Chao Yu

Bio: Chao Yu is an academic researcher from Southeast University. The author has contributed to research in topics: Amplifier & Predistortion. The author has an hindex of 14, co-authored 110 publications receiving 1000 citations.

Multibeam Antenna Technologies for 5G Wireless Communications

Abstract: With the demanding system requirements for the fifth-generation (5G) wireless communications and the severe spectrum shortage at conventional cellular frequencies, multibeam antenna systems operating in the millimeter-wave frequency bands have attracted a lot of research interest and have been actively investigated. They represent the key antenna technology for supporting a high data transmission rate, an improved signal-to-interference-plus-noise ratio, an increased spectral and energy efficiency, and versatile beam shaping, thereby holding a great promise in serving as the critical infrastructure for enabling beamforming and massive multiple-input multiple-output (MIMO) that boost the 5G. This paper provides an overview of the existing multibeam antenna technologies which include the passive multibeam antennas (MBAs) based on quasi-optical components and beamforming circuits, multibeam phased-array antennas enabled by various phase-shifting methods, and digital MBAs with different system architectures. Specifically, their principles of operation, design, and implementation, as well as a number of illustrative application examples are reviewed. Finally, the suitability of these MBAs for the future 5G massive MIMO wireless systems as well as the associated challenges is discussed.

The Role of Millimeter-Wave Technologies in 5G/6G Wireless Communications

Abstract: Ever since the deployment of the first-generation of mobile telecommunications, wireless communication technology has evolved at a dramatically fast pace over the past four decades. The upcoming fifth-generation (5G) holds a great promise in providing an ultra-fast data rate, a very low latency, and a significantly improved spectral efficiency by exploiting the millimeter-wave spectrum for the first time in mobile communication infrastructures. In the years beyond 2030, newly emerged data-hungry applications and the greatly expanded wireless network will call for the sixth-generation (6G) communication that represents a significant upgrade from the 5G network – covering almost the entire surface of the earth and the near outer space. In both the 5G and future 6G networks, millimeter-wave technologies will play an important role in accomplishing the envisioned network performance and communication tasks. In this paper, the relevant millimeter-wave enabling technologies are reviewed: they include the recent developments on the system architectures of active beamforming arrays, beamforming integrated circuits, antennas for base stations and user terminals, system measurement and calibration, and channel characterization. The requirements of each part for future 6G communications are also briefly discussed.

Ultrawideband Printed Log-Periodic Dipole Antenna With Multiple Notched Bands

Abstract: A printed log-periodic dipole antenna (PLPDA) with multiple notched bands is proposed for ultrawideband (UWB) applications. The impedance bandwidth of 3.1 GHz-10.6 GHz with VSWR less than 2 is achieved based on the wideband property of the PLPDA as well as the half mode substrate integrated waveguide (HMSIW) Balun. Different from omnidirectional UWB antennas, the end-fire radiation pattern of the PLPDA is more stable within the UWB band. Multiple notched bands are generated by integrating U-shaped slots into the PLPDA for blocking the interference from other narrow band wireless communication systems. Several antennas with the notched frequencies of 3.5 GHz, 5.5 GHz, 6.8 GHz, and 8.5 GHz are designed, fabricated, and measured. The measured results are in agreement with the predicted results.

Full-Angle Digital Predistortion of 5G Millimeter-Wave Massive MIMO Transmitters

Abstract: In this paper, a full-angle digital predistortion (DPD) technique is proposed to linearize fifth-generation (5G) millimeter-wave (mmWave) massive multiple-input-multiple-output (mMIMO) transmitters with low implementation complexity. It is achieved by compensating the differences of power amplifiers (PAs) in different transmitter chains first and then adopting a common digital block to linearize the whole subarray. Based on this operation, all the transmitter chains can be efficiently linearized simultaneously, providing the merits of full-angle linearization including the main beam and sidelobes. To validate the proposed idea, an mmWave full-digital beam-forming transmitter has been developed, which is operated at the center frequency of 24.75–28.5 GHz to meet the 5G candidate frequency bands. Experimental results show that the proposed method can effectively linearize the mmWave mMIMO transmitter in all directions, which provides a promising linearization solution for 5G mMIMO beam-forming systems.

Support Vector Regression-Based Behavioral Modeling Technique for RF Power Transistors

Abstract: A nonlinear behavioral modeling technique, based on support vector regression (SVR), is presented in this letter. As an advanced machine-learning technique, the SVR method provides a more effective way to determine the optimal model when compared with the more traditional modeling approaches based on artificial neural network (ANN) techniques. The proposed technique can overcome the well-known overfitting issue often associated with ANNs. In this letter, the basic theory of the proposed SVR modeling method is provided, along with details on model implementation in the context of RF transistor devices. Both simulation and experimental test examples for a 10-W gallium nitride (GaN) transistor are provided, revealing that the new modeling methodology provides a more efficient and robust prediction throughout the Smith chart when compared with ANNs, with the latest results showing excellent model fidelity at both the fundamental and at the second harmonic.

