This paper presents a novel framework for object-oriented integrated sensing and communications (ISAC) channel modeling in urban environments over low-altitude 3D space, which jointly considers unmanned aerial vehicle (UAV)-based and ground-level measurements in conjunction with ray-tracing (RT) technology. Two types of object-oriented channels are considered, namely the static background channel, e.g., buildings, and the dynamic channel, e.g., cars and UAVs. Firstly, a systematic measurement campaign was conducted in the 4 GHz band at 80 m height using a UAV and at ground level using a vehicle. Additionally, photographs of the measurement area are taken by the UAV for an object-oriented 3D model. Based upon channel measurement data and employing RT techniques, the EM parameters of the considered coverage area are accurately calibrated and validated at both heights. This has allowed us to obtain accurate simulations across the vertical airspace of both sensing- and communication-background channels, followed by a comprehensive channel characteristics analysis. By subtracting the background channel from the dynamic channel, experimental results have demonstrated that the angle and distance information of objects can be accurately estimated. Through a case study, it has been shown that the acquired prior scene information can be utilized as a reference to improve object-oriented coverage.

Integrating dense channel fingerprints into deep learning (DL) becomes a promising way to realize precise three-dimensional (3D) indoor localization. However, most existing methods are frequency-dependent, which limits the localization precision when operating in different frequency bands. To address this challenge, this paper proposes a masked Transformer encoder (MTE) model capable of using the channel state information (CSI) data of an arbitrary number of sub-channels (frequency bands) as input. The proposed MTE model can locate a UE using frequency-scalable CSI data, to realize resilient localization. We first introduce how to transform CSI data into sequential data suitable for Transformer-based models, with length of the sequence determined by the number of sub-channels. Based on this, an MTE model is designed to achieve resilient FP localization with frequency-scalability, i.e., capable of processing the CSI data of an arbitrary number of sub-channels. Next, we construct a 3D CSI FP dataset using ray-tracing (RT) simulations based on real-world indoor scenarios and versatile electromagnetic (EM) coefficients. The reliability of the dataset is verified by measurement data. Extensive experiments demonstrate that the MTE model outperforms many state-of-the-art baselines, classical time-series models, and alternative Transformer-based methods, especially under arbitrary sub-channel CSI data. Moreover, we demonstrate that the MTE model also offers many advantages in terms of training and storage costs through comparisons with conventional models.

Accurate prediction of rain attenuation in short-range millimeter-wave (mmWave) wireless links is critical for ensuring reliable performance under rainy conditions. This study presents a measurement-based model to precisely characterize the impact of rainfall on wireless channels. A dedicated measurement system is developed to capture channel responses under controlled rainfall conditions. Based on the measurement data and the framework of the International Telecommunication Union Radiocommunication Sector (ITU-R) Recommendation P.838-3 model, the proposed model optimizes model parameters and incorporates wet antenna effects. Comparison with the ITU model demonstrates that the proposed model improves attenuation prediction accuracy by over 70% for short-range mmWave links, while cross-band validation confirms its applicability. The proposed model provides reliable technical support for wireless system design in complex meteorological environments.

Accurate modeling of wireless channels is crucial for the design and optimization of railway wireless communication systems. Ray-tracing (RT) is a widely used technique for generating multipath channel characteristics, but the high computational resource requirements make it difficult to meet real-time demands. To address this issue, a super-resolution (SR) model based on the Graph Neural Network (GNN) for generating multipath channel data is proposed. The model leverages the ability of GNNs to process graph-structured data, mapping the multipath propagation process to the graph representation. RT generates low-resolution multipath data, which is then improved by the GNN-based SR model to produce high-resolution channel characteristics. Experimental results in the railway scenario demonstrate that the proposed method significantly reduces computation time while maintaining high accuracy. The GNNbased SR model performs excellently in power, spatial, and angular domains, providing a more efficient and accurate solution for railway wireless communication systems.

Fingerprint-based localization methods are regarded as a promising solution in sixth-generation (6G) wireless communication because of their ubiquitous infrastructure and high precision in lab-level experiments. However, received signal strength (RSS) instability and fingerprint spatial ambiguity (FSA) significantly undermine the precision of localization methods under real-world applications. Signal spatial difference (SSD) is one of the approaches which can effectively address RSS instability and FSA in fingerprint localization. However, few of these approaches are effectively incorporated into localization methods. In this paper, we propose an SSD-based advantage actor-critic (A2C) method, SSD-A2C, for 3D indoor localization. This method is the first to combine SSD and reinforcement learning (RL), achieving a highly efficient method that can address FSA and RSS instability. A 3D indoor localization simulation environment is developed based on ray-tracing (RT) simulation results of two real-world indoor scenarios, and the proposed method is trained and evaluated by it. Experiment results proved the excellent performances of SSD-A2C in minor localization errors and a high success rate of predicting the desired location. SSD is found to be more appropriate than RSS for RL. The strengths, shortcomings, and future research directions of the proposed method are also discussed in this paper.

This article presents a new sensing channel modeling approach by jointly considering ray-tracing (RT) and stochastic methods, to accurate and efficient model sensing channels for vehicle-to-everything (V2X) applications. For the former, moving targets are modeled through accurate RT simulations while for the latter a statistical approach is used for generating complex environmental clutter by emphasizing for the first time individual object modeling. This approach is used to form a feature library of objects which ensures space-time consistency while significantly improving the modeling speed. The channel transfer functions generated by RT and stochastic methods are jointly considered through coherent superposition to form a more complete sensing channel which includes both clutters and targets. To verify its effectiveness and accuracy, a comprehensive experimental study has been conducted taking systematic measurements using a 77-GHz mmWave radar as it is the prevalent equipment for sensing used for intelligent driving applications. We have considered a typical V2X scenario, with the radar deployed on vehicles traveling along roads at an urban intersection. The experimental results obtained have demonstrated that the accuracy in target distance detection and velocity estimation has improved leading to errors of less than 0.5 m and less than 0.2 m/s, respectively, while for clutter modeling, the error of power is 3 to 6 dB. Moreover, compared to traditional RT methods, the proposed approach is 20 times faster. Through the proposed approach, realistic sensing channel data can be obtained in a systematic, effective, and accurate manner, facilitating research of sensing-assisted communication applications.

