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Millimeter-wave (MmWave) channel characteristics are quite different from sub-6 GHz frequency bands. The major differences include higher path loss and sparser multipath components (MPCs), resulting in more significant time-varying characteristics in mmWave channels. It is difficult to characterize the time-varying characteristics of mmWave channels through statistical models, e.g. slope-intercept models for path loss and lognormal models for delay spread and angular spread. Therefore, highly accurate channel modeling and prediction are necessary for deployment of mmWave communication systems. In this paper, a mmWave channel parameter prediction method using deep learning and environment point cloud is proposed. The parameters predicted include path loss, root-mean-square (RMS) delay spread, angular spread and Rician $K$ factor. First, we introduce a novel measurement campaign for indoor mmWave channel at 60 GHz, where a light detection and ranging (LiDAR) sensor and panoramic camera were co-located with a channel sounder and then time-synchronized point clouds and images were captured to describe environmental information. Furthermore, a fusion method between the point clouds and images is proposed based on geometric relationship between the LiDAR and camera, to compress the size of the data collected. Second, based on a classic point cloud classification model (PointNet), we propose a novel regression PointNet model applied to channel parameter prediction. To overcome generalization problem of model under limited measurements, an area-by-area training and testing method is proposed. Third, we evaluate the proposed prediction model and training method, by comparing prediction results with measured ground truth. To provide insights on what training inputs are best, we demonstrate the impacts of different combinations of input information on prediction accuracy. Last, the deployment and implementation method of the proposed model is recommended to the readers.

Radio propagation environment plays a critical role in the performance of wireless communication systems, and understanding channel characteristics is vital for ensuring reliable communication links and optimizing system performance. Ray tracing is an effective method to investigate propagation characteristics in a complex environment, and how to quickly and accurately obtain environmental information needs to be solved. This paper presents dynamic environment reconstruction and ray tracing simulation in railway tunnel environment based on Simultaneous Localization and Mapping (SLAM) algorithm and Poisson reconstruction algorithm. Accurate channel parameters are obtained and analyzed based on ray tracing simulation. Both straight and curved tunnels are considered and investigated, and the results show the channel characteristics in complex railway tunnel environments.

2020 marked the first year of the era of 5G commercialization in China and the inception of worldwide research in 6G.Compared with 5G networks, 6G networks will have larger information capacity, higher transmission rate, lower transmission delay, larger number of connected devices, higher spectrum efficiency, higher energy efficiency, and can support applications with higher mobile speed.Therefore, 6G networks will involve many new key technologies and also face many challenges.The background, development, typical applications, performance indicators, network architecture, key technologies and future challenges of 6G were systematically reviewed, which are expected to provide some reference for future 6G networks related research.

Urban rail traffic is an important part of modern transportation infrastructure.As a new generation of mobile communication technology, 5G can provide high data rate and low latency wireless transmission, which helps to improve the efficiency and service quality of urban rail traffic system.Due to the complexity of urban rail traffic scenarios, accurate communication scenario classification, channel characterization and channel models are required to provide theoretical support for the design of urban rail traffic 5G communication systems.The classification of 5G urban rail traffic radio propagation scenarios to support channel measurements and modeling was proposed.Current status of urban rail traffic channel measurements and modeling was shown, and the current challenges were analyzed.The applications of artificial intelligence in channel feature extraction and channel modeling were discussed, and the 5G urban rail traffic channels by considering reconfigurable intelligent surface and unmanned aerial vehicle were analyzed.Finally, the research on 5G urban rail traffic channel modeling at millimeter wave frequency band was described.

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.

