Nan An, Fang Yang, Ling Cheng, Jian Song, Zhu Han
©SHUTTERSTOCK/METAMORWORKS
As one of the key technologies of the intelligent transportation system (ITS), vehicle-to-everything (V2X) communications have received increasing attention. Because of the broad and unlicensed spectrum, free space optical (FSO) communications have become a potential technology for V2X communications. In this article, the implementation of FSO communications is introduced first. Afterward, for the three advantages of FSO communications in ITS, the concrete principle and implementation approaches are illustrated in detail. First, based on the adaptive illumination schemes, the communication range is expanded without visual interference caused by illumination. Second, with the help of mode division multiplexing (MDM) and wavelength division multiplexing (WDM), the communication rate is further increased. Third, integrating sensing and communication enables vehicles to effectively obtain information and improve traffic efficiency and safety. In addition, applications of FSO communications in various communication scenarios in ITS are described. To satisfy the complex requirements of communication, illumination, and sensing in traffic scenarios, a deep learning-enabled heterogeneous system of FSO and radio frequency is proposed. Furthermore, future applications and open issues are provided for further investigation.
With the continuous development of technology, the transportation system has also made remarkable progress in recent years. At the same time, traffic accidents, traffic congestion, and other traffic problems have become serious social problems. The ITS uses a variety of technologies to establish a large-scale, real-time, accurate, and efficient integrated transportation and management system. To efficiently transfer a large amount of data generated in ITS, V2X communications have attracted much attention in both academia and industry.
For the purpose of improving the performance of V2X communications, researchers have experimented with a variety of radio frequency (RF) communications technologies, such as Wi-Fi, dedicated short-range communications (DSRC), long-term evolution-vehicle (LTE-V), millimeter wave (mmWave), and television white space spectrum (White-Fi) [1], [2]. Based on the ubiquitous light-emitting diode (LED), vehicular visible light communications (VVLC) are introduced into ITS [3]. In light of achieving a large communication capacity, FSO communications based on laser diodes (LDs) are a potential technology for V2X communications. Compared with RF communications, FSO communications occupy a wide and unregulated spectrum band and do not induce electromagnetic interference (EMI), and thus are suitable for electromagnetically sensitive environments. Various V2X communications technologies work in different spectrum bands and have different characteristics in terms of delay, data rate, and so on. The comparisons of various V2X communications technologies are shown in Table 1.
Table 1 Comparison of different V2X communications technologies.
With the characteristics of small size, high brightness, long illumination range, and high electrooptic conversion efficiency, LDs have been applied to the illumination industry. Compared with LEDs, LDs can provide 10 to 100 times more brightness and illuminate 10 times farther [4]. Hence, LD headlights have been adopted by some products of BMW and Audi, and become the development direction of future headlights. Besides, LD can be utilized as the light source of street lamps in the future, which can enhance public illumination. In addition to the visible laser used for illumination, the infrared laser is applied to lidars, which helps vehicles obtain information about their surroundings. With the popularization of laser equipment, the LD will appear increasingly in ITS, laying a foundation for the application of FSO communications.
In ITS, various use cases with different requirements for V2X communications are difficult to be fully supported by a single communication technology [2], while heterogeneous communication systems can alleviate this problem [1]. Applying FSO communications into ITS is of mutual benefit with other V2X communications, instead of replacing them. Potential heterogeneous systems for FSO communications with other V2X systems are shown in Figure 1.
Figure 1 Illustration of various communication technologies in ITS.
With the aim of exploring the potential of FSO communications in ITS, the opportunities and challenges of FSO communications in ITS are investigated in this article. We review the main implementations of FSO communications. Furthermore, three promising advantages of FSO communications in ITS are illustrated, as follows:
The remainder of the article is organized as follows: the implementations of FSO communications are introduced in the “System Implementation†section. For the next three sections, three advantages offered by FSO communications in ITS are elaborated comprehensively. In the “Future Applications and Open Issues†section, a sketch map for the future application of FSO communications in ITS is provided, and then we give the challenges that need to be further investigated. Finally, the last section concludes the whole article.
In this section, the system model and physical implementation of the FSO communications in ITS are introduced. In FSO communications, data are loaded onto the light at the transmitter and sent to the receiver through atmospheric channels. Then, the receiver directly detects the optical intensity of the incident light to recover the data, which is known as intensity modulation direct detection. The system model of FSO communications in ITS is shown in Figure 2.