Localization and sensing inputs are enabling new and improved Internet of Things (IoT) applications and have been studied extensively over the past decade. Unfortunately, many solutions focus primarily on improving localization performance, which in turn reduces communication capabilities. Driven by the Integrated Sensing and Communication (ISAC) aspects, we investigate how localization functionality can be seamlessly integrated into a IEEE 802.15.4 TSCH communication protocol. We present two new methods of phase-based ranging that estimate the distance between two devices with each transmitted data packet. We further analyze the effects of introduced changes on communication, their power consumption, and ranging operability. We improve the state-of-the-art algorithm of phase-based distance estimation by reducing the number of phase samples required, without reducing its accuracy and sensitivity while increasing the energy efficiency.

The Industrial Internet of Things (IIoT) enables machines to communicate robustly. High reliability, high throughput, and low latency are the critical capabilities of IIoT, which have posed great challenges to existing wireless solutions for industrial applications. Due to the vast available bandwidth, the emerging millimeter-wave (mmWave) technology is promising to address this bottleneck. However, the propagation behaviors at such high frequencies in the harsh industrial environment have yet been well understood. In this work, extensive measurements have been conducted in a representative industrial application scenario using a 2-GHz wideband directional channel sounder in the 28-GHz mmWave band. By exploiting the measurement with excellent resolution, the multipath components’ (MPCs) delay-angular space is transformed onto the scatter points (SPs) in the propagation environment. An effective clustering algorithm is then proposed to cluster the SPs without prior knowledge and iterations. Through a geometrical optics analysis, the SP clusters are classified corresponding to the reflectors. By doing this, the cluster-generating reflectors are reduced to a quasi-deterministic (QD) channel model that ensures spatial consistency and MPCs’ stochastic dispersion. Finally, it is shown that the measurement data agrees well with the proposed QD model, indicating the high fidelity of the proposed model.

This paper presents systematic experimental and related analytical studies for the accurate channel modeling of vehicle-to-vehicle (V2V) radio channels in mountainous plateau environments (MPEs) at the 376MHz band which is allocated for emergency communications. The measurement campaign has been conducted in the Ailao Mountains (Yunnan Province of China) and the influence of this mountainous environment on the radio channel characteristics in the power domain has been thoroughly investigated. The time-varying characteristics of the measured V2V radio channels have identified the stationarity time based on the local scattering function as a prerequisite for accurate modeling. Furthermore, Akaike’s information criteria and Kolmogorov-Smirnov tests have revealed that the Weibull probability distribution function is the best fit for the measured data. With the Weibull shaping parameter, the line of sight (LOS) and non-LOS (NLOS) channels can be conveniently and accurately classified. As large variations of the LOS probability and the shape parameter over long communication distances and LOS/NLOS conditions have been observed, an analytical approach using a 3rd order sum of sinusoid functions has been used to model them. Through this model, the maximum distance for LOS communications has been derived directly from the measured data. The large-scale propagation properties, including path loss, shadow fading, and shadow fading correlation, have been thoroughly studied, and accurate path loss models specifically for the MPEs have been proposed. Moreover, a new shadow fading correlation model has been presented. Compared to previously known models, this model is more accurate and more suitable for long-distance emergency communication scenarios.

With the emergence of communication services with stringent requirements such as autonomous driving or on-flight Internet, the sixth-generation (6G) wireless network is envisaged to become an enabling technology for future transportation systems. In this paper, two ways of interactions between 6G networks and transportation are extensively investigated. On one hand, the new usage scenarios and capabilities of 6G over existing cellular networks are firstly highlighted. Then, its potential in seamless and ubiquitous connectivity across the heterogeneous space-air-ground transportation systems is demonstrated, where railways, airplanes, high-altitude platforms and satellites are investigated. On the other hand, we reveal that the introduction of 6G guarantees a more intelligent, efficient and secure transportation system. Specifically, technical analysis on how 6G can empower future transportation is provided, based on the latest research and standardization progresses in localization, integrated sensing and communications, and security. The technical challenges and insights for a road ahead are also summarized for possible inspirations on 6G enabled advanced transportation.

Millimeter-wave (mmWave) automotive radar can detect the surrounding environment using highly directional, high-frequency electromagnetic waves. With these advantages, mmWave radar has become an important component of autonomous driving and integrated sensing systems. Designing systems and sensing algorithms requires the application of realistic channel models and simulation. In this paper, the propagation channel is measured, characterized, and modeled for mmWave automotive radar with typical configurations in scenarios. Based on the channel measurements in urban street and expressway environments, ray-tracing (RT) technology is verified for modeling important objects regarding radar cross-section and echo power. A hybrid channel model is proposed by integrating RT with the stochastic modeling of targets and surrounding environments, which significantly improves simulation efficiency and provides more flexibility for virtual tests in various complex environments.

Vehicular communication and sensing technologies are key to enabling 6G Intelligent Autonomous Transportation Systems (IATS). With the introduction of massive sensors and artificial intelligence (AI) fusion applications, IATS is needed to support data transmission rates up to 10 Gb/s. Millimeter-wave (mmWave) technology has attracted extensive attention owing to abundant spectrum resources, which can support the timely transmission of massive data. However, performance degradation of mmWave due to signal blockage has become one of the critical technical challenges. Road bridges as one of the common obstacles in urban scenarios, which has severe blockage effects on communication links. Therefore, this paper comprehensively studies the impact of road bridge blockage effects on mmWave vehicle-to-infrastructure (V2I) links and proposes an empirical model that can accurately characterize the bridge blockage effect. First, we use a self-developed mmWave channel sounder to carry out channel measurements on typical urban roads. Measurement results indicate that a maximum extra propagation loss of up to 23 dB is caused by road bridges. In addition, to address the deficiencies of existing propagation prediction models, the Single Road Bridge (SRB) model is proposed in this work. This model reveals for the first time the extra propagation loss caused by the road bridge to the channel. Compared with existing models, the SRB model can make the mean absolute error (MAE) and root mean square error (RMSE) within 5 dB. The proposed SRB model is of great value for accurately simulating real-world road bridge blockage events when designing future IATS.

