Ziwei Huang’s research while affiliated with Peking University and other places

In this paper, a novel multi-modal intelligent channel model for sixth-generation (6G) multiple-unmanned aerial vehicle (multi-UAV)-to-multi-vehicle communications is proposed. To thoroughly explore the mapping relationship between the physical environment and the electromagnetic space in the complex multi-UAV-to-multi-vehicle scenario, two new parameters, i.e., terrestrial traffic density (TTD) and aerial traffic density (ATD), are developed and a new sensing-communication intelligent integrated dataset is constructed in suburban scenario under different TTD and ATD conditions. With the aid of sensing data, i.e., light detection and ranging (LiDAR) point clouds, the parameters of static scatterers, terrestrial dynamic scatterers, and aerial dynamic scatterers in the electromagnetic space, e.g., number, distance, angle, and power, are quantified under different TTD and ATD conditions in the physical environment. In the proposed model, the channel non-stationarity and consistency on the time and space domains and the channel non-stationarity on the frequency domain are simultaneously mimicked. The channel statistical properties, such as time-space-frequency correlation function (TSF-CF), time stationary interval (TSI), and Doppler power spectral density (DPSD), are derived and simulated. Simulation results match ray-tracing (RT) results well, which verifies the accuracy of the proposed multi-UAV-to-multi-vehicle channel model.

Given the importance of datasets for sensing-communication integration research, a novel simulation platform for constructing communication and multi-modal sensory dataset is developed. The developed platform integrates three high-precision software, i.e., AirSim, WaveFarer, and Wireless InSite, and further achieves in-depth integration and precise alignment of them. Based on the developed platform, a new synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM), named SynthSoM, is proposed. The SynthSoM dataset contains various air-ground multi-link cooperative scenarios with comprehensive conditions, including multiple weather conditions, times of the day, intelligent agent densities, frequency bands, and antenna types. The SynthSoM dataset encompasses multiple data modalities, including radio-frequency (RF) channel large-scale and small-scale fading data, RF millimeter wave (mmWave) radar sensory data, and non-RF sensory data, e.g., RGB images, depth maps, and light detection and ranging (LiDAR) point clouds. The quality of SynthSoM dataset is validated via statistics-based qualitative inspection and evaluation metrics through machine learning (ML) via real-world measurements. The SynthSoM dataset is open-sourced and provides consistent data for cross-comparing SoM-related algorithms.

This paper proposes a novel sixth-generation (6G) multi-modal intelligent vehicle-to-vehicle (V2V) channel model from light detection and ranging (LiDAR) point clouds based on Synesthesia of Machines (SoM). To explore the mapping relationship between physical environment and electromagnetic space, a new V2V high-fidelity mixed sensing-communication integration simulation dataset with different vehicular traffic densities (VTDs) is constructed. Based on the constructed dataset, a novel scatterer recognition (ScaR) algorithm utilizing neural network SegNet is developed to recognize scatterer spatial attributes from LiDAR point clouds via SoM. In the developed ScaR algorithm, the mapping relationship between LiDAR point clouds and scatterers is explored, where the distribution of scatterers is obtained in the form of grid maps. Furthermore, scatterers are distinguished into dynamic and static scatterers based on LiDAR point cloud features, where parameters, e.g., distance, angle, and number, related to scatterers are determined. Through ScaR, dynamic and static scatterers change with the variation of LiDAR point clouds over time, which precisely models channel non-stationarity and consistency under different VTDs. Some important channel statistical properties, such as time-frequency correlation function (TF-CF) and Doppler power spectral density (DPSD), are obtained. Simulation results match well with ray-tracing (RT)-based results, thus demonstrating the necessity of exploring the mapping relationship and the utility of the proposed model.

Currently, the research on the cellular vehicle-to-everything (C-V2X) technology has obvious challenges and limitations of security, reliability, and consistency. This article aims to provide insights for a comprehensive understanding of C-V2X testing from the perspectives of communication performance, functional application, and security, which can support the C-V2X network softwarization and management. First, we review the recent progress of C-V2X testing on wireless communications, including antenna and propagation, protocol conformance, communication performance, and interoperability testing. Second, we discuss the current work in the area of C-V2X testing on functional applications, including functional environment and positioning performance testing. Third, we review the recent advance in C-V2X testing on security, including vehicular gateway and penetration security testing. Finally, open issues and promising future research directions for C-V2X testing are outlined.