Since the radio frequency identification (RFID) technology was put forward,it has been widely used in the transportation,logistics,industry and business due to its convenience and high efficiency.As a carrier for storing recognizable data,the RFID tags play a crucial role in the Internet of things (IoT).More and more functions and modules are embedded in the RFID tags and developed into smart tags in different application fields.Recently,wireless batteryless smart tags have arisen and grown rapidly with the development of the IoT and various emerging backscatter technologies.Wireless batteryless smart tags use the passive backscatter technology to harvest the energy and transmit information through radio frequency signals.Starting from the RFID technology,the history of the RFID and smart tags was introduced briefly,the differences between traditional smart tags and emerging wireless smart tags were compared,the advantages of wireless batteryless smart tags were concluded,several specific applications of it in different fields were listed,and the open challenges were analyzed.

We report on a novel weakly-coupled 6-LP-mode fiber that features a nanopore-assisted step-index core with double-cladding profile. The purpose of this fiber design is to alleviate modes coupling and bend sensitivity, thus simplifies the multiple-input multiple-output (MIMO) process and the environmental requirements in which the fiber is used. Through numerical simulations, we show that the minimum effective index difference (Δfleff) of larger than 1.8*10-3 among all LP-mode over C and L band can be achieved by a simple nanopore with 170 nm radius in the center of the core. Furthermore, the dependences of both bending loss and chromatic dispersion on fiber structures are investigated by the finite element method (FEM) to show the good broadband performance. The proposed fiber targets applications in simpler MIMO space division multiplexing (SDM) systems such as data center and short-reach large-capacity transmission.

The millimeter-wave (mmWave) technology is one possible solution to addressing the explosive requirements for mobile data access on high-speed railways (HSRs). However, utilizing the mmWave technology on HSRs will result in large Doppler shifts, which should be estimated and compensated. Traditional Doppler shift estimators, such as the cyclic prefix (CP)-based estimator (CPBE), the two-training-symbol-based estimator (TBE), and the best linear unbiased estimator (BLUE), have inferior estimation accuracy especially at low signal-to-noise ratio (SNR). In this paper, we, therefore, propose three new Doppler shift estimators for mmWave communication systems on HSRs: a radio environment map (REM)-based estimator (RBE) derived from the maximum a posteriori algorithm; an equally-divided structure-based estimator (ESBE) that divides the effective orthogonal frequency-division multiplexing symbol (OFDM) into multiple equal fragments; and an enhanced ESBE (EESBE) that takes the results of RBE as a priori knowledge. Simulation results show that these three algorithms outperform existing Doppler shift estimators in mmWave communication systems on HSRs.

Herbal weight loss drugs are becoming more widely used in the fight against obesity, but ineffective regulation of these products have resulted in harmful additives. These products may contain adulterants such as sibutramine hydrochloride that may result in serious adverse health events including death. This work established a color precipitation reaction-based rapid screening method for illegal adulteration of sibutramine hydrochloride in natural herbal medicines (NHM) and dietary supplements (DS). While a variety of chromatography- and electrophoresis-based systems have been reported to measure this analyte, they generally suffer from high costs, complicated sample preparation, and a costly analytical infrastructure. In contrast, we present a simple, handheld kit to assay for sibutramine. The performance metrics of this tool include an average detection time of approximately 3 minutes, which is markedly shorter than conventional methods (HPLC or HPLC-MS, etc.), a detection limit of 0.1 mg per aliquot, and an accuracy of 99.02% (n = 820). More strikingly, the sensitivity is 100% (n = 278), and the specificity is 98.52% (n = 542). The rapid test kit developed from this screening method was evaluated by FDA. In summary, this screening method is a rapid, simple, and low-cost tool for the detection of sibutramine in NHM and DS with superior selectivity and sensitivity. For these reasons, this method is especially suitable for underdeveloped settings because it can be employed onsite without any instrumentation. In addition, this approach could rapidly exclude most of the negative samples to boost efficiency in large-scale samples assay. If necessary, positive samples can undergo further alternate testing methods to confirm the positive results of sibutramine hydrochloride content.

