Jingfan Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jingfan Zhang has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Yu Liu
Shandong University
4 shared publications - Jie Huang
Southeast University
3 shared publications - Zhaolei Zhang
School of Integrated Circuits, Shandong University, Jinan, China
3 shared publications - Hengtai Chang
School of Information Science and Engineering, Shandong University, Qingdao, China
2 shared publications - Jingquan Li
School of Microelectronics, Shandong University, Jinan 250101, China
2 shared publications - Cheng-Xiang Wang
Southeast University
1 shared publication
Latest Publications
- 20 May 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Vehicular Technology
- Vol. 73 (10) , 14113-14126
Machine Learning Based Clustering and Modeling for 6G UAV-to-Ground Communication Channels
Towards the sixth-generation (6G) wireless communication, unmanned aerial vehicles (UAVs) have been regarded as an indispensable part due to its flexible deployment, wide coverage, and high mobility. This also creates challenges for channel research. Scatterers are normally present in the structure of clusters during UAV communication, and cluster-based channel modeling is significant. In this paper, the variational BayesianGaussian mixture model (VB-GMM) algorithm is proposed for clustering, which takes into account the time-space properties. Cluster tracking is implemented using the multipath component distance (MCD) algorithm. Intra- and inter-cluster characterization, such as the number of clusters, cluster power distribution, angular/delay offset, and angular/delay spreads, are well studied. Moreover, cluster lifetime and birth-death (B-D) properties are extracted and analyzed. Based on these cluster characteristics acquired by machine learning (ML) method, a novel UAV-toground communication channel model is proposed, and a fourstate Markov chain is also introduced to portray the evolution of clusters. Simulation results match well with channel measurements, which verifies the practicality of the proposed model. This paper can give theoretical and technical support for the design and evaluation of UAV-to-ground communication systems
- 20 May 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Vehicular Technology
- Vol. 73 (10) , 14113-14126
Machine Learning Based Clustering and Modeling for 6G UAV-to-Ground Communication Channels
Towards the sixth-generation (6G) wireless communication, unmanned aerial vehicles (UAVs) have been regarded as an indispensable part due to its flexible deployment, wide coverage, and high mobility. This also creates challenges for channel research. Scatterers are normally present in the structure of clusters during UAV communication, and cluster-based channel modeling is significant. In this paper, the variational BayesianGaussian mixture model (VB-GMM) algorithm is proposed for clustering, which takes into account the time-space properties. Cluster tracking is implemented using the multipath component distance (MCD) algorithm. Intra- and inter-cluster characterization, such as the number of clusters, cluster power distribution, angular/delay offset, and angular/delay spreads, are well studied. Moreover, cluster lifetime and birth-death (B-D) properties are extracted and analyzed. Based on these cluster characteristics acquired by machine learning (ML) method, a novel UAV-toground communication channel model is proposed, and a fourstate Markov chain is also introduced to portray the evolution of clusters. Simulation results match well with channel measurements, which verifies the practicality of the proposed model. This paper can give theoretical and technical support for the design and evaluation of UAV-to-ground communication systems
- Open Access
- 9 July 2023
- Published by MDPI AG in Electronics
- Vol. 12 (14) , 3015
A Novel UAV-Assisted Multi-Mobility Channel Model for Urban Transportation Emergency Communications
With the increasing requirements for unmanned aerial vehicle (UAV) communication in various application scenarios, the UAV-assisted emergency communication in urban transportation scenario has received great attention. In this paper, a novel UAV-assisted UAV-to-vehicle (U2V) geometry-based stochastic model (GBSM) for the urban traffic communication scenario is proposed. The three-dimensional (3D) multi-mobilities of the transmitter (Tx), receiver (Rx), and clusters are considered by introducing the time-variant acceleration and velocity correspondingly. The velocity variation of the clusters is used to simulate the motion of vehicles around the Rx. Moreover, to describe the vehicles’ moving states, Markov chain is adopted to analyze the changes in cluster motion states, including survival, death, dynamic, and static states. By adjusting the scenario-specific parameters, such as the vehicle density () and dynamic–static ratio (), the model can support various urban traffic scenarios. Based on the proposed model, several key statistical properties, namely the root mean square (RMS) delay spread, temporal autocorrelation function (ACF), level-crossing rate (LCR), power delay profile (PDP), and stationary interval, under different clusters and antenna accelerations are obtained and analyzed. The accuracy of the proposed model is verified by the measured data. The results demonstrate the usability of our model, which can be provided as a reference for the design, evaluation, and optimization of future communication networks between UAV and vehicles in urban transportation emergency communications.- 9 July 2023
- Published by MDPI AG in Electronics
- Vol. 12 (14) , 3015
Open AccessA Novel UAV-Assisted Multi-Mobility Channel Model for Urban Transportation Emergency Communications