The terahertz (THz) band (0.1-10 THz) is widely considered to be a candidate band for the sixth-generation mobile communication technology (6G). However, due to its short wavelength (less than 1 mm), scattering becomes a particularly significant propagation mechanism. In previous studies, we proposed a scattering model to characterize the scattering in THz bands, which can only reconstruct the scattering in the incidence plane. In this paper, a three-dimensional (3D) stochastic model is proposed to characterize the THz scattering on rough surfaces. Then, we reconstruct the scattering on rough surfaces with different shapes and under different incidence angles utilizing the proposed model. Good agreements can be achieved between the proposed model and full-wave simulation results. This stochastic 3D scattering model can be integrated into the standard channel modeling framework to realize more realistic THz channel data for the evaluation of 6G.

The smart home integrates home-related facilities, which can greatly facilitate people’s life. To support applications such as breath detection and health monitoring for the smart home, it is significant to understand the effect of the human body on the characteristics of the wireless channel. In this paper, the channel characteristics for human exhalation and inhalation at 6.5 GHz are measured and analyzed. Based on the measurement results in the living room scenario, the ray tracing (RT) simulator is calibrated and used to analyze the continuous process of exhalation and inhalation. According to the measurement and simulation results, the effect of human exhalation and inhalation processes on the power and phase of the received signal is studied. The analysis of this paper could be useful in guiding the deployment of smart home devices for human body monitoring. The calibrated electromagnetic (EM) parameters of typical indoor materials will support RT-based channel simulation and modeling in indoor scenarios.

To realize intelligent connection of everything and the digital twin (DT) of the physical world in 6G and beyond, new communication and sensing solutions are demanded. The potential of multiple spectrums is maximized for various applications and scenarios. In such a context, an accurate, efficient, and pervasive multi-spectrum propagation model is needed as a critical and unified baseline for testing the performance of the solutions in various scenarios. This work presents ray-tracing (RT) oriented methods for the DT presentation of radio propagation at multiple frequency bands from microwave to visible light. The material- and field-measurement-based approaches are proposed to characterize the electromagnetic properties of materials. On that basis, the propagation mechanisms are developed and validated, and the corresponding parameters are inverted. For the real-time simulation demand, RT and artificial intelligence (AI) algorithms are fused to develop a super-resolution modeling method. The experimental results indicate that the proposed method outperforms the baseline model regarding stability and accuracy. It can significantly reduce the computation time with comparable accuracy to the RT-only approach. The proposed methodologies and the in-depth discussions in this work are expected to pave the way to realize the DT of multi-spectrum propagation for evaluating 6G and beyond technologies.

Millimeter-wave automotive radar is essential for realizing autonomous driving. Realistic channel model and simulation are important for system and sensing algorithm design. This work introduces channel measurements and ray-tracing (RT) channel simulations for frequency modulated continuous wave (FMCW) automotive mmWave radar. The 77 GHz channel measurements in an urban crossroads environment are presented. The dominant echoes (multi-path components) of the measurement are detected and matched with corresponding objects for each frame. A measurement-based electromagnetic (EM) parameter estimation method is proposed to find the optimal parameter set that minimizes the error of simulated radar cross section (RCS). As a result, the issue of lacking reliable EM material parameters for RT simulation at 77 GHz frequency band is tackled. Simulation results are validated in power, range, velocity, and angle domains with the provided EM parameter set. Extending validated RT simulations in similar environments with various configurations allows more reliable channels for rigorous testing without the limitation of channel measurement.

Channel modeling has always been the core part in communication system design and development, especially in 5G and 6G era. Traditional approaches like stochastic channel modeling and ray-tracing (RT) based channel modeling depend heavily on measurement data or simulation, which are usually expensive and time consuming. In this paper, we propose a novel super resolution (SR) model for generating channel character-istics data. The model is based on multi-task learning (MTL) convolutional neural networks (CNN) with residual connection. Experiments demonstrate that the proposed SR model could achieve excellent performances in mean absolute error and standard deviation of error. Advantages of the proposed model are demonstrated in comparisons with other state-of-the-art deep learning models. Ablation study also proved the necessity of multi-task learning and techniques in model design. The contribution in this paper could be helpful in channel modeling, network optimization, positioning and other wireless channel characteristics related work by largely reducing workload of simulation or measurement.

The vision of smart maritime requires a seamless high-data rate wireless connectivity, which can be realized by the fifth-generation communication system (5G). As one of the most widely used 5G frequency spectrum, 3.5 GHz is a promising candidate band to provide high-quality wireless coverage inside ships. Hence, in this paper, the wireless channel in the intra-ship scenario at 3.5 GHz is characterized through extensive ray-tracing (RT) simulations. Channel parameters are extracted and analysed in terms of root-mean-square delay spread (RMS DS), Rician$K$-factor (KF), azimuth/elevation angular spread of arrival/departure (ASA, ASD, ESA, ESD), cross-polarization ratio (XPR), and their cross-correlations. Based on these results, we provide valuable insights into the system design and evaluation for the 5G in the intra-ship scenario.

Abstract This paper investigates an advanced effective signal processing technique to suppress noise, addressing a modern high‐performance detection in the field of radar sensing. To achieve a higher accuracy, the frequency modulated continuous wave radar is taken as a case study to derive the algorithm based on Karhunen ‐ Loève transform (KLT) before detection. KLT defines a linear projection of the signal statistics on the eigenfunctions domain, which makes the input‐dependent signals orthogonal to each other under new eigen‐basis and eigenvalues. The highest energy along slow time dimension of each range bin is concentrated in the transformed domain corresponding to the largest N eigenvalues. The performance of the algorithm is evaluated by different eigenvalue selection strategies. Numerical experiments are employed to obtain the relationship between signal‐to‐noise ratio and different eigenvalue selection strategies. Pertaining to the detection performance, constant false alarm ratio detector is applied to demonstrate the detection ability as a result of the processor by use of probability of detection ( P d ).

