Resource Allocation for Two‑Tier RIS‑Assisted Heterogeneous NOMA Networks

doi:10.12142/ZTECOM.202201006

ZTE Communications ›› 2022, Vol. 20 ›› Issue (1): 36-47.DOI: 10.12142/ZTECOM.202201006

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Resource Allocation for Two‑Tier RIS‑Assisted Heterogeneous NOMA Networks

XU Yongjun¹, YANG Zhaohui², HUANG Chongwen³(), YUEN Chau⁴, GUI Guan⁵

^1.School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
^2.Department of Electronic and Electrical Engineering, University College London, London WC1E 6BT, UK
^3.College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
^4.Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore 487372, Singapore
^5.College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Received:2022-01-04 Online:2022-03-25 Published:2022-04-06
About author:XU Yongjun received his Ph.D. degree (Hons.) in communication and information system from Jilin University, China in 2015. He received the Outstanding Doctoral Thesis of Jilin Province in 2016. He is currently an associate professor with the School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, China. From 2018 to 2019, he was a visiting scholar with Utah State University, USA. He has authored or coauthored more than 100 papers for renowned journals, including IEEE COMMUN SURV/TVT/TCOM/TII/TCCN/TGCN/CL/WCL, etc. His recent interests include heterogeneous networks, resource allocation, intelligent reflecting surface, energy harvesting, backscatter communications, mobile edge computing, and UAV communications. He serves as an associate editor of EURASIP Journal on Wireless Communications and Networking, an editor of Physical Communications, and an academic editor of Digital Communications and Networks. He serves as a reviewer for IEEE TWC/TSP/TCOM/TVT/CL, etc. He is a senior member of IEEE.|YANG Zhaohui received his Ph.D. degree in communication and information system with the National Mobile Communications Research Laboratory, Southeast University, China in May 2018. From May 2018 to October 2020, he was a postdoctoral research associate with the Center for Telecommunications Research, Department of Informatics, King's College London, UK. He is currently a visiting associate professor with College of Information Science and Electronic Engineering Zhejiang Key Lab of Information Processing Communication and Networking, Zhejiang University, China and also a research fellow with the Department of Electronic and Electrical Engineering, University College London, UK. He is an associate editor for the IEEE Communications Letters, IET Communications and EURASIP Journal on Wireless Communications and Networking. He was an exemplary reviewer for IEEE Transactions on Communications in 2019 and 2020.|HUANG Chongwen (chongwenhuang@zju.edu.cn) obtained his B.Sc. degree in 2010 from Nankai University, China, and the M.Sc. degree from the University of Electronic Science and Technology of China in 2013, and Ph.D. degree from Singapore University of Technology and Design (SUTD). From Oct. 2019 to Sept. 2020, he is a Postdoc in his mother University SUTD. Since Sept. 2020, he joined Zhejiang University as a tenure-track young professor. Dr. HUANG is the recipient of IEEE Marconi Prize Paper Award in Wireless Communications, and IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award in 2021. He has served as an editor of IEEE Communications Letter, Elsevier Signal Processing, EURASIP Journal on Wireless Communications and Networking and Physical Communication since 2021. His main research interests focus on holographic MIMO surface/reconfigurable intelligent surface, B5G/6G wireless communication, mmWave/THz communications, deep learning technologies for wireless communications, etc.|YUEN Chau received his B.Eng. and Ph.D. degrees from Nanyang Technological University (NTU), Singapore in 2000 and 2004, respectively. He was a Post-Doctoral Fellow with Lucent Technologies Bell Labs, USA in 2005, and a visiting assistant professor with The Hong Kong Polytechnic University, China in 2008. From 2006 to 2010, he was with the Institute for Infocomm Research (I2R), Singapore, where he was involved in an industrial project on developing an 802.11n Wireless LAN system and participated actively in 3Gpp LTE and LTE-Advanced (LTE-A) standardization. Since 2010, he has been with the Singapore University of Technology and Design. He was a recipient of the Lee Kuan Yew Gold Medal, the Institution of Electrical Engineers Book Prize, the Institute of Engineering of Singapore Gold Medal, the Merck Sharp and Dohme Gold Medal, and twice a recipient of the Hewlett Packard Prize. He is a fellow of IEEE.|GUI Guan received his doctoral degree in information and communication engineering from University of Electronic Science and Technology of China in 2012. Since 2015, he has been a professor with Nanjing University of Posts and Telecommunications (NJUPT), China. His recent research interests include artificial intelligence, deep learning, non-orthogonal multiple access, wireless power transfer, and physical layer security. He received the IEEE Communications Society Heinrich Hertz Award in 2021 and Highly Cited Chinese Researchers by Elsevier in 2020. In addition, he served as the IEEE VTS Ad Hoc Committee Member in AI Wireless, Executive Chair of VTC 2021-Fall, Vice Chair of WCNC 2021, TPC Chair of PHM 2021, Symposium Chair of WCSP 2021, and General Co-Chair of Mobimedia 2020.
Supported by:
the China National Key R&D Program(2021YFA1000502);National Natural Science Foundation of China(62101492);Zhejiang Provincial Natural Science Foundation of China(LR22F010002);Distinguished Young Scholars of the National Natural Science Foundation of China, Ng Teng Fong Charitable Foundation in the form of ZJU-SUTD IDEA Grant, Zhejiang University Education Foundation Qizhen Scholar Foundation, and Fundamental Research Funds for the Central Universities(2021FZZX001-21)