With the increasing requirements for unmanned aerial vehicle (UAV) communication in various application scenarios, the UAV-assisted emergency communication in urban transportation scenario has received great attention. In this paper, a novel UAV-assisted UAV-to-vehicle (U2V) geometry-based stochastic model (GBSM) for the urban traffic communication scenario is proposed. The three-dimensional (3D) multi-mobilities of the transmitter (Tx), receiver (Rx), and clusters are considered by introducing the time-variant acceleration and velocity correspondingly. The velocity variation of the clusters is used to simulate the motion of vehicles around the Rx. Moreover, to describe the vehicles’ moving states, Markov chain is adopted to analyze the changes in cluster motion states, including survival, death, dynamic, and static states. By adjusting the scenario-specific parameters, such as the vehicle density () and dynamic–static ratio (), the model can support various urban traffic scenarios. Based on the proposed model, several key statistical properties, namely the root mean square (RMS) delay spread, temporal autocorrelation function (ACF), level-crossing rate (LCR), power delay profile (PDP), and stationary interval, under different clusters and antenna accelerations are obtained and analyzed. The accuracy of the proposed model is verified by the measured data. The results demonstrate the usability of our model, which can be provided as a reference for the design, evaluation, and optimization of future communication networks between UAV and vehicles in urban transportation emergency communications. - Open Access
Channel Modeling and Characteristics Analysis under Different 3D Dynamic Trajectories for UAV-Assisted Emergency Communications
This study involved channel modeling and characteristics analysis of unmanned aerial vehicles (UAVs) according to different operating trajectories. Based on the idea of standardized channel modeling, air-to-ground (AG) channel modeling of a UAV was carried out, taking into consideration that both the receiver (Rx) and the transmitter (Tx) ran along different types of trajectories. In addition, based on Markov chains and a smooth-turn (ST) mobility model, the influences of different operation trajectories on typical channel characteristics—including time-variant power delay profile (PDP), stationary interval, temporal autocorrelation function (ACF), root mean square (RMS) delay spread (DS), and spatial cross-correlation function (CCF)—were studied. The multi-mobility multi-trajectory UAV channel model matched well with actual operation scenarios, and the characteristics of the UAV AG channel could be analyzed more accurately, thus providing a reference for future system design and sensor network deployment of sixth-generation (6G) UAV-assisted emergency communications.
Open AccessChannel Modeling and Characteristics Analysis under Different 3D Dynamic Trajectories for UAV-Assisted Emergency Communications
This study involved channel modeling and characteristics analysis of unmanned aerial vehicles (UAVs) according to different operating trajectories. Based on the idea of standardized channel modeling, air-to-ground (AG) channel modeling of a UAV was carried out, taking into consideration that both the receiver (Rx) and the transmitter (Tx) ran along different types of trajectories. In addition, based on Markov chains and a smooth-turn (ST) mobility model, the influences of different operation trajectories on typical channel characteristics—including time-variant power delay profile (PDP), stationary interval, temporal autocorrelation function (ACF), root mean square (RMS) delay spread (DS), and spatial cross-correlation function (CCF)—were studied. The multi-mobility multi-trajectory UAV channel model matched well with actual operation scenarios, and the characteristics of the UAV AG channel could be analyzed more accurately, thus providing a reference for future system design and sensor network deployment of sixth-generation (6G) UAV-assisted emergency communications.
- Open Access
Channel Characterization and Modeling for 6G UAV-Assisted Emergency Communications in Complicated Mountainous Scenarios
Regarding the new demands and challenges of sixth-generation (6G) mobile communications, wireless networks are undergoing a significant shift from traditional terrestrial networks to space-air-ground-sea-integrated networks. Unmanned aerial vehicle (UAV) communications in complicated mountainous scenarios are typical applications and have practical implications, especially in emergency communications. In this paper, the ray-tracing (RT) method was applied to reconstruct the propagation scenario and then acquire the wireless channel data. Channel measurements are also conducted in real mountainous scenarios for verification. By setting different flight positions, trajectories, and altitudes, channel data in the millimeter wave (mmWave) band was obtained. Important statistical properties, such as the power delay profile (PDP), Rician K-factor, path loss (PL), root mean square (RMS) delay spread (DS), RMS angular spreads (ASs), and channel capacity were compared and analyzed. The effects of different frequency bands on channel characteristics at 3.5 GHz, 4.9 GHz, 28 GHz, and 38 GHz bands in mountainous scenarios were considered. Furthermore, the effects of extreme weather, especially different precipitation, on the channel characteristics were analyzed. The related results can provide fundamental support for the design and performance evaluation of future 6G UAV-assisted sensor networks in complicated mountainous scenarios.
Open AccessChannel Characterization and Modeling for 6G UAV-Assisted Emergency Communications in Complicated Mountainous Scenarios
Regarding the new demands and challenges of sixth-generation (6G) mobile communications, wireless networks are undergoing a significant shift from traditional terrestrial networks to space-air-ground-sea-integrated networks. Unmanned aerial vehicle (UAV) communications in complicated mountainous scenarios are typical applications and have practical implications, especially in emergency communications. In this paper, the ray-tracing (RT) method was applied to reconstruct the propagation scenario and then acquire the wireless channel data. Channel measurements are also conducted in real mountainous scenarios for verification. By setting different flight positions, trajectories, and altitudes, channel data in the millimeter wave (mmWave) band was obtained. Important statistical properties, such as the power delay profile (PDP), Rician K-factor, path loss (PL), root mean square (RMS) delay spread (DS), RMS angular spreads (ASs), and channel capacity were compared and analyzed. The effects of different frequency bands on channel characteristics at 3.5 GHz, 4.9 GHz, 28 GHz, and 38 GHz bands in mountainous scenarios were considered. Furthermore, the effects of extreme weather, especially different precipitation, on the channel characteristics were analyzed. The related results can provide fundamental support for the design and performance evaluation of future 6G UAV-assisted sensor networks in complicated mountainous scenarios.