A Sub-6-GHz 5G New Radio RF Transceiver Supporting EN-DC With 3.15-Gb/s DL and 1.27-Gb/s UL in 14-nm FinFET CMOS

Lee, Jongwoo; Son, Ki Yong; Lim, Hong Sik; Jeong, Daechul; Jong, I.Y.; Baek, Sang-Min; Lee, Jae Hoon; Ni, Ronghua; Zuo, Yongrong; Yao, Chunhua; Heo, Sung; Han, Sang-Wook; Cho, Thomas Byunghak; Kang, I-Seok; Kang, Bongsoon; Bae, Jeong-Yeol; Jang, Jaehyuk; Oh, Sangyoon; Chang, Jun Keun; Kang, Seong-Hoon

doi:10.1109/jssc.2019.2938132

Cited by 40 publications

(8 citation statements)

References 4 publications

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“…If only an offset in linear gain coefficient (a 1 and b 1 ) exists in (2), assuming that there is no phase error and higher order non-linearity, the resulting I /Q image can be expressed as…”

Section: Causes For I /Q Image Degradation In Conventional Ddrmsmentioning

confidence: 99%

A Wideband IQ-Mapping Direct-Digital RF Modulator for 5G Transmitters

Shen

Bootsman

Alavi

et al. 2022

IEEE J. Solid-State Circuits

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This article presents a wideband 2 × 12-bit direct-digital RF modulator (DDRM) operating in a 0.5-to-3-GHz band for 5G transmitters. The proposed digital Cartesian modulator features an advanced IQ-mapping technique to boost RF power by 3 dB and suppress the I/ Q image. To verify the proposed concept, a 40-nm CMOS prototype is implemented whose RF peak output power at 2 GHz is more than 14 dBm. It achieves an adjacent-channel leakage ratio (ACLR) of −52 dBc and an error vector magnitude (EVM) of −40 dB for a 20-MHz 256-QAM signal at 2.4 GHz. With a 320-MHz 256-QAM signal, the measured ACLR and EVM performances are better than −43 dBc and −32 dB at 2.4 GHz, respectively, without using any digital pre-distortion.

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Section: Causes For I /Q Image Degradation In Conventional Ddrmsmentioning

confidence: 99%

A Wideband IQ-Mapping Direct-Digital RF Modulator for 5G Transmitters

Shen

Bootsman

Alavi

et al. 2022

IEEE J. Solid-State Circuits

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“…In [309], the authors also followed the same approach in which a low pass filter (LPF) achieved high authentication, improved channel reciprocity, suppressed channel fluctuations, and reduced fundamental disagreement in the OFDM subcarrier's channel. The authors conducted another work to enhance channel reciprocity and CSI [310].…”

Section: Security and Privacy In 5gmentioning

confidence: 99%

Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

et al. 2021

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Fifth-generation (5G) technology will play a vital role in future wireless networks. The breakthrough 5G technology will unleash a massive Internet of Everything (IoE), where billions of connected devices, people, and processes will be simultaneously served. The services provided by 5G include several use cases enabled by the enhanced mobile broadband, massive machine-type communications, and ultra-reliable low-latency communication. Fifth-generation networks potentially merge multiple networks on a single platform, providing a landscape for seamless connectivity, particularly for high-mobility devices. With their enhanced speed, 5G networks are prone to various research challenges. In this context, we provide a comprehensive survey on 5G technologies that emphasize machine learning-based solutions to cope with existing and future challenges. First, we discuss 5G network architecture and outline the key performance indicators compared to the previous and upcoming network generations. Second, we discuss next-generation wireless networks and their characteristics, applications, and use cases for fast connectivity to billions of devices. Then, we confer physical layer services, functions, and issues that decrease the signal quality. We also present studies on 5G network technologies, 5G propelling trends, and architectures that help to achieve the goals of 5G. Moreover, we discuss signaling techniques for 5G massive multiple-input and multiple-output and beam-forming techniques to enhance data rates with efficient spectrum sharing. Further, we review security and privacy concerns in 5G and standard bodies’ actionable recommendations for policy makers. Finally, we also discuss emerging challenges and future directions.

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“…Nevertheless, direct-conversion architectures are still preferred in commercial designs due to their superior RF performance and lower complexity [13], [14], [22].…”

Section: Trends In Rf Front-endsmentioning

confidence: 99%

A Survey of Self-Interference in LTE-Advanced and 5G New Radio Wireless Transceivers

Sadjina

Paireder

Huemer

et al. 2020

IEEE Trans. Microwave Theory Techn.

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This article presents a comprehensive survey of the literature on self-interference (SI) in long-term evolution advanced (LTE-A) and fifth-generation (5G) new radio transceivers and should serve the reader as a guide and starting point for further work on SI management. Current trends in cellular transceiver designs are discussed, and reasons why new technologies, such as carrier aggregation, cause potential sensitivity degradation due to self-interfering signals are highlighted. The survey provides an overview of the most common interference mechanisms and continues with a taxonomy on SI mitigation architectures by comparing the strengths and weaknesses of various techniques.

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A Sub-6-GHz 5G New Radio RF Transceiver Supporting EN-DC With 3.15-Gb/s DL and 1.27-Gb/s UL in 14-nm FinFET CMOS

Cited by 40 publications

References 4 publications

A Wideband IQ-Mapping Direct-Digital RF Modulator for 5G Transmitters

A Wideband IQ-Mapping Direct-Digital RF Modulator for 5G Transmitters

Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

A Survey of Self-Interference in LTE-Advanced and 5G New Radio Wireless Transceivers

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