Max-Min Fair Energy-Efficient Beamforming Design for Intelligent Reflecting Surface-Aided SWIPT Systems With Non-Linear Energy Harvesting Model

This paper considers an intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) network, where multiple users decode data and harvest energy from the transmitted signal of a transmitter. The proposed design framework exploits the cost-effective IRS to...

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Vydáno v:	IEEE transactions on vehicular technology Ročník 70; číslo 6; s. 5848 - 5864
Hlavní autoři:	Zargari, Shayan, Khalili, Ata, Wu, Qingqing, Robat Mili, Mohammad, Ng, Derrick Wing Kwan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.06.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Array signal processing Beamforming Computational geometry Convex analysis Convexity Energy conversion efficiency Energy efficiency Energy efficiency (EE) Energy harvesting intelligent reflecting surface (IRS) Interference Mathematical programming Nonlinear systems Optimization Power transfer Programming Receivers Reconfigurable intelligent surfaces Signal to noise ratio simultaneous wireless information and power transfer (SWIPT) Wireless communication
ISSN:	0018-9545, 1939-9359
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Abstract	This paper considers an intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) network, where multiple users decode data and harvest energy from the transmitted signal of a transmitter. The proposed design framework exploits the cost-effective IRS to establish favorable communication environment to improve the fair energy efficient. In particular, we study the max-min energy efficiency (EE) of the system by jointly designing the transmit information and energy beamforming at the base station (BS), phase shifts at the IRS, as well as the power splitting (PS) ratio at all users subject to the minimum rate, minimum harvested energy, and transmit power constraints. The formulated problem is non-convex and thus challenging to be solved. We propose two algorithms namely penalty-based and inner approximation (IA)-based to handle the non-convexity of the optimization problem. As such, we divide the original problem into two sub-problems and apply the alternating optimization (AO) algorithm for both proposed algorithms to handle it iteratively. In particular, in the penalty-based algorithm for the first sub-problem, the semi-definite relaxation (SDR) technique, difference of convex functions (DC) programming, majorization-minimization (MM) approach, and fractional programming theory are exploited to transform the non-convex optimization problem into a convex form that can be addressed efficiently. For the second sub-problem, a penalty-based approach is proposed to handle the optimization on the phase shifts introduced by the IRS with the proposed algorithms. For the IA-based method, we jointly optimize beamforming vectors and phase shifts while the PS ratio is solved optimally in the first sub-problem. Simulation results verify the effectiveness of the IRS, which can significantly improve the system EE as compared to conventional benchmark schemes and also unveil a trade-off between convergence and performance gain for the two proposed algorithms.
AbstractList	This paper considers an intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) network, where multiple users decode data and harvest energy from the transmitted signal of a transmitter. The proposed design framework exploits the cost-effective IRS to establish favorable communication environment to improve the fair energy efficient. In particular, we study the max-min energy efficiency (EE) of the system by jointly designing the transmit information and energy beamforming at the base station (BS), phase shifts at the IRS, as well as the power splitting (PS) ratio at all users subject to the minimum rate, minimum harvested energy, and transmit power constraints. The formulated problem is non-convex and thus challenging to be solved. We propose two algorithms namely penalty-based and inner approximation (IA)-based to handle the non-convexity of the optimization problem. As such, we divide the original problem into two sub-problems and apply the alternating optimization (AO) algorithm for both proposed algorithms to handle it iteratively. In particular, in the penalty-based algorithm for the first sub-problem, the semi-definite relaxation (SDR) technique, difference of convex functions (DC) programming, majorization-minimization (MM) approach, and fractional programming theory are exploited to transform the non-convex optimization problem into a convex form that can be addressed efficiently. For the second sub-problem, a penalty-based approach is proposed to handle the optimization on the phase shifts introduced by the IRS with the proposed algorithms. For the IA-based method, we jointly optimize beamforming vectors and phase shifts while the PS ratio is solved optimally in the first sub-problem. Simulation results verify the effectiveness of the IRS, which can significantly improve the system EE as compared to conventional benchmark schemes and also unveil a trade-off between convergence and performance gain for the two proposed algorithms. This paper considers an intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) network, where multiple users decode data and harvest energy from the transmitted signal of a transmitter. The proposed design framework exploits the cost-effective IRS to establish favorable communication environment to improve the fair energy efficient. In particular, we study the max-min energy efficiency (EE) of the system by jointly designing the transmit information and energy beamforming at the base station (BS), phase shifts at the IRS, as well as the power splitting (PS) ratio at all users subject to the minimum rate, minimum harvested energy, and transmit power constraints. The formulated problem is non-convex and thus challenging to be solved. We propose two algorithms namely penalty-based and inner approximation (IA)-based to handle the non-convexity of the optimization problem. As such, we divide the original problem into two sub-problems and apply the alternating optimization (AO) algorithm for both proposed algorithms to handle it iteratively. In particular, in the penalty-based algorithm for the first sub-problem, the semi-definite relaxation (SDR) technique, difference of convex functions (DC) programming, majorization-minimization (MM) approach, and fractional programming theory are exploited to transform the non-convex optimization problem into a convex form that can be addressed efficiently. For the second sub-problem, a penalty-based approach is proposed to handle the optimization on the phase shifts introduced by the IRS with the proposed algorithms. For the IA-based method, we jointly optimize beamforming vectors and phase shifts while the PS ratio is solved optimally in the first sub-problem. Simulation results verify the effectiveness of the IRS, which can significantly improve the system EE as compared to conventional benchmark schemes and also unveil a trade-off between convergence and performance gain for the two proposed algorithms.
Author	Wu, Qingqing Robat Mili, Mohammad Ng, Derrick Wing Kwan Zargari, Shayan Khalili, Ata
Author_xml	– sequence: 1 givenname: Shayan orcidid: 0000-0003-1660-4733 surname: Zargari fullname: Zargari, Shayan email: shayanzargari66@gmail.com organization: School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran – sequence: 2 givenname: Ata orcidid: 0000-0002-3845-1144 surname: Khalili fullname: Khalili, Ata email: ata.khalili@ieee.org organization: Electronics Research Institute, Sharif University of Technology, Tehran, Iran – sequence: 3 givenname: Qingqing orcidid: 0000-0002-0043-3266 surname: Wu fullname: Wu, Qingqing email: qingqingwu@um.edu.mo organization: State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China – sequence: 4 givenname: Mohammad orcidid: 0000-0003-4120-1872 surname: Robat Mili fullname: Robat Mili, Mohammad email: mohammad.robatmili@gmail.com organization: Electronics Research Institute, Sharif University of Technology, Tehran, Iran – sequence: 5 givenname: Derrick Wing Kwan orcidid: 0000-0001-6400-712X surname: Ng fullname: Ng, Derrick Wing Kwan email: w.k.ng@unsw.edu.au organization: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia
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SubjectTerms	Algorithms Array signal processing Beamforming Computational geometry Convex analysis Convexity Energy conversion efficiency Energy efficiency Energy efficiency (EE) Energy harvesting intelligent reflecting surface (IRS) Interference Mathematical programming Nonlinear systems Optimization Power transfer Programming Receivers Reconfigurable intelligent surfaces Signal to noise ratio simultaneous wireless information and power transfer (SWIPT) Wireless communication
Title	Max-Min Fair Energy-Efficient Beamforming Design for Intelligent Reflecting Surface-Aided SWIPT Systems With Non-Linear Energy Harvesting Model
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