Figure 2 System model of FSO communications in ITS.
In ITS, the main function of LD is to produce white light for illumination, such as LD headlights of vehicles. With the intention of illuminating a large area, the intensity patterns of laser beams are altered by diffusers [5].
White lights are mainly generated by two approaches, which are combining multiple light beams of different colors and illuminating the phosphor with a monochromatic LD. The common implementation of the two approaches is to combine red, green, and blue (RGB) lasers [6] and to illuminate yellow phosphors with blue LDs, as the transmitter front-ends A and B shown in Figure 2, respectively. In the first method, LDs with different colors need to cooperate with each other to ensure illumination performance, such as color temperature, which increases the complexity and cost of the system. In contrast, the second method has lower cost and ease of implementation, but energy loss occurs during the phosphor excitation by photons. Besides, the slow transient response of phosphors limits the fast switching speeds of LDs.
In addition to visible lasers for illumination, infrared lasers are utilized in lidar for sensing, as shown by the transmitter front-end C in Figure 2.
For FSO communications in ITS, signals are mainly transmitted through line-of-sight (LOS) links in an atmospheric channel, which is mainly influenced by vehicle mobility and atmospheric conditions.
In ITS, vehicle mobility results in the rapid change of distance and relative direction between vehicles. Because of the nonuniform intensity distribution of vehicle lights at different angles, the variation of the relative direction causes the fluctuation of the received optical intensity. Besides, considering the receiver’s limited area and beam divergence, a receiver can only receive part of the beam, resulting in geometric loss. The increase in the propagation distance aggravates beam divergence such that the distance variation leads to the fluctuation of geometric loss. Vehicle mobility can be modeled by microscopic traffic flow models, such as the car-following model and lane-change model. On this basis, the influence of vehicle mobility on optical signal propagation is characterized by coherence time and link duration [7].
On account of the time-varying atmospheric conditions, statistical models are often utilized to model the effects of atmospheric conditions on FSO communications. On the one hand, because of the absorption and scattering of light beams by particles, the optical signal is affected by atmospheric attenuation, and the relevant statistical models are adopted [8]. On the other hand, due to the constant change of atmospheric refractive index, atmospheric turbulence causes random fluctuation of optical intensity. According to the strength of atmospheric turbulence, models such as the log-normal model are adopted [8].
In ITS, the implementations of receivers for FSO communications are based on photodiodes (PDs) or cameras, whose schematic structures are shown as receiver front-ends A and B in Figure 2, respectively. With the help of optical systems, PDs and cameras convert optical signals into electrical signals.
Camera-based receivers can recognize signals from different light sources, which allow data from multiple transmitters to be received simultaneously. In addition, based on the received images, the camera can track the trajectory of the transmitter to establish a stable communication link. In ITS, the main limitation of camera-based receivers is a low frame rate, which limits the data rate, such as 55 Mb/s at a 120-m distance [9]. Due to the additional latency of reading pixel value, camera-based receivers are not suitable for V2X applications with strict latency requirements, such as applications related to traffic safety and cooperative driving. In comparison, because of the fast PD response, PD-based receivers can play a role in these applications. In ITS, a PD with a wide field-of-view (FOV) is susceptible to interference from other light sources, but a PD with a narrow FOV is difficult to cope with vehicle mobility. A positive-intrinsic-negative PD is commonly used in FSO communications for ITS on account of its low cost and simple driving circuits. In comparison, the utilization of high-cost avalanche PDs and single photon avalanche diodes provides higher sensitivity and higher gain, which are suitable for V2X communications under severe weather.
At the receiver, three kinds of noise mainly exist, which are optical background noise, thermal noise, and shot noise. The optical background noise caused by the background radiation can be reduced by the optical filter. Additionally, the thermal noise and shot noise can be modeled as Gaussian noise.
The FSO communications bring three advantages to the ITS: a larger communication range realized by utilizing LDs and adaptive illumination schemes, a higher communication rate, where both MDM and WDM are employed, and more benefits from ISAC function. These characteristics are shown in Figure 3 at a glance. Based on the typical communication application and corresponding requirements of ITS summarized in Table 2, the contents of the above advantages are further elaborated in the following three sections. First, expanding the communication range is discussed in this section.
Figure 3 Three advantages of FSO communications in ITS: expanding the communication range, increasing the communication rate, and ISAC. ADB, adaptive driving beam; AFS, adaptive front-lighting system.