TeraHertz (THz) communications can satisfy the high data rate demand with massive bandwidth. However, severe path attenuation and hardware imperfection greatly alleviate its performance. Therefore, we utilize the reconfigurable intelligent surface (RIS) technology and investigate the RIS-aided THz communications. We first prove that the small-scale amplitude fading of THz signals can be accurately modeled by the fluctuating two-ray distribution based on two THz signal measurement experiments conducted in a variety of different scenarios. To optimize the phase-shifts at the RIS elements, we propose a novel swarm intelligence-based method that does not require full channel estimation. We then derive exact statistical characterizations of end-to-end signal-to-noise plus distortion ratio (SNDR) and signal-to-noise ratio (SNR). Moreover, we present asymptotic analysis to obtain more insights when the SNDR or the number of RIS’s elements is high. Finally, we derive analytical expressions for the outage probability and ergodic capacity. The tight upper bounds of ergodic capacity for both ideal and non-ideal radio frequency chains are obtained. It is interesting to find that increasing the number of RIS’s elements can significantly improve the THz communications system performance. For example, the ergodic capacity can increase up to 25% when the number of elements increases from 40 to 80, which incurs only insignificant costs to the system.

For reliable and efficient communications of aerial platforms, such as unmanned aerial vehicles (UAVs), the cellular network is envisioned to provide connectivity for the aerial and ground user equipment (GUE) simultaneously, which brings challenges to the existing pattern of the base station (BS) tailored for ground-level services. Thus, we focus on the coverage probability analysis to investigate the coexistence of aerial and terrestrial users, by employing realistic antenna and channel models reported in the 3rd Generation Partnership Project (3GPP). The homogeneous Poisson point process (PPP) is used to describe the BS distribution, and the BS antenna is adjustable in the down-tilted angle and the number of the antenna array. Meantime, omnidirectional antennas are used for cellular users. We first derive the approximation of coverage probability and then conduct numerous simulations to evaluate the impacts of antenna numbers, down-tilted angles, carrier frequencies, and user heights. One of the essential findings indicates that the coverage probabilities of high-altitude users become less sensitive to the down-tilted angle. Moreover, we found that the aerial user equipment (AUE) in a certain range of heights can achieve the same or better coverage probability than that of GUE, which provides an insight into the effective deployment of cellular-connected aerial communications.

Unmanned aerial vehicles (UAVs) have aroused great attention for future wireless communications since the drone can be used as an aerial base station and also as a cellular user to perform on-demand tasks. Because the reliable and robust connection between UAV and ground station (GS) plays an essential role in the UAV-based air-to-ground (AG) communications, the accurate radio channel model is needed. In this paper, we conducted AG wideband radio channel measurements in a hilly environment at 6.5 GHz with a bandwidth of 500 MHz, which is one of the high bands for ultra-wideband (UWB) communications in IEEE standards. The large-scale characteristics, including path loss and shadow fading, are represented by the close-in (CI) model. The small-scale characteristics that illustrate the fading and the multipath effect are analyzed with three critical parameters, including the small-scale fading, Rician K-factor and root-mean-square (RMS) delay spread. Afterward, the tapped delay line (TDL) model is built according to the detected multipath in the power delay profile (PDP), and the channel impulse response (CIR) is discussed for wideband channel modeling. The results can be used for the design and analysis of the AG systems.

A method is proposed to estimate the direction of a ground radio-frequency (RF) transmitter by using an Unmanned Aerial Vehicle (UAV) equipped with a single antenna, which is critical when considering the form factor and computational capabilities of a UAV. By considering the received signal at several locations along its trajectory, the UAV receiver implicitly creates a virtual multi-antenna array (VMA), which can estimate the direction-of-arrival (DOA) of the transmitter. The major difficulty is the Local Oscillator (LO) frequency offset that occurs between the transmitter and the UAV receiver, which adds a cumulative phase offset to the received signal at each antenna of the virtual array. Oscillators of inferior qualities will undergo severe phase and frequency drifts over time, and these LO offsets must be estimated and compensated during DOA estimation. To overcome this difficulty, we proposed two approaches by estimating the LO frequency offset jointly with the direction of the transmitter. Then we extend the Multiple Signal Classification (MUSIC) algorithm to perform multidimensional estimation (including azimuth and elevation). In this paper, the proposed VMA method is simulated and tested by considering different virtual array geometries and various LO qualities. Simulation results prove the feasibility of our proposed method, and the median estimation error for azimuth and elevation are below 9° and 12° on average, even with low-quality oscillators.

The millimeter wave (mmWave) communication with large bandwidth is a key enabler for both the fifth-generation mobile communication system (5G) and smart rail mobility. Thus, in order to provide realistic channel fundamental, the wireless channel at 22.6 GHz is characterized for a typical high-speed railway (HSR) environment in this paper. After importing the three-dimensional environment model of a typical HSR scenario into a self-developed high-performance cloud-computing Ray-Tracing platform - CloudRT, extensive raytracing simulations are realized. Based on the results, the HSR channel characteristics are extracted and analyzed, considering the extra loss of various weather conditions. The results of this paper can help for the design and evaluation for the HSR communication systems enabling smart rail mobility.

Powered by the Internet of Things (IoT), the cellular, vehicular, and other emerging networks, such as space-air-ground integrated networks, are expected to comprehensively evolve into the new era of the Internet of Everything (IoE) in which the propagation links between the IoT devices or sensors become diversified. Thus, a general propagation channel model is required. The line-of-sight (LOS) probability plays an essential role in the channel modeling, especially for built-up environments where the blockages from buildings are unfavorable to the signal transmission. The 4-D LOS probability model with considering the 3-D environment and frequency is comprehensively investigated in this article. Using the geometry-based stochastic method, the LOS probability is derived for arbitrary sizes, heights, and orientations of buildings in finely defined urban scenarios. The major contribution is the universality, simplicity, and good extension of the proposed model with comprehensive considerations of the influence of frequency, the type of urban, and the height of transceiver. The simulation results show that the propagation with higher frequency has a higher LOS probability. Moreover, the size, height, and density of building in urban environments are closely related to the LOS probability. The good agreements of the comparisons with the ray-tracing (RT) model and standard models show the accuracy of our proposed model. These results will be useful in the modeling of various channels and the design of IoT wireless communications systems.