Vehicle detection and identification techniques have been widely applied in traffic scene to acquire traffic information depending on various sensors, such as video camera, induction loop, and magnetic sensor. The quantity and category of vehicles are the key components of the intelligent transportation systems as they provide original data for further analysis. Compared with the inductive loop and video camera, magnetic sensor can measure magnetic field distortion caused by the movement of vehicles. The precise amount and category of a vehicle can be obtained through reasonable data analysis. A novel vehicle detection algorithm is proposed based on the short-term variance sequence transformed from raw magnetic signal. The parking-sensitive module is introduced to enhance the robustness and adaptability of a detection method. With abundant signal data, 42-D features are extracted from every vehicle signal comprising statistical features of whole waveform and short-term features of fragment signal. The Gradient Tree Boosting algorithm is employed to identify four vehicle categories. The effectiveness of the proposed approach is validated by the data collected at a freeway exit of Beijing. According to the experiential results based on 4507 vehicles, the vehicle detection algorithm proves to have 99.8% accurate rate and can be highly practical in site traffic environment. The 80.5% accuracy rate on vehicle identification approves the effectiveness of the proposed features on recognizing vehicles.

In this paper, a massive multiple-input multiple-output (MIMO) channel measurement campaign with 256-element virtual rectangular array at the base station was conducted. The typical hotspot scenario, subway station, is considered, and the measurements were conducted at 6 GHz with a bandwidth of 100 MHz. A hybrid clustering approach is proposed to characterize the cluster evolution over the large-scale array. In the hybrid approach, we apply the space-alternating generalized expectation maximization algorithm to estimate the multipath components (MPCs), and use the multipath component distance-based tracking algorithm and the KPowerMeans algorithm for MPCs tracking and clustering. A cluster partition algorithm is further proposed to adjudge the clusters association over the array, and output the final clustering results. Under such a scheme, cluster-based model parameters are provided with detailed analysis. The extracted parameters include overall angle distribution, global angular spread, inter-cluster parameters, and intra-cluster parameters. The obtained model parameters can be fed into the new channel simulator for massive MIMO. This is useful for the design and application of the practical massive MIMO system in the future.

The Internet of Vehicles, previously referred to as Vehicular Ad Hoc Networks (VANETs), will be a collection of mobile ad hoc networks for vehicular communications to improve driving safety and traffic efficiency. Major categories include vehicle-to-vehicle (V2V) communications and vehicle-to-infrastructure (V2I) communications. Collectively these (and future categories) can be termed V2X communications. These two categories entail a variety of communication channels, network topologies, vehicle densities and communication scenarios, and in response to this, IEEE has released the IEEE 802.11p and IEEE 1609 protocols for VANETs. With the global commercial application of Long Term Evolution (LTE) mobile communication systems, LTE based vehicular (LTE-V) networks have been shown to offer advantages in V2I communications, coverage, high mobility and complicated communication scenarios. Therefore, in the 5th generation’s (5G) mobile communication networks, the 3GPP is discussing V2V communications as a device-to-device (D2D) communication application. As is well known, in high mobility scenarios, the wireless channel is rapidly time- varying, Doppler shifts and spreads can be much larger than in cellular, network topology can change quickly, and switching among base stations and vehicles is more frequent. All this presents formidable challenges to realizing reliable communications with low latency in 5G LTE-V systems.

This paper presents results from an extensive measurement campaign of multi-user channels with large-scale antenna arrays (MULAs) in a subway station environment. Based on the measurements, two spatial separation metrics are characterized. The variance of user separability and temporal behavior are investigated. Furthermore, a MULA channel model with transmit correlation is proposed, which ensures that the MULA channels can be generated with a given degree of compatibility. The proposed MULA model is validated by comparing the distributions of the sum rate capacity and the condition number to measurements.