Table 2 Typical communication applications and corresponding requirements in ITS.
For VVLC systems, based on spontaneous radiation, LEDs radiate photons randomly in all directions, leading to a reduction in the LED output power in the desired direction. As for the existing VVLC prototypes, camera-based receivers and PD-based receivers can reach a low error communication distance of up to 100 m and 40–60 m, respectively [3], which is not suitable for V2X communications applications in long ranges, such as infotainment and traffic efficiency. To solve these problems, the introduction of LD-based FSO communications can achieve better performance.
On the basis of stimulated radiation, the divergence angle of beams generated by LDs is smaller than that of traditional light sources so that the energy of laser beams is concentrated. In addition, compared with LED-based vehicle lights, LD-based vehicle lights provide more brightness and illuminate farther, which expands the illumination and communication range. In Figure 4, simulations are carried out to demonstrate the comparison of communication rate versus communication range between VVLC and FSO communications in ITS. Three typical weather conditions of clear weather, moderate fog, and thick fog are considered in the simulation, and their attenuation coefficients are set as 0 m – 1, .00782 m – 1, and .01565 m – 1, respectively. Additionally, the log-normal model is used to represent the influence of atmospheric turbulence. The bit error rate (BER) of simulations is set at 10– 5 and both systems have the same transmitted optical power. Under the same weather conditions, FSO communications achieve a longer communication range than VVLC. When the communication rate is fixed at 107 bps, the communication ranges of FSO communications are about 40 m, 60 m, and 80 m farther than that of VLC in dense fog, moderate fog, and clear weather, respectively. When the distance between vehicles is long, the information can be effectively exchanged between vehicles through FSO communications, which guarantees the requirements of infotainment and traffic efficiency listed in Table 2.
Figure 4 Communication rate versus communication range under different weathers for FSO communications and VVLC in ITS.
Driven by the development trend of automobile intelligence, pixelated fine illumination is the development trend of vehicle lights in ITS. According to the changeable surrounding environment, an adaptive front-lighting system (AFS) controls LD arrays to provide pixelated fine illumination and choose appropriate illumination parameters adaptively [10], while an adaptive driving beam (ADB) system can switch the low and high beams automatically.
With the help of AFS and ADB, illumination modes of vehicles are adaptively adjusted to avoid visual interference and satisfy illumination and communication requirements under different scenarios. AFS and ADB help drivers recognize road conditions and avoid glare from oncoming vehicles, which improves driving safety and comfort [10]. As shown in Figure 3, when a vehicle turns, the illumination direction of headlights is rotated to the corresponding direction by AFS, avoiding traffic accidents caused by the visual blind area and realizing intersection assistance (Table 2). For vulnerable road user (pedestrians, cyclists, etc.) protection shown in Table 2, pixelated illumination provides optical traffic signs to alert traffic participants based on AFS and ADB. Additionally, the illumination and communication functions of LDs can be switched flexibly. For example, in the case that some LDs in the vehicle lights meet the illumination requirements, the beam direction of the remaining LDs aligned with receivers to establish LOS links [11] so that the communication range is not limited to the current illumination area, which alleviates the communication interruption caused by vehicle mobility.
In contrast to LEDs, LDs have much larger modulation bandwidth, thus FSO communications are more suitable for high-speed data transmission in ITS than VVLC [11]. As shown in Figure 4, FSO communications can achieve a higher communication rate than VVLC over the same weather and communication range. In an attempt to further increase the transmission rates, multiplexing schemes are applied to transmit multiple independent signals simultaneously. In FSO communications, WDM and MDM have unique advantages due to the utilization of LD. This section will provide a detailed introduction to WDM and MDM in FSO communications.
As shown in Figure 3, WDM modulates multiple signals on lights with diverse wavelengths at the transmitter front-end, which fully utilizes the resources of the wavelength domain and brings a high data rate [4]. In VVLC, most of the visible light spectrum is occupied by a typical RGB triplet of LEDs, such that only a few channels of information are transmitted in parallel by WDM [11]. In contrast, for the reason that the spectral linewidth of an LD is about 2–3 nm [4], the wavelength domain resources are finely divided to establish more parallel channels, which improves the spectral efficiency and data rate. As shown in Figure 2, the transmitter front-end A integrates multiple LDs with different wavelengths, which establishes parallel channels and naturally has the advantage of WDM. However, multiple LDs with different wavelengths cooperate to meet illumination requirements in ITS, thus each LD may not achieve its maximum data rate [6].