Intelligent Transportation System (ITS) is more and more crucial in the modern transportation field, such as the applications of autonomous vehicles, dynamic traffic light sequences, and automatic road enforcement. As the upcoming fifth-generation mobile network (5G) is entering the deployment phase, the idea of cellular vehicle-to-everything (C-V2X) is proposed. The same 5G networks, coming to mobile phones, will also allow vehicles to communicate wirelessly with each other. Hence, 3.5 GHz, as the main sub-6 GHz band licensed in 5G, is focused in our study. In this paper, a comprehensive study on the channel characteristics for vehicle-to-infrastructure (V2I) link at 3.5 GHz frequency band is conducted through channel measurements and ray-tracing (RT) simulations. Firstly, the channel parameters of the V2I link are characterized based on the measurements, including power delay profile (PDP), path loss, root-mean-square (RMS) delay spread, and coherence bandwidth. Then, the measurement-validated RT simulator is utilized to conduct the simulations in order to supplement other channel parameters, in terms of the Ricean K-factor, angular spreads, the cross-correlations of abovementioned parameters, and the autocorrelation of each parameter itself. This work is aimed at helping the researchers understand the channel characteristics of the V2I link at 3.5 GHz and support the link-level and system level design for future vehicular communications of 5G.

The use of unmanned air vehicles (UAVs) as "flying base stations" is gaining significant traction in the scientific and industrial community. Such UAV-based wireless networks could be quickly deployed to increase the range of a network or to replace a damaged infrastructure in emergencies. One crucial requirement of UAV-based communication networks is understanding the distinctive features of UAV propagation channels, especially the angular spreads and delay spreads that dictate the performances of Orthogonal Frequency Division Multiplexing (OFDM) technology and Multiple-Input Multiple-Output (MIMO) system. In this paper, we analyze the angular spreads and delay spreads of the air-to-ground wireless channels based on extensive ray-tracing simulations. Our results indicate that, contrary to expectations, the angular spreads and the delay spreads at the ground-based Mobile Terminals (MTs) are more substantial in Line-of- Sight (LoS) conditions than in Non-Line-of-Sight (NLoS) conditions, which can be explained by the particular geometry of the air-to-ground channels. These results also make meaningful suggestions for the UAV-based communication system design.

We show that the deterministic two-ray model and the statistical two-wave with diffuse power (TWDP) model are suitable channel models for millimeter wave train-to-infrastructure wireless communications. Both models make parsimonious use of model parameters and are therefore implementable with very low complexity. Once directive antennas are employed, such models turn out to be accurate. The wireless channel is mainly dominated by the line-of-sight component and a ground reflection then. These two components lead to an oscillatory interference pattern with instantaneous frequency defined by the geometry. We derive fading envelopes to find a simple parametrization of the TWDP model through the deterministic two-ray model. The effectiveness of our approach is demonstrated on ray-tracing data.

Powered by the Internet of Things, the vehicular ad-hoc networks are expected to evolve into the Internet of Intelligent Vehicles in which each vehicle can be much more efficient in various vehicular and transportation applications. In order to realize this vision, a seamless low-latency and ultrareliable vehicle-to-vehicle (V2V) communication network is required. Thus, it is of importance to characterize the V2V channels in various realistic environments, especially when the line-of-sight between transmitter (Tx) and receiver (Rx) is obstructed. In this paper, we characterize obstructed V2V channels in the 5-GHz band through measurement-calibrated ray-tracing (RT) simulations. To begin, the main objects in the real world are divided into two groups: 1) the small-scale structures (e.g., lampposts, traffic signs, etc.) and 2) the large-scale structures (such as buildings and ground). Then, we integrate the radar cross sections of the small-scale structures into our RT simulator through a framework based on high frequency prediction techniques. For the large-scale structures, we calibrate the electromagnetic and scattering parameters of the large-scale structures through V2V channel measurements. After such integration and calibration, extensive RT simulations for V2V channels with Tx and Rx located on vehicles traveling in the opposite or same direction are realized with various antenna deployments in urban and open space environments, with and without sloped terrain. Based on the RT results, we characterize the path loss, shadow fading, and delay spread of the channel for each case, and show agreement with measured results in the literature for all these channel characteristics.

The terahertz (THz) communication utilizes the frequency spectrum above 300 GHz and is widely considered as a promising solution to the future high-speed short-range wireless communication beyond millimeter wave communication. While providing tens of gigahertz bandwidth, it is subjected to high-propagation path loss, inter-symbol, and inter-user interferences. The massive multiple-input-multiple-output (MIMO) can be applied to address these problems by cooperation between many access point antennas. However, the THz channel characteristics, including high-propagation path loss, frequency selectivity, and a big number of samples per channel impulse response, require carefully tailored algorithm for massive MIMO signal processing. In this paper, we propose a single carrier minimum mean square error precoding and detection algorithm for frequency selective THz channels. The MIMO signal transmission is described with the block matrices. A gain control heuristic is introduced to reduce the complexity. The sparsity property of the channel is utilized to construct sparse channel matrices and the least square QR algorithm is applied to efficiently solve the problems. Besides the uniform antenna array, the hybrid array consisting of several subarrays is considered as well. The simulation results show that the massive MIMO array can provide a satisfactory performance in terms of bit error rate.

A measurement campaign has been carried out in a reference indoor environment - a medium-size meeting room - using an ultra-wideband channel sounder operating at 3 different frequency bands: 10 GHz, 60 GHz and 300 GHz. Automatic rotational units are used at Tx and Rx side to perform a full horizontal scanning with directional antennas, in order to achieve a double-directional channel characterization. Preliminary results show that multipath is mainly affected by few single or double-bounce components, and significant contributions may come from objects other than the walls, e.g. TV monitor and entrance door. Most of the contributions are clearly identifiable at all the considered frequencies, although they appear to be stronger at 60 GHz compared to 10 GHz. The radio channel's multipath richness appears to be frequency dependent, and in particular it decreases when comparing 60 GHz to 300 GHz.