As one of the most important candidate technologies for the fifth-generation wireless communication systems, massive MIMO technology has been widely studied recently because of the significant improvements it can provide in terms of spectrum efficiency and power efficiency. As the foundation of wireless communication, research on propagation characteristics for massive MIMO channels is of primary importance. This paper investigates the characteristics for massive MIMO channels in an indoor hall scenario at 6-GHz. Channel measurements were conducted with a bandwidth of 200 MHz in both line of sight (LOS) and non-LOS (NLOS) conditions. The statistical parameters in the delay domain were extracted to show the spatial variation along the large-scale antenna array. Based on the measured data, the spatial variation is first defined, and then characterized by using the spatial power delay profile correlation coefficient and spatial channel gain correlation matrix collinearity, and the quasi-stationarity region along the massive MIMO array is estimated. Furthermore, by using the space-alternating generalized expectation-maximization algorithm, the multipath components are extracted and classified according to the propagation environments, which provide more insights to the spatial variation of massive MIMO channels. Finally, the characteristics of the extracted angular parameters are investigated and the fluctuations are modeled, where the spatial variation phenomenon was clearly observed over the large-scale antenna array. The quasi-stationarity distance of the variation was found to range from 2.5 to 32.5 cm, and the LOS case differs from the NLOS case at 6 GHz. These results and discussions are useful for analysis and future modeling for massive MIMO channels, and may contribute to future definitions of channel spatial consistency.

In this paper, the Gaussian mixture model (GMM) is introduced to the channel multipath clustering. In the GMM field, the expectation-maximization (EM) algorithm is usually utilized to estimate the model parameters. However, the EM widely converges into local optimization. To address this issue, a hybrid differential evolution (DE) and EM (DE-EM) algorithms are proposed in this paper. To be specific, the DE is employed to initialize the GMM parameters. Then, the parameters are estimated with the EM algorithm. Thanks to the global searching ability of DE, the proposed hybrid DE-EM algorithm is more likely to obtain the global optimization. Simulations demonstrate that our proposed DE-EM clustering algorithm can significantly improve the clustering performance.

With the rapid development of wireless communication networks, the need of massive Multiple-Input Multiple-Output (MIMO) to offer sufficient network capacity has become evident. As a part of array signal processing, direction-of-arrival (DoA) estimation is of vital importance to obtain directional information of sources and to enable the 3-D beamforming. In this paper, the performance of DoA estimation for massive MIMO systems is analyzed and compared using a low complexity algorithm. To be specific, 2-D unitary estimation of signal parameters via rotational invariance techniques (U-ESPRIT) algorithm is studied to jointly estimate elevation and azimuth information, and uniform rectangular array is selected to represent massive MIMO systems. The simulation results indicate that the U-ESPRIT algorithm works well for the massive MIMO systems. The performance has been evaluated with various types of antenna configuration and source numbers. Finally, the array resolution is adopted to analyze the performance of elevation and azimuth estimation and how to choose the appropriate antenna array configuration.

In reverberation chambers, the multipath channels with different delay characteristics can be generated by loading the chamber with different amounts of absorbers. This paper investigates the impacts of absorber loadings on the delay characteristics based on realistic measurements. An efficient method for estimating the root-mean-square (rms) delay spread in the chamber is presented. Furthermore, it is found that the chamber loadings also significantly affect the quality of a digitally modulated signal and the corresponding modulation quality measurements are performed.

According to the demands for fifth-generation (5G) communication systems, high frequency bands (above 6 GHz) need to be adopted to provide additional spectrum. This paper investigates the characteristics of indoor corridor channels at 15 GHz. Channel measurements with a vector network analyzer in two corridors were conducted. Based on a ray-optical approach, a deterministic channel model covering both antenna and propagation characteristic is presented. The channel model is evaluated by comparing simulated results of received power and root mean square delay spread with the corresponding measurements. By removing the impact of directional antennas from the transmitter and receiver, a path loss model as well as small-scale fading properties for typical corridors is presented based on the generated samples from the deterministic model. Results show that the standard deviation of path loss variation is related to the Tx height, and placing the Tx closer to the ceiling leads to a smaller fluctuation of path loss.