For V2X communications applications related to infotainment, a high data rate brought by WDM provides a richer variety of entertainment services, which brings a better travel experience for passengers in vehicles. Furthermore, WDM allows massive traffic information to be fully exchanged among vehicles and infrastructures, thus improving the performance of FSO communications in applications related to traffic efficiency and cooperative driving, as shown in Table 2. For example, in cooperative platooning, the high data rate ensures the information exchange within a platoon. In addition, a large amount of local sensing information can be exchanged among vehicles at a high data rate with the help of WDM, which guarantees the requirements of cooperative sensing.
In MDM, multiple signals are loaded on orthogonal modes and transmitted in overlapping spaces. For FSO communications, OAM is a common set of orthogonal modes. As shown in Figure 3, OAM beams have spiral wavefronts, which are often characterized by the OAM order. Because of the orthogonality between OAM beams with different orders, the coaxial multimode OAM beams can be separated into single-mode OAM beams to recover the origin signal loaded on every OAM mode [12]. In the case of employing OAM-based MDM, a 120-m free-space OAM-multiplexing link with a total capacity of 400 Gb/s has been demonstrated [12]. On account of the random phase of the beam generated by spontaneous radiation, it is difficult for LEDs to produce OAM beams with specific spiral phases, thus VVLC cannot utilize the OAM domain resources to improve the data rate.
According to whether OAM beams are generated in a laser resonator cavity, the OAM beam generation methods are divided into intracavity approaches and extracavity approaches [12]. For intracavity approaches, diverse OAM beams are generated directly by adjusting the resonator parameters. The intracavity method has high loss and is difficult to generate stable OAM beams, which limits its application. For extracavity approaches, a laser beam generated by an LD is converted into an OAM beam through converters, as shown in Figure 5, which contains a spiral phase plate, cylindrical lens pairs, fork grating, and liquid crystal spatial light modulator (LC-SLM). Furthermore, almost all of the converters mentioned above can be used as inverse converters to eliminate the spiral wavefront, which is the basis of OAM detection. As shown in Figure 5, the synthesis and decomposition of coaxial multimode OAM beams are implemented with the help of the beam splitter or Dammann grating [12].
Figure 5 The framework of OAM-based MDM in FSO communications.
In ITS, vehicle mobility causes misalignment between the transmitter and receiver, leading to the crosstalk among orthogonal OAM modes, which can be alleviated by acquisition, tracking, and pointing (ATP) mechanisms. To maintain FSO links, ATP mechanisms have been widely applied to ensure alignment and reduce pointing errors, which play an important role in high-mobility FSO links [12].
The third advantage of FSO communications in ITS is the potential of the ISAC system. In this section, the advantages of ISAC are introduced in detail, and a potential implementation is introduced.
Compared with RF radar, lidar has the advantages of high resolution and small size, which lays the foundation for the wide application of lidar in ITS. Based on the reflected laser, lidar forms a high-resolution point cloud of the surroundings, accomplishing the sensing of complex traffic scenarios in ITS.
From the view of system structure, the similarities of the signal processing lead to resemblances in the hardware of FSO communications and lidars. Therefore, lidar-based ISAC enables communication and sensing to share the same hardware, thereby reducing the system cost [13].
In an ISAC system, both functions can benefit each other. Based on the locations of receivers provided by lidar, transmitters are aligned to the receivers to establish a LOS link, which alleviates the link instability caused by vehicle mobility, especially the misalignment of OAM beams mentioned in the “MDM†subsection, above. On the basis of lidar-based ISAC, local sensing information is transmitted to other vehicles, so as to achieve long-range sensing through cooperative sensing.
For traffic safety applications shown in Table 2, according to the movement of vehicles and traffic participants acquired by lidar, a vehicle can respond in time when abnormal traffic behavior and vulnerable road users appear. Additionally, for communication applications related to traffic efficiency, a vehicle obtains some state information of other vehicles through lidar, thus reducing the communication burden of exchanging vehicle states. Moreover, the omnidirectional communication of the lidar-based ISAC enables FSO communication links not to be limited to the illuminated area of the vehicle lights, so vehicles flexibly establish communication links with the receivers, which is useful in cooperative driving.