In this paper, a novel channel modeling approach, named light detection and ranging (LiDAR)-aided geometry-based stochastic modeling (LA-GBSM), is developed. Based on the developed LA-GBSM approach, a new millimeter wave (mmWave) channel model for sixth-generation (6G) vehicular intelligent sensing-communication integration is proposed, which can support the design of intelligent transportation systems (ITSs). The proposed LA-GBSM is accurately parameterized under high, medium, and low vehicular traffic density (VTD) conditions via a sensing-communication simulation dataset with LiDAR point clouds and scatterer information for the first time. Specifically, by detecting dynamic vehicles and static buildings/trees through LiDAR point clouds via machine learning, scatterers are divided into static and dynamic scatterers. Furthermore, statistical distributions of parameters, e.g., distance, angle, number, and power, related to static and dynamic scatterers are quantified under high, medium, and low VTD conditions. To mimic channel non-stationarity and consistency, based on the quantified statistical distributions, a new visibility region (VR)-based algorithm in consideration of newly generated static/dynamic scatterers is developed. Key channel statistics are derived and simulated. By comparing simulation results and ray-tracing (RT)-based results, the utility of the proposed LA-GBSM is verified.

In the future sixth-generation (6G) era, to support accurate localization sensing and efficient communication link establishment for intelligent agents, a comprehensive understanding of the surrounding environment and proper channel modeling are indispensable. The existing method, which solely exploits radio frequency (RF) communication information, is difficult to accomplish accurate channel modeling. Fortunately, multi-modal devices are deployed on intelligent agents to obtain environmental features, which could further assist in channel modeling. Currently, some research efforts have been devoted to utilizing multi-modal information to facilitate channel modeling, while still lack a comprehensive review. To fill this gap, we embark on an initial endeavor with the goal of reviewing multi-modal intelligent channel modeling (MMICM) via Synesthesia of Machines (SoM). Compared to channel modeling approaches that solely utilize RF communication information, the utilization of multi-modal information can provide a more in-depth understanding of the propagation environment around the transceiver, thus facilitating more accurate channel modeling. First, this paper introduces existing channel modeling approaches from the perspective of the channel modeling evolution. Then, we have elaborated and investigated recent advances in the topic of capturing typical channel characteristics and features, i.e., channel non-stationarity and consistency, by characterizing the mathematical, spatial, coupling, and mapping relationships. In addition, applications that can be supported by MMICM are summarized and analyzed. To corroborate the superiority of MMICM via SoM, we give the simulation result and analysis. Finally, some open issues and potential directions for the MMICM are outlined from the perspectives of measurements, modeling, and applications.

In this paper, a mixed-bouncing based channel model with cooperative space-array-time (S-A-T) consistency and space-array-time-frequency (S-A-T-F) non-stationarity is proposed for sixth generation (6G) multiple-unmanned aerial vehicle (multi-UAV) cooperative communication systems with millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) technologies. To model the transmission propagation in multi-UAV integrated channels more accurately, the single-bouncing transmissions and multi-bouncing transmissions in multi-UAV integrated channels are simultaneously modeled and quantified by a cooperative cluster density index for the first time. Meanwhile, the transmissions through line-of-sight (LoS), ground reflection, single-bouncing, and multi-bouncing are captured. To jointly mimic cooperative S-A-T consistency and S-A-T-F non-stationarity in the integrated scattering environment (SE), a new cooperative consistent and non-stationary modeling algorithm is developed based on the frequency-dependent path gain, visibility region (VR), and birth-death (BD) survival probability. The channel parameters related to multi-UAVs are also taken into account in the developed algorithm. The corresponding multi-UAV cooperative channel statistical properties are derived by taking mixed-bouncing transmission into account. Meanwhile, the accuracy of the mixed-bouncing based multi-UAV integrated channel model with cooperative S-A-T consistency and S-A-T-F non-stationarity is validated as simulation results match well with ray-tracing results.

In this letter, a multi-modal sensing data based real-time path loss prediction scheme for sixth-generation (6G) unmanned aerial vehicle (UAV)-to-ground communications is developed. Meanwhile, a new mixed multi-modal sensing and communication integration dataset in the UAV-to-ground scenario is constructed. Based on the constructed dataset, the mapping relationship between physical space and electromagnetic space is explored, and the multi-modal sensing data based real-time path loss prediction scheme is developed. Simulation results show that the proposed scheme outperforms 3GPP UMa non-line-of-sight (NLoS) and slope-intercept models. By comparing simulation and ray-tracing (RT)-based results, the utility of the proposed scheme is further verified.