Energy-efficient power allocation in cell-free massive MIMO with zero-forcing: First order methods

Trang C. Mai; Hien Quoc Ngo; Le-Nam  Tran

doi:10.1016/j.phycom.2021.101540

Energy-efficient power allocation in cell-free massive MIMO with zero-forcing: First order methods

Trang C. Mai^*, Hien Quoc Ngo, Le-Nam Tran

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

In this paper, the downlink of cell-free massive multiple-input multiple-output (MIMO) with zero-forcing processing is considered. To maximize the system energy efficiency (EE), we design power allocation algorithms taking into account imperfect channel state information, hardware, and backhaul power consumption. The total EE optimization problem is nonconvex, which traditionally is solved by the successive convex approximation framework which involves second order cone programs (SOCPs). As such methods have high complexity, the run time is extremely long, especially in large-scale systems with thousands of access points (APs) and users. To overcome this problem, in this paper, we propose to apply two computationally efficient methods, namely proximal gradient (PG) method and accelerated proximal gradient (APG) method to solve the considered problem. Numerical results show that, compared to the conventional SOCPs approximation methods, our proposed methods achieve the same performance while the run time is much smaller.

Original language	English
Article number	101540
Journal	Physical Communication
Volume	51
Early online date	29 Dec 2021
DOIs	https://doi.org/10.1016/j.phycom.2021.101540
Publication status	Published - Apr 2022

Bibliographical note

Funding Information:
Hien Quoc Ngo received the B.S. degree in electrical engineering from the Ho Chi Minh City University of Technology, Vietnam, in 2007, the M.S. degree in electronics and radio engineering from Kyung Hee University, South Korea, in 2010, and the Ph.D. degree in communication systems from Linköping University (LiU), Sweden, in 2015. In 2014, he visited the Nokia Bell Labs, Murray Hill, New Jersey, USA. From January 2016 to April 2017, Hien Quoc Ngo was a VR researcher at the Department of Electrical Engineering (ISY), LiU. He was also a Visiting Research Fellow at the School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, UK, funded by the Swedish Research Council.

Funding Information:
Parts of this work were presented at the 2020 ICCE Conference Mai et al. (2021) [1] .The work of Trang C. Mai and Hien Quoc Ngo was supported by the UK Research and Innovation Future Leaders Fellowships under Grant MR/S017666/1 . The work of Le-Nam Tran was supported in part by a Grant from Science Foundation Ireland under Grant number 17/CDA/4786 .

Publisher Copyright:
© 2021 Elsevier B.V.

Keywords

Accelerated proximal gradient
Cell-free massive MIMO
Energy efficiency
First order
Proximal gradient

ASJC Scopus subject areas

Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

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title = "Energy-efficient power allocation in cell-free massive MIMO with zero-forcing: First order methods",

abstract = "In this paper, the downlink of cell-free massive multiple-input multiple-output (MIMO) with zero-forcing processing is considered. To maximize the system energy efficiency (EE), we design power allocation algorithms taking into account imperfect channel state information, hardware, and backhaul power consumption. The total EE optimization problem is nonconvex, which traditionally is solved by the successive convex approximation framework which involves second order cone programs (SOCPs). As such methods have high complexity, the run time is extremely long, especially in large-scale systems with thousands of access points (APs) and users. To overcome this problem, in this paper, we propose to apply two computationally efficient methods, namely proximal gradient (PG) method and accelerated proximal gradient (APG) method to solve the considered problem. Numerical results show that, compared to the conventional SOCPs approximation methods, our proposed methods achieve the same performance while the run time is much smaller.",

keywords = "Accelerated proximal gradient, Cell-free massive MIMO, Energy efficiency, First order, Proximal gradient",

author = "Mai, {Trang C.} and Ngo, {Hien Quoc} and Le-Nam Tran",

note = "Funding Information: Hien Quoc Ngo received the B.S. degree in electrical engineering from the Ho Chi Minh City University of Technology, Vietnam, in 2007, the M.S. degree in electronics and radio engineering from Kyung Hee University, South Korea, in 2010, and the Ph.D. degree in communication systems from Link{\"o}ping University (LiU), Sweden, in 2015. In 2014, he visited the Nokia Bell Labs, Murray Hill, New Jersey, USA. From January 2016 to April 2017, Hien Quoc Ngo was a VR researcher at the Department of Electrical Engineering (ISY), LiU. He was also a Visiting Research Fellow at the School of Electronics, Electrical Engineering and Computer Science, Queen{\textquoteright}s University Belfast, UK, funded by the Swedish Research Council. Funding Information: Parts of this work were presented at the 2020 ICCE Conference Mai et al. (2021) [1] .The work of Trang C. Mai and Hien Quoc Ngo was supported by the UK Research and Innovation Future Leaders Fellowships under Grant MR/S017666/1 . The work of Le-Nam Tran was supported in part by a Grant from Science Foundation Ireland under Grant number 17/CDA/4786 . Publisher Copyright: {\textcopyright} 2021 Elsevier B.V.",

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language = "English",

volume = "51",

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N2 - In this paper, the downlink of cell-free massive multiple-input multiple-output (MIMO) with zero-forcing processing is considered. To maximize the system energy efficiency (EE), we design power allocation algorithms taking into account imperfect channel state information, hardware, and backhaul power consumption. The total EE optimization problem is nonconvex, which traditionally is solved by the successive convex approximation framework which involves second order cone programs (SOCPs). As such methods have high complexity, the run time is extremely long, especially in large-scale systems with thousands of access points (APs) and users. To overcome this problem, in this paper, we propose to apply two computationally efficient methods, namely proximal gradient (PG) method and accelerated proximal gradient (APG) method to solve the considered problem. Numerical results show that, compared to the conventional SOCPs approximation methods, our proposed methods achieve the same performance while the run time is much smaller.

AB - In this paper, the downlink of cell-free massive multiple-input multiple-output (MIMO) with zero-forcing processing is considered. To maximize the system energy efficiency (EE), we design power allocation algorithms taking into account imperfect channel state information, hardware, and backhaul power consumption. The total EE optimization problem is nonconvex, which traditionally is solved by the successive convex approximation framework which involves second order cone programs (SOCPs). As such methods have high complexity, the run time is extremely long, especially in large-scale systems with thousands of access points (APs) and users. To overcome this problem, in this paper, we propose to apply two computationally efficient methods, namely proximal gradient (PG) method and accelerated proximal gradient (APG) method to solve the considered problem. Numerical results show that, compared to the conventional SOCPs approximation methods, our proposed methods achieve the same performance while the run time is much smaller.

KW - Accelerated proximal gradient

KW - Cell-free massive MIMO

KW - Energy efficiency

KW - First order

KW - Proximal gradient

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DO - 10.1016/j.phycom.2021.101540

M3 - Article

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VL - 51

JO - Physical Communication

JF - Physical Communication

SN - 1874-4907

M1 - 101540

ER -

Energy-efficient power allocation in cell-free massive MIMO with zero-forcing: First order methods

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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