Compared to RF ISAC systems, optical ISAC systems do not cause serious communication interference at high vehicle density scenarios. Because of the directivity of laser beams, the self-interference problem faced by an RF ISAC system can be easily solved in an optical ISAC system. Moreover, due to the requirement of LOS links, the optical signals are difficult to be intercepted and eavesdropped on, which ensures the security of communication and sensing. In a VVLC-based ISAC system, the large divergence angle of beams emitted by LED results in the weak intensity of received beams. Therefore, positioning is limited to a short distance, which cannot meet the sensing requirements of vehicles for complex traffic scenarios.
In ITS, frequency-modulated continuous-wave (FMCW) lidar is considered a potential sensor and has attracted the attention of researchers. FMCW lidar transmits linear frequency modulated signal, also known as chirp signal, and extracts sensing information from the reflected lasers based on coherent demodulation. To implement lidar-based ISAC, information is loaded on a chirp signal, which is recovered well at a low signal-to-noise ratio (SNR) due to the coherent demodulation. Nevertheless, the loading of information deteriorates the correlation performance of the chirp signal, thereby degrading the sensing performance.
To verify the potential of ISAC based on FMCW lidar in ITS, a numerical simulation has been performed in Figure 6. The up-ramp chirp signal is utilized as a basic waveform to sense the distance of other vehicles. To transmit data, the on–off keying modulation scheme with a biased-chirp spread spectrum is employed to communicate. Biased chirp signals and zeros are transmitted when data bits are ones and zeros, respectively. After receiving the reflected signal, the distance is calculated based on the time-of-flight, which is extracted from the correlation results of the matching filter. Specifically, the distance between two vehicles is 245 m and the bandwidth of the chirp signal is 100 MHz. In an attempt to simulate the real atmospheric channel, the Mie scattering model and log-normal model are applied to simulate atmospheric scattering and atmospheric turbulence, respectively.
Figure 6 The numerical results of lidar-based ISAC system based on up-ramp chirp signal: (a) BER versus SNR; (b) RMSE of distance versus SNR.
In Figure 6, simulation results of BER and root mean-square error (RMSE) of distance varying with SNR are shown, respectively. The correlation performance of a chirp signal is closely related to the product of its period and bandwidth. The larger the product, the narrower the correlation peak of the result outputted by a matched filter, in favor of improving the sensing accuracy and reducing the BER of FSO communications. In addition, the data rate is inversely proportional to the chirp period. Therefore, when the bandwidth of a chirp signal and SNR are fixed, the BER and RMSE of distance decrease with the increase of the chirp signal period, while the communication rate also decreases. In an attempt to make a compromise between sensing and communication, the chirp period should be carefully selected. According to the required sensing performance, the product of a chirp period and bandwidth is determined, thus the chirp period is selected based on chirp bandwidth. Moreover, if the chirp’s time-of-flight is longer than the chirp period, it is difficult for lidar to distinguish in which period the received reflected signal is emitted, thus the maximum distance that can be measured by this ISAC system without ambiguity is the distance that the laser travels in half a chirp period. For example, the unambiguous range is about 300 m when the chirp period is 2 ${\mu}{\text{s}}$.
In practice, the illumination, communication, and sensing requirements in ITS are changeable, which brings challenges to FSO communications. In light of exploring the possible trends, a deep learning-enabled heterogeneous communication system for ITS is shown in Figure 7 and the following challenges need to be researched.
Figure 7 Future application of the FSO communications for ITS integrated with emerging technologies.
The LOS links of FSO communications are easily blocked by obstacles, while RF communication covers the blind areas of FSO communications through non-LOS communication. For RF communications, FSO communications mitigate communication interference between vehicles with the help of LOS links. Therefore, the heterogeneous FSO–RF system as shown in Figure 7 can further enhance the communication capability of ITS. For heterogeneous FSO–RF systems, communication is achieved based on switching between FSO and RF or based on aggregated FSO–RF system. For the two systems, switching technology and bandwidth aggregation technology need to be further researched.
On the basis of multiple lights of a vehicle and multiple receivers on another vehicle or infrastructure, multiple-inputs, multiple-outputs (MIMO) technology can be introduced into the FSO communications for ITS. Therefore, transmit diversity is employed to reduce the influences of optical noise and optical intensity fluctuation. Additionally, spatial multiplexing is utilized in FSO MIMO to transmit data in parallel, which allows various applications to transmit information at different data rates and modulation modes to guarantee communication effectiveness.
