraavia kestävän kehityksen tavoitteita:

Pääsy asiakirjaan

10.1109/GPECOM55404.2022.9815775

Ghazalian_paperAccepted author manuscript, 1,51 MB

Muut tiedostot ja linkit

Linkki julkaisuun Scopuksessa

ULTRA: Uusia menetelmiä tehokkaaseen tiedonsiirtoon, paikannukseen ja ympäristön havainnointiin autonomisissa järjestelmissä
Jäntti, R. (Vastuullinen tutkija)
01/01/2020 → 31/12/2022
Projekti: Academy of Finland: Other research funding

Siteeraa tätä

Ghazalian, R., & Golipoor, S. (2022). A Study on the Effect of Phase Shifter Quantization Error on the Spectral Efficiency Using Neural Network. teoksessa Proceedings - 2022 IEEE 4th Global Power, Energy and Communication Conference, GPECOM 2022 (Sivut 626-631). (Proceedings - IEEE Global Power, Energy and Communication Conference). IEEE. https://doi.org/10.1109/GPECOM55404.2022.9815775

@inproceedings{d635d0acd2944340970d22764363cf53,

title = "A Study on the Effect of Phase Shifter Quantization Error on the Spectral Efficiency Using Neural Network",

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.",

keywords = "Neural Networks, Phase shifter resolution bits, Spectral efficiency loss model",

author = "Reza Ghazalian and Sahar Golipoor",

note = "Funding Information: ACKNOWLEDGMENT This work has been funded in part by Academy of Finland ULTRA (n:o 328215) project. Publisher Copyright: {\textcopyright} 2022 IEEE.; IEEE Global Power, Energy and Communication Conference, GPECOM ; Conference date: 14-06-2022 Through 17-06-2022",

year = "2022",

month = jul,

day = "11",

doi = "10.1109/GPECOM55404.2022.9815775",

language = "English",

series = "Proceedings - IEEE Global Power, Energy and Communication Conference",

publisher = "IEEE",

pages = "626--631",

booktitle = "Proceedings - 2022 IEEE 4th Global Power, Energy and Communication Conference, GPECOM 2022",

address = "United States",

}

Ghazalian, R & Golipoor, S 2022, A Study on the Effect of Phase Shifter Quantization Error on the Spectral Efficiency Using Neural Network. julkaisussa Proceedings - 2022 IEEE 4th Global Power, Energy and Communication Conference, GPECOM 2022. Proceedings - IEEE Global Power, Energy and Communication Conference, IEEE, Sivut 626-631, IEEE Global Power, Energy and Communication Conference, Cappadocia, Turkki, 14/06/2022. https://doi.org/10.1109/GPECOM55404.2022.9815775

A Study on the Effect of Phase Shifter Quantization Error on the Spectral Efficiency Using Neural Network. / Ghazalian, Reza; Golipoor, Sahar.
Proceedings - 2022 IEEE 4th Global Power, Energy and Communication Conference, GPECOM 2022. IEEE, 2022. s. 626-631 (Proceedings - IEEE Global Power, Energy and Communication Conference).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - A Study on the Effect of Phase Shifter Quantization Error on the Spectral Efficiency Using Neural Network

AU - Ghazalian, Reza

AU - Golipoor, Sahar

PY - 2022/7/11

Y1 - 2022/7/11

N2 - 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.

AB - 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.

KW - Neural Networks

KW - Phase shifter resolution bits

KW - Spectral efficiency loss model

UR - http://www.scopus.com/inward/record.url?scp=85134880130&partnerID=8YFLogxK

U2 - 10.1109/GPECOM55404.2022.9815775

DO - 10.1109/GPECOM55404.2022.9815775

M3 - Conference article in proceedings

AN - SCOPUS:85134880130

T3 - Proceedings - IEEE Global Power, Energy and Communication Conference

SP - 626

EP - 631

BT - Proceedings - 2022 IEEE 4th Global Power, Energy and Communication Conference, GPECOM 2022

PB - IEEE

T2 - IEEE Global Power, Energy and Communication Conference

Y2 - 14 June 2022 through 17 June 2022

ER -

A Study on the Effect of Phase Shifter Quantization Error on the Spectral Efficiency Using Neural Network

Abstrakti

Julkaisusarja

Conference

YK:n kestävän kehityksen tavoitteet

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Projektit

ULTRA: Uusia menetelmiä tehokkaaseen tiedonsiirtoon, paikannukseen ja ympäristön havainnointiin autonomisissa järjestelmissä

Siteeraa tätä