Abstract

Abstract:

Reconfigurable intelligent surface (RIS) as a promising technology has been proposed to change weak communication environments. However, most of the current resource allocation (RA) schemes have focused on RIS-assisted homogeneous networks, and there is still no open works about RA schemes of RIS-assisted heterogeneous networks (HetNets). In this paper, we design an RA scheme for a RIS-assisted HetNet with non-orthogonal multiple access to improve spectrum efficiency and transmission rates. In particular, we jointly optimize the transmit power of the small-cell base station and the phase-shift matrix of the RIS to maximize the sum rates of all small-cell users, subject to the unit modulus constraint, the minimum signal-to-interference-plus-noise ratio constraint, and the cross-tier interference constraint for protecting communication quality of microcell users. An efficient suboptimal RA scheme is proposed based on the alternating iteration approach, and successive convex approximation and logarithmic transformation approach. Simulation results verify the effectiveness of the proposed scheme in terms of data rates.

Key words: heterogeneous networks, non-orthogonal multiple access, reconfigurable intelligent surface, resource allocation

XU Yongjun, YANG Zhaohui, HUANG Chongwen, YUEN Chau, GUI Guan. Resource Allocation for Two‑Tier RIS‑Assisted Heterogeneous NOMA Networks[J]. ZTE Communications, 2022, 20(1): 36-47.

Figures/Tables 9

Figure 1 A downlink RIS-assisted heterogeneous non-orthogonal multiple access (NOMA) network

Figure 2 Simulation setup

Table 1 System parameters

Parameters	Values	Parameters	Values
$M$	4^[1]	$K$	3^[1]
$N$	2^[29]	$P$	20 dBm^[29]
$I n t h$	$10 - 3$ ^[21]	$p m a x$	20 mW^[21]
$γ m i n$	2 dB^[29]	$r k$	5 m^[6]
$d m$	10 m^[6]	$ε$	$10 - 6$ ^[21]
$α$	3^[6]	$σ 2$	-100 dBm^[6]

Table 1 System parameters

Parameters	Values	Parameters	Values
$M$	4^[1]	$K$	3^[1]
$N$	2^[29]	$P$	20 dBm^[29]
$I n t h$	$10 - 3$ ^[21]	$p m a x$	20 mW^[21]
$γ m i n$	2 dB^[29]	$r k$	5 m^[6]
$d m$	10 m^[6]	$ε$	$10 - 6$ ^[21]
$α$	3^[6]	$σ 2$	-100 dBm^[6]

Figure 3 Data rate of SCU versus transmit power of the MBS P

Figure 4 Data rate of SCU versus SINR threshold of MCU?γmin

Figure 5 Allocated power from SBS to small-cell user (SCU) versus the number of reflecting elements M

Figure 6 Convergence of proposed resource allocation (RA) scheme

Figure 7 Total rate of SCUs versus the number of reflecting elements M

Figure 8 Total rate of SCUs versus the interference power limit of the MCU Inth

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Resource Allocation for Two‑Tier RIS‑Assisted Heterogeneous NOMA Networks

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