IEEE Transactions on Microwave Theory and Techniques and Antennas and Propagation Announce a Joint Special Issue on Ultra-Wideband (UWB) Technology

Abstract: Before the emergence of ultra-wideband (UWB) radios, widely used wireless communications were based on sinusoidal carriers, and impulse technologies were employed only in specific applications (e.g. radar). In 2002, the Federal Communication Commission (FCC) allowed unlicensed operation between 3.1–10.6 GHz for UWB communication, using a wideband signal format with a low EIRP level (−41.3dBm/MHz). UWB communication systems then emerged as an alternative to narrowband systems and significant effort in this area has been invested at the regulatory, commercial, and research levels.

Prospective Multiple Antenna Technologies for Beyond 5G

Abstract: Multiple antenna technologies have attracted much research interest for several decades and have gradually made their way into mainstream communication systems. Two main benefits are adaptive beamforming gains and spatial multiplexing, leading to high data rates per user and per cell, especially when large antenna arrays are adopted. Since multiple antenna technology has become a key component of the fifth-generation (5G) networks, it is time for the research community to look for new multiple antenna technologies to meet the immensely higher data rate, reliability, and traffic demands in the beyond 5G era. Radically new approaches are required to achieve orders-of-magnitude improvements in these metrics. There will be large technical challenges, many of which are yet to be identified. In this paper, we survey three new multiple antenna technologies that can play key roles in beyond 5G networks: cell-free massive MIMO, beamspace massive MIMO, and intelligent reflecting surfaces. For each of these technologies, we present the fundamental motivation, key characteristics, recent technical progresses, and provide our perspectives for future research directions. The paper is not meant to be a survey/tutorial of a mature subject, but rather serve as a catalyst to encourage more research and experiments in these multiple antenna technologies.

The Role of Millimeter-Wave Technologies in 5G/6G Wireless Communications

Abstract: Ever since the deployment of the first-generation of mobile telecommunications, wireless communication technology has evolved at a dramatically fast pace over the past four decades. The upcoming fifth-generation (5G) holds a great promise in providing an ultra-fast data rate, a very low latency, and a significantly improved spectral efficiency by exploiting the millimeter-wave spectrum for the first time in mobile communication infrastructures. In the years beyond 2030, newly emerged data-hungry applications and the greatly expanded wireless network will call for the sixth-generation (6G) communication that represents a significant upgrade from the 5G network – covering almost the entire surface of the earth and the near outer space. In both the 5G and future 6G networks, millimeter-wave technologies will play an important role in accomplishing the envisioned network performance and communication tasks. In this paper, the relevant millimeter-wave enabling technologies are reviewed: they include the recent developments on the system architectures of active beamforming arrays, beamforming integrated circuits, antennas for base stations and user terminals, system measurement and calibration, and channel characterization. The requirements of each part for future 6G communications are also briefly discussed.

Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks

Abstract: The next-generation wireless networks are evolving into very complex systems because of the very diversified service requirements, heterogeneity in applications, devices, and networks. The network operators need to make the best use of the available resources, for example, power, spectrum, as well as infrastructures. Traditional networking approaches, i.e., reactive, centrally-managed, one-size-fits-all approaches, and conventional data analysis tools that have limited capability (space and time) are not competent anymore and cannot satisfy and serve that future complex networks regarding operation and optimization cost effectively. A novel paradigm of proactive, self-aware, self-adaptive, and predictive networking is much needed. The network operators have access to large amounts of data, especially from the network and the subscribers. Systematic exploitation of the big data dramatically helps in making the system smart, intelligent, and facilitates efficient as well as cost-effective operation and optimization. We envision data-driven next-generation wireless networks, where the network operators employ advanced data analytics, machine learning (ML), and artificial intelligence. We discuss the data sources and strong drivers for the adoption of the data analytics, and the role of ML, artificial intelligence in making the system intelligent regarding being self-aware, self-adaptive, proactive and prescriptive. A set of network design and optimization schemes are presented concerning data analytics. This paper concludes with a discussion of challenges and the benefits of adopting big data analytics, ML, and artificial intelligence in the next-generation communication systems.

Big Data Analytics, Machine Learning and Artificial Intelligence in Next-Generation Wireless Networks.

Abstract: The next-generation wireless networks are evolving into very complex systems because of the very diversified service requirements, heterogeneity in applications, devices, and networks. The mobile network operators (MNOs) need to make the best use of the available resources, for example, power, spectrum, as well as infrastructures. Traditional networking approaches, i.e., reactive, centrally-managed, one-size-fits-all approaches and conventional data analysis tools that have limited capability (space and time) are not competent anymore and cannot satisfy and serve that future complex networks in terms of operation and optimization in a cost-effective way. A novel paradigm of proactive, self-aware, self- adaptive and predictive networking is much needed. The MNOs have access to large amounts of data, especially from the network and the subscribers. Systematic exploitation of the big data greatly helps in making the network smart, intelligent and facilitates cost-effective operation and optimization. In view of this, we consider a data-driven next-generation wireless network model, where the MNOs employ advanced data analytics for their networks. We discuss the data sources and strong drivers for the adoption of the data analytics and the role of machine learning, artificial intelligence in making the network intelligent in terms of being self-aware, self-adaptive, proactive and prescriptive. A set of network design and optimization schemes are presented with respect to data analytics. The paper is concluded with a discussion of challenges and benefits of adopting big data analytics and artificial intelligence in the next-generation communication system.