For real-world performance evaluation, channel models should be accurate in reflecting a realistic behavior between transmitter and receiver. A major concern with the actual standardized channel models, is that they do not consider the time evolution and are relevant only for drop based simulations. The need for spatial consistency of these channel models has been also acknowledged by standardization bodies, e.g., 3GPP, and alternative models are under discussion. In this paper, we compare the statistics generated with the 3GPP 3D channel model to those of ray tracing simulations and validate our method to achieve spatial consistency. Moreover, we estimate the correlation parameters such that the results obtained with our method match the ray-tracing results.

More people prefer to using rail traffic for travel or for commuting due to its convenience and flexibility. As the record of the maximum speed of rail has been continuously broken and new applications are foreseen, the high-speed railway (HSR) communication system requires higher data rate with seamless connectivity, and therefore, the system design faces new challenges to support high mobility. Millimeter-wave (mmWave) technologies are considered as candidates to provide wideband communication. However, mmWave is rarely explored in HSR scenarios. In this paper, channel characteristics are studied in the 5G mmWave band for typical HSR scenarios, including urban, rural, and tunnel, with straight and curved route shapes. Based on the wideband measurements conducted in the tunnel scenario by using the “mobile hotspot network” system, a 3-D ray tracer (RT) is calibrated and validated to explore more channel characteristics in different HSR scenarios. Through extensive RT simulations with 500-MHz bandwidth centered at 25.25 GHz, the power contributions of the multipath components are studied, and the dominant reflection orders are determined for each scenario. Path loss is analyzed, and the breakpoint is observed. Other key parameters, such as Doppler shifts, coherence time, polarization ratios, and so on, are studied. Suggestions on symbol rate, sub-frame bandwidth, and polarization configuration are provided to guide the 5G mmWave communication system design in typical HSR scenarios.

Heterogeneous cellular networks (HCNs) with millimeter wave (mm-wave) communications included are emerging as a promising candidate for the fifth generation mobile network. With highly directional antenna arrays, mm-wave links are able to provide several Gbps transmission rate. However, mm-wave links are easily blocked without line of sight. On the other hand, device to device (D2D) communications have been proposed to support many content-based applications and need to share resources with users in HCNs to improve spectral reuse and enhance system capacity. Consequently, an efficient resource allocation scheme for D2D pairs among both mm-wave and the cellular carrier band is needed. In this paper, we first formulate the problem of the resource allocation among mm-wave and the cellular band for multiple D2D pairs from the view point of game theory. Then, with the characteristics of cellular and mm-wave communications considered, we propose a coalition formation game to maximize the system sum rate in statistical average sense. We also theoretically prove that our proposed game converges to a Nash-stable equilibrium and further reaches the near-optimal solution with fast convergence rate. Through extensive simulations under various system parameters, we demonstrate the superior performance of our scheme in terms of the system sum rate compared with several other practical schemes.

Angle of arrival (AoA) estimation is required by adaptive directive antennas in order to realize a high antenna gain, which is necessary for future indoor terahertz communications due to its extremely high path loss. This paper proposes an AoA estimation algorithm using belief propagation in a dynamic scenario, where the user equipment (UE) is moving during the data transmission, based on the space-time correlations of AoA change. The temporal correlation of AoA change is due to the limited moving speed and statistical movement pattern. Furthermore, if we have distributed antennas or hybrid massive multiple-input-multiple-output (MIMO) array, the AoA changes of different antennas reveal spatial correlation because all the AoA changes are caused by the same spatial displacement of UE. This spatial correlation is utilized in this paper to further improve the estimation accuracy by passing messages between antennas and combining the intrinsic estimate by each antenna and extrinsic information from other antennas. In order to demonstrate the algorithm performance, a distributed antenna system and a hybrid massive MIMO array (an array of multiple directive antenna arrays) are considered as application scenarios. The simulation results show that the cooperative estimation brings significant advantage in both scenarios in respect of mean effective antenna gain and level crossing rate.

In this paper, we present a measurement campaign of indoor massive MIMO channels by using a wideband channel sounder and a 64-element virtual linear array. The measurements are conducted at 6 GHz, with a bandwidth of 200 MHz. Both the light-of-sight (LOS) and obstructed-line-of-sight (OLOS) scenarios are considered in measurements. The Frequency Domain Space-Alternating Generalized Expectation maximization algorithm (FD-SAGE) is used to determine the channel characteristics in angular domain. In order to validate the obtained FD-SAGE estimates, the power azimuth spectrum (PAS) has been calculated using the Bartlett Beamformer (BBF). The non-stationarity of radio channels, which is reflected by power of the estimated MPCs, azimuth of departure (AOD), and azimuth of arrival (AOA), is discussed. The direction spread is estimated, which reflects spatial dispersion of wireless channel. It is also found that the diffuse scattering components at 6 GHz in OLOS scenario is richer than that in LOS scenario, and the magnitude of the diffuse scattering components are comparable with the specular components. It is suggested that the non-stationarity of the diffuse scattering components should not be neglected in the indoor massive MIMO channel modeling at the frequency bands below 6 GHz.

The millimeter wave (mmWave) communications and massive multiple-input multiple-output (MIMO) are both widely considered to be the candidate technologies for the fifth generation mobile communication system. It is thus a good idea to combine these two technologies to achieve a better performance for large capacity and high data-rate transmission. However, one of the fundamental challenges is the characterization of mmWave massive MIMO channel. Most of the previous investigations in mmWave channel only focus on single-input single-output links or MIMO links, whereas the research of massive MIMO channels mainly focus on a frequency band below 6 GHz. This paper investigates the channel behaviors of massive MIMO at a mmWave frequency band around 26 GHz. An indoor mmWave massive MIMO channel measurement campaign with 64 and 128 array elements is conducted, based on which, path loss, shadow fading, root-mean-square (RMS) delay spread, and coherence bandwidth are extracted. Then, by using our developed ray-tracing simulator calibrated by the measurement data, we make the extensive ray-tracing simulations with 1024 antenna elements in the same indoor scenario, and get insights into the variation tendency of mean delay and the RMS delay with different array elements. It is observed that the measurement and the ray-tracing-based simulation results have reached a good agreement.