The indoor radio channels at 15 GHz are investigated based on measurements. The large- and small-scale fading behaviors as well as the delay dispersion characteristics are discussed. It is found that the large-scale fading, Ricean K-factor, and delay spread can be described by log-normal distributions. Furthermore, both autocorrelation and cross correlation properties of the above parameters are analyzed and modeled. These parameters characterize fading and delay behaviors as well as their mutual dependency and can be used as empirical values for future wireless system design and simulation in 15 GHz short-range indoor channels.

Recently, high frequency bands (above 6 GHz) have attracted more attention for the next generation communication systems due to the limited frequency resources below 6 GHz. To reveal the influence of frequency on propagation channels, channel characterization results at 14.6 and 3.6 GHz bands based on measurements in an indoor scenario and in a reverberation chamber are presented. The measurement results indicate minimal differences in path loss exponents, shadow fading standard deviation, root-mean-square (RMS) delay spread and coherence bandwidth for the two frequency bands, while the path loss at 14.6 GHz band is clearly larger than that at the 3.6 GHz band. Furthermore, the underlying factors that influence the channel characteristics are investigated. It is found that the RMS delay spread is independent of the frequency in the scenario where free space propagation and/or reflection are the main mechanisms. Measurements in the reverberation chamber verify this inference.

The need for improving the safety and the efficiency of transportation systems has become of extreme importance. In this regard, the concept of vehicle-to-X (V2X) communication has been introduced with the purpose of providing wireless communication technology in vehicular networks. Not like the traditional views, the wide-sense V2X (WSV2X) communications in this paper are defined by including not only vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications but also train-to-X (T2X) communications constituted of train-to-train (T2T) and train-to-infrastructure (T2I) communications. All the information related to the wide-sense V2X channels, such as the standardization, scenarios, characters, and modeling philosophies, is organized and summarized to form the comprehensive understanding of the development of the WSV2X channels.

Along with the increase of the use of working frequencies in advanced radio communication systems, the near-region inside tunnels lengthens considerably and even occupies the whole propagation cell or the entire length of some short tunnels. This paper analytically models the propagation mechanisms and their dividing point in the near-region of arbitrary cross-sectional tunnels for the first time. To begin with, the propagation losses owing to the free space mechanism and the multimode waveguide mechanism are modeled, respectively. Then, by conjunctively employing the propagation theory and the three-dimensional solid geometry, the paper presents a general model for the dividing point between two propagation mechanisms. It is worthy to mention that this model can be applied in arbitrary cross-sectional tunnels. Furthermore, the general dividing point model is specified in rectangular, circular, and arched tunnels, respectively. Five groups of measurements are used to justify the model in different tunnels at different frequencies. Finally, in order to facilitate the use of the model, simplified analytical solutions for the dividing point in five specific application situations are derived. The results in this paper could help deepen the insight into the propagation mechanisms in tunnels.

Radio wave propagation scene partitioning is necessary for wireless channel modeling. As far as we know, there are no standards of scene partitioning for high-speed rail (HSR) scenarios, and therefore we propose the radio wave propagation scene partitioning scheme for HSR scenarios in this paper. Based on our measurements along the Wuhan-Guangzhou HSR, Zhengzhou-Xian passenger-dedicated line, Shijiazhuang-Taiyuan passenger-dedicated line, and Beijing-Tianjin intercity line in China, whose operation speeds are above 300 km/h, and based on the investigations on Beijing South Railway Station, Zhengzhou Railway Station, Wuhan Railway Station, Changsha Railway Station, Xian North Railway Station, Shijiazhuang North Railway Station, Taiyuan Railway Station, and Tianjin Railway Station, we obtain an overview of HSR propagation channels and record many valuable measurement data for HSR scenarios. On the basis of these measurements and investigations, we partitioned the HSR scene into twelve scenarios. Further work on theoretical analysis based on radio wave propagation mechanisms, such as reflection and diffraction, may lead us to develop the standard of radio wave propagation scene partitioning for HSR. Our work can also be used as a basis for the wireless channel modeling and the selection of some key techniques for HSR systems.