Full-Duplex Massive MIMO Self-Interference Suppression Based on Beamforming

doi:10.12142/ZTECOM.202504011

ZTE Communications ›› 2025, Vol. 23 ›› Issue (4): 97-109.DOI: 10.12142/ZTECOM.202504011

• Research Papers • 上一篇下一篇

收稿日期:2024-04-10 出版日期:2025-12-25 发布日期:2025-12-22

Full-Duplex Massive MIMO Self-Interference Suppression Based on Beamforming

ZHANG Boyu, ZHANG Ling, LI Zijing, SHEN Ying()

University of Electronic Science and Technology of China, Chengdu 611731, China

Received:2024-04-10 Online:2025-12-25 Published:2025-12-22
About author:ZHANG Boyu received his bachelor’s degree in electronic information engineering from University of Electronic Science and Technology of China (UESTC) in 2022. He is currently working towards a master’s degree in communication and information engineering at UESTC. His research interests include wireless communication and full duplex for massive MIMO systems.
ZHANG Ling received her master’s degree from University of Electronic Science and Technology of China (UESTC) in 2023. She is currently a chip and device design engineer. Her research interests include wireless communications, massive MIMO systems, chip development.
LI Zijing received her master’s degree in electronic information engineering from University of Electronic Science and Technology of China (UESTC) in 2023. She is currently working towards a PhD degree in communication and information engineering at UESTC. Her research interests include SI suppression and integrated communication and sensing for massive MIMO systems.
SHEN Ying (shenying@uestc.edu.cn) received his PhD degree in communication and information systems from University of Electronic Science and Technology of China (UESTC) in 2009. He is currently working at the National Key Laboratory of Wireless Communications, UESTC. His research interests include full-duplex, MIMO, and broadcasting systems.

摘要/Abstract

Abstract:

The complexities of hardware and signal processing make it especially challenging to develop self-interference cancellation (SIC) techniques for full-duplex (FD) massive multiple-input-multiple-output (MIMO) systems. This paper examines an FD massive MIMO system featuring multi-stream transmission. Specifically, it adopts an architecture where a single transmit or receive radio frequency (RF) channel is connected to three antennas in the same polarization direction, effectively reducing the number of transmit and receive RF channels by half. The SoftNull algorithm serves as the primary method for SI suppression, leveraging digital precoding during transmission. Additionally, this study outlines a design strategy to enhance SIC in the proposed system. Simulation results highlight the efficacy of the SoftNull algorithm, which achieves a remarkable total SIC of up to 64 dB. Furthermore, combined with measures such as antenna isolation and increased transceiver array spacing, the resulting sum rate can be twice that of a half-duplex system.

Key words: full-duplex, massive MIMO, self-interference, beamforming, precoding

. [J]. ZTE Communications, 2025, 23(4): 97-109.

ZHANG Boyu, ZHANG Ling, LI Zijing, SHEN Ying. Full-Duplex Massive MIMO Self-Interference Suppression Based on Beamforming[J]. ZTE Communications, 2025, 23(4): 97-109.

图/表 10

Figure 1 FD massive MIMO communication system with multi-stream transmission

Figure 2 Application scenarios of FD massive MIMO system

Figure 3 Base station transceiver’s uniform rectangular array structure

Figure 4 FD massive MIMO system with transmitting digital precoder

Table 1 Simulation parameters of full-duplex massive MIMO system

Parameter	Value
Bandwidth	100 MHz
Center frequency	2.595 GHz
Transmit power of base station	30 dBm
Transmit power of user	30 dBm
Number of base station antennas	$N t = N r = 6 × 8 × 2 = 96$
Number of base station RF channels	$N R F, t = N R F, r = 32$
Number of uplink users	$K U p = 2 × 4 = 8$
Number of downlink users	$K D o w n = 2 × 4 = 8$
Antenna spacing along x-axis	$d t, x = d r, x = 0.67 λ$
Antenna spacing along y-axis	$d r, y = d t, y = 0.5 λ$
URAs spacing	$d 1 = 0.5 λ$
User spacing along x-axis	$d U p, x = d D o w n, x = 2.679 5 m$
User spacing along y-axis	$d U p, y = d D o w n, y = 2.679 5 m$
Cross-polarization isolation	$P X P I, d B = 25 d B$
Path loss factor	$η = 2.92$
Number of uplink channel multipaths	$C U p = 1$ , $L U p, c = 1$ , $L U p = 1$
Number of downlink channel multipaths	$C D o w n = 1$ , $L D o w n, c = 1$ , $L D o w n = 1$
Number of SI channel multipaths	$C S I = 1$ , $L S I, c = 1$ , $L S I = 1$

Table 1 Simulation parameters of full-duplex massive MIMO system

Parameter	Value
Bandwidth	100 MHz
Center frequency	2.595 GHz
Transmit power of base station	30 dBm
Transmit power of user	30 dBm
Number of base station antennas	$N t = N r = 6 × 8 × 2 = 96$
Number of base station RF channels	$N R F, t = N R F, r = 32$
Number of uplink users	$K U p = 2 × 4 = 8$
Number of downlink users	$K D o w n = 2 × 4 = 8$
Antenna spacing along x-axis	$d t, x = d r, x = 0.67 λ$
Antenna spacing along y-axis	$d r, y = d t, y = 0.5 λ$
URAs spacing	$d 1 = 0.5 λ$
User spacing along x-axis	$d U p, x = d D o w n, x = 2.679 5 m$
User spacing along y-axis	$d U p, y = d D o w n, y = 2.679 5 m$
Cross-polarization isolation	$P X P I, d B = 25 d B$
Path loss factor	$η = 2.92$
Number of uplink channel multipaths	$C U p = 1$ , $L U p, c = 1$ , $L U p = 1$
Number of downlink channel multipaths	$C D o w n = 1$ , $L D o w n, c = 1$ , $L D o w n = 1$
Number of SI channel multipaths	$C S I = 1$ , $L S I, c = 1$ , $L S I = 1$

Figure 5 Impact of fD on SIC in received RF channels

Figure 6 Impact of fD on total SIC

Figure 7 Impact of fD on sum rate

Figure 8 Impact of antenna isolation on sum rate

Figure 9 Impact of antenna isolation on sum rate with transceiveruniform rectangular array spacing of 5λ

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Full-Duplex Massive MIMO Self-Interference Suppression Based on Beamforming

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