For the purpose of obtaining high-accuracy sensing results, complex signal processing algorithms require a large number of computing resources, which cannot be supported by the limited computing resources of vehicle processors. In this case, mobile edge computing can offload computing tasks from vehicles to a base station and other idle computing servers, such as parked vehicles, to guarantee computing efficiency [14]. Furthermore, communication, sensing, and computing result in energy consumption of vehicles, which is alleviated by energy harvesting technologies. Based on the simultaneous lightwave information and power transfer [15], the information and energy transmission are unified so that energy consumption is reduced in ITS.
In ITS, the environment around the vehicle is complex and changeable, leading to the rapid change of requirements in communication, illumination, sensing, and computation. Neural networks can adaptively select transmission modes and allocate resources according to system parameters and requirements. With the help of computing technologies, local networks can exchange data and models with edge computing servers, which stimulates the potential of deep learning. Nevertheless, the problems of deep learning, such as easy overfitting and poor migration effect, bring bottlenecks to the application of deep learning in a communication system, which opens up a new research space for intelligent communication.
To fully tap the potential of FSO communications in ITS, this article introduces its advantages from three aspects in combination with various communication applications. First, based on the LD with high brightness, the communication range can be expanded with AFS and ADB. Second, on the basis of WDM and MDM, the communication rate of FSO communications is further increased. Third, in an optical ISAC system, sensing results are utilized to improve communication performance, while vehicles can exchange sensing information through FSO communications to expand the sensing range. With the development of ITS, more requirements will be put forward for V2X communications, thus resulting in several future applications and open issues. With continuing efforts, FSO communications are expected to become a practical V2X communications technology in ITS.
This work was supported in part by the National Key Research and Development Program of China under Grant 2022YFE0101700; the Science, Technology, and Innovation Commission of Shenzhen Municipality under Grant JSGG20211029095003004; the Beijing National Research Center for Information Science and Technology under Grant BNR2022RC01017; the National Research Foundation of South Africa under Grants 148765 and 129311; the National Science Foundation CNS-2107216, CNS-2128368, and CMMI-2222810; Toyota; and Amazon. Fang Yang is the corresponding author.
Nan An (an21@mails.tsinghua.edu.cn) is currently pursuing his Ph.D. degree with the DTV Technology Research and Development Center, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. He received his B.S. degree from the Department of Electronic Engineering, Tsinghua University, Beijing, China, in 2021. His research interests are in the field of visible light communications and wireless communications.
Fang Yang (fangyang@tsinghua.edu.cn) is an associate professor with the Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. He received his B.S.E. and Ph.D. degrees in electronic engineering from Tsinghua University, Beijing, China, in 2005 and 2009, respectively. His research interests include visible and wireless communications. He received the IEEE Scott Helt Memorial Award (2015). He is an Institution of Engineering and Technology fellow and a Senior Member of IEEE.
Ling Cheng (ling.cheng@wits.ac.za) is a full professor at the University of the Witwatersrand, Wits 2050, Johannesburg, South Africa. He received his B. Eng. degree from Huazhong University of Science and Technology in 1995, his M. Ing. degree in 2005, and his D. Ing. degree in 2011 from the University of Johannesburg. His research interests are in telecommunications and artificial intelligence. He has published over 100 papers in journals and conference proceedings. He is a senior member of IEEE and the vice-chair of the IEEE South African Information Theory Chapter.
Jian Song (jsong@tsinghua.edu.cn) is the director of the Tsinghua DTV Technology R&D Center, Tsinghua University, Beijing 100084, China. He received his B. Eng and Ph.D. degrees in electrical engineering from Tsinghua University, in 1990 and 1995, respectively. His current research interest is in the area of digital TV broadcasting. He has published more than 300 peer-reviewed journal and conference papers. He holds two U.S. and more than 80 Chinese patents. He is Fellow of both IEEE and IET.
Zhu Han (hanzhu22@gmail.com) is a professor in the Electrical and Computer Engineering Department and the Computer Science Department at the University of Houston, Houston, Texas 77004 USA. He received his B.S. degree in electronic engineering from Tsinghua University in 1997, and his M.S. and Ph.D. degrees in electrical and computer engineering from the University of Maryland, College Park, in 1999 and 2003, respectively. He is Fellow of IEEE.
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Digital Object Identifier 10.1109/MVT.2023.3244032