We present a measurement campaign to characterize an indoor massive multiple-input multiple-output (MIMO) channel system, using a 64-element virtual linear array, a 64-element virtual planar array, and a 128-element virtual planar array. The array topologies are generated using a 3D mechanical turntable. The measurements are conducted at 2, 4, 6, 11, 15, and 22 GHz, with a large bandwidth of 200 MHz. Both line-of-sight (LOS) and non-LOS (NLOS) propagation scenarios are considered. The typical channel parameters are extracted, including path loss, shadow fading, power delay profile, and root mean square (RMS) delay spread. The frequency dependence of these channel parameters is analyzed. The correlation between shadow fading and RMS delay spread is discussed. In addition, the performance of the standard linear precoder—the matched filter, which can be used for intersymbol interference (ISI) mitigation by shortening the RMS delay spread, is investigated. Other performance measures, such as entropy capacity, Demmel condition number, and channel ellipticity, are analyzed. The measured channels, which are in a rich-scattering indoor environment, are found to achieve a performance close to that in independent and identically distributed Rayleigh channels even in an LOS scenario.

Railway, subway, airplane, and other transportation systems have drawn an increasing interest on the use of wireless communications for critical and noncritical services to improve performance, reliability, and passengers experience. Smart transportation systems require the use of critical communications for operation and control, and wideband services can be provided using noncritical communications. High speed train (HST) is one of the best test cases for the analysis of communication links and specification of the general requirements for train control and supervision, passenger communications, and onboard and infrastructure wireless sensors. In this paper, we analyze in detail critical and noncritical networks mainly using the HST as a test case. First, the different types of links for smart rail transportation are described, specifying the main requirements of the transportation systems, communications, and their applications for different services. Then, we propose a network architecture and requirements of the communication technologies for critical and noncritical data. Finally, an analysis is made for the future technologies, including the fifth-generation (5G) communications, millimeter wave (mmWave), terahertz (THz), and satellites for critical and high-capacity communications in transportation.

Long Term Evolution (LTE) is expected to substitute the Global System for Mobile Communications (GSM) as the radio access technology for railway communications. Recently, especial attention has been devoted to high-speed trains since this particular environment poses challenging problems in terms of performance simulation and measurement. In order to severely decrease the cost and complexity of high-speed measurement campaigns, we have proposed a technique to induce effects caused by highly-time varying channels on Orthogonal Frequency-Division Multiplexing (OFDM) signals while conducting measurements at low speeds. In this work, we employ this technique to evaluate LTE transmissions in a Time-Division Duplex (TDD) fashion, which is more convenient for the railway environment due to a number of reasons (e.g., better flexibility in frequency choice and the importance of the uplink transmissions for railway control communications). We assess the performance obtained in high- speed scenarios up to 500 km/h.

Due to the extra loss resulting from tunnel curve, most of the theoretical models cannot be directly used for propagation inside curved tunnels. In this paper,extensive simulations are made for propagation at different frequencies in different types of curved tunnels, by using a ray-tracing simulator that is validated by numerous measurements. From the simulated received power, the extra losses of tunnel curve in various cases are extracted, analyzed, and stochastically modeled by normal distributions. Thus, with the parameters presented in this paper, the total propagation loss inside curved tunnels can be predicted by adding the stochastically generated extra loss to the propagation loss in the straight tunnel. This provides a fairly simple and effective way to extend the theoretical propagation models to fulfill network planning and system design for communication systems in real curved tunnels.

Mobile Hotspot Network (MHN) is designed as a new communication system for high speed mobile group vehicles and aiming at providing multi-Giga bps data rate by using millimeter wave (mmWave). According to the MHN setup, the channel at 30 GHz band is simulated by a ray-tracing tool in a typical straight subway tunnel. In order to assess the system physical layer performance, system simulations based on entire channel characteristics provide an profound significance. the channel parameters such as path loss, power delay profile, decorrelation distance, Rician K-factor, Doppler characteristics, etc., are extracted and analyzed for verifying system feasibility and can serve as a guide for physical layer design.

Massive MIMO has been chosen as one of the candidate technologies of the fifth-generation mobile communication system (5G), and channel modeling of massive MIMO is of great importance. The most direct and effective approach to investigate the propagation characteristics of massive MIMO channels is channel measurements. However, there are only few measurements of massive MIMO channels, and there still lacks deep investigations of massive MIMO channel characteristics. In this paper, we present a measurement campaign of indoor massive MIMO channels, by using a linear large-scale array with 64 elements. The measurements are conducted at 2 GHz, 4 GHz, and 6 GHz, respectively, with a bandwidth of 200 MHz. Both LOS and NLOS propagation scenarios are considered in the measurements. The basic channel parameters are extracted, including path loss, delay spread, and coherence bandwidth. The non-stationarity of radio channels, which is reflected by the variations of delay spread and coherence bandwidth over different array locations, is discussed. The impact of carrier frequency on the above channel parameters is further discussed. The results would be useful for the design of massive MIMO system in the indoor environments.

The rapidly time-varying channel in high-speed railway poses tough design challenges, which necessitates the research of accurate channel models. Existing researches focus on the isolated high-speed railway scenarios, and mainly deal with path loss, shadowing fading, Ricean K-factor and delay spread. However, few studies have been done in Doppler domain. In this paper, a deterministic channel model that employs ray- tracing algorithm is presented. The proposed deterministic modeling approach is applied in composite high-speed railway scenario rather than isolated one. The scenario is flexibly reconstructed through SketchUp. The simulation results are validated by the data measured in the same scenario. The channel characteristics in Doppler domain and the effect of Doppler shift are statistically analyzed based on the deterministic channel model. The transition regions in the composite scenario are emphatically investigated, and the results are compared with those of prior studies.

To ensure the stability of radio signal transmission in high-speed railway station, a detailed channel modeling for this special environment is essential and valuable. In this work, an evaluation of the performance of radio propagation in a real high speed railway station scenario is conducted. The corresponding measurements in the actual environment are carried out with the assistance of hardware testbed at 950 MHz and 2150 MHz, respectively. The propagation in both Line of Sight (LOS) and None-Line of Sight (NLOS) conditions are considered and tested. The measurement scenarios and configurations are presented in detail. Furthermore, the key parameters, such as power delay profile, reverberation time are extracted from the broadband measurement results and compared at both frequencies to achieve a rational solution for broadband communication system applying in the development of next generation high-speed railway.

Unlike solid obstacles, the excess loss of semiclosed obstacles (SCOs) can be considerably overestimated by directly applying existing diffraction models, i.e., multiedge diffraction models. By regarding the propagation situation as a superposition of the cases of the “Open Field” and the “Closed Obstacle,” this paper presents a simple way to model the excess loss of SCOs that widely exists in intelligent transportation systems. By estimating two weight coefficients according to the specific situation, this model structure can be applied to different SCOs. To illustrate our modeling concepts, two typical cut-and-cover tunnels in high-speed railway are studied in detail. Combining this case with our previous implementations for train stations and crossing bridges, a complete set of coefficients for the excess loss of the main SCOs in railway settings is presented. This case study shows that the proposed approach provides an effective and fairly simple way to include various SCOs in the network planning, simulation, and design of communication systems. As our approach has determined the coefficients empirically, the proposed model structure can provide the foundation for future work that aims to streamline the excess loss prediction via estimation of coefficients either analytically or via a reduced set of measurements.

Wave propagation characteristics in curved tunnels are of importance for designing reliable communications in subway systems. This paper presents the extensive propagation measurements conducted in two typical types of subway tunnels—traditional arched “Type I” tunnel and modern arched “Type II” tunnel—with 300- and 500-m radii of curvature with different configurations—horizontal and vertical polarizations at 920, 2400, and 5705 MHz, respectively. Based on the measurements, statistical metrics of propagation loss and shadow fading (path-loss exponent, shadow fading distribution, autocorrelation, and crosscorrelation) in all the measurement cases are extracted. Then, the large-scale fading characteristics in the curved subway tunnels are compared with the cases of road and railway tunnels, the other main rail traffic scenarios, and some “typical” scenarios to give a comprehensive insight into the propagation in various scenarios where the intelligent transportation systems are deployed. Moreover, for each of the large-scale fading parameters, extensive analysis and discussions are made to reflect the physical laws behind the observations. The quantitative results and findings are useful to realize intelligent transportation systems in the subway system.

High-speed railways (HSRs) have been widely introduced to meet the increasing demand for passenger rail travel. While it provides more and more conveniences to people, the huge cost of the HSR has laid big burden on the government finance. Reducing the cost of HSR has been necessary and urgent. Optimizing arrangement of base stations (BS) by improving prediction of the communication link is one of the most effective methods, which could reduce the number of BSs to a reasonable number. However, it requires a carefully developed propagation model, which has been largely neglected before in the research on the HSR. In this paper, we propose a standardized path loss/shadow fading model for HSR channels based on an extensive measurement campaign in 4594 HSR cells. The measurements are conducted using a practically deployed and operative GSM-Railway (GSM-R) system to reflect the real conditions of the HSR channels. The proposed model is validated by the measurements conducted in a different operative HSR line. Finally, a heuristic method to design the BS separation distance is proposed, and it is found that using an improved propagation model can theoretically save around 2/5 cost of the BSs.

High-speed railway (HSR) brings convenience to peoples' lives and is generally considered as one of the most sustainable developments for ground transportation. One of the important parts of HSR construction is the signaling system, which is also called the “operation control system,” where wireless communications play a key role in the transmission of train control data. We discuss in detail the main differences in scientific research for wireless communications between the HSR operation scenarios and the conventional public land mobile scenarios. The latest research progress in wireless channel modeling in viaducts, cuttings, and tunnels scenarios are discussed. The characteristics of nonstationary channel and the line-of-sight (LOS) sparse and LOS multiple-input-multiple-output channels, which are the typical channels in HSR scenarios, are analyzed. Some novel concepts such as composite transportation and key challenging techniques such as train-to-train communication, vacuum maglev train techniques, the security for HSR, and the fifth-generation wireless communications related techniques for future HSR development for safer, more comfortable, and more secure HSR operation are also discussed.

In a realistic high-speed railway environment, the track, terrain, vegetation, cuttings, barriers, pylons, buildings, and crossing bridges are the main sources of reflection, diffraction, and scattering. Moreover, the radiation pattern and the polarization of the transmitting and receiving antennas considerably influence the propagation. This paper presents a deterministic modeling approach covering all the effects in a realistic highspeed railway environment for the first time. The antenna influence and the mechanisms of transmission, scattering, and reflection are evaluated by developing a 3D ray-optical tool. The diffraction loss is obtained by the multi-edge diffraction models using raster databases. This approach compensates the limitation of the existent empirical and stochastic models used for the high-speed railway, and promotes the deterministic modeling towards to the realistic environment. Therefore, it allows a detailed and realistic evaluation and verification of the train control communications systems.

The propagation situations inside tunnels vary with the distance between transmitter and receiver, as well as the relative size of the users. This paper presents a novel hybrid propagation model covering three propagation mechanisms with two types of users. In the first case, the users are relatively small in size compared with the tunnel. The entire process is interpreted by combining the free space propagation model and the limited multi-mode propagation model. In the second case, for relatively large users , the free space propagation can be replaced by the near shadowing effect to some extent or totally. Hence, the whole procedure is described by coupling the statistical model in the near shadowing zone, the free space model, and the limited multi-mode model. Two groups of measurements are employed to validate the model. The results show good agreement and, therefore, the model supports an effective way to predict the propagation situation inside tunnels for different types of users.

There is no unanimous consensus yet on the propagation mechanism before the break point inside tunnels. Some deem that the propagation mechanism follows the free space model, others argue that it should be described by the multimode waveguide model. Firstly, this paper analyzes the propagation loss in two mechanisms. Then, by conjunctively using the propagation theory and the three-dimensional solid geometry, a generic analytical model for the boundary between the free space mechanism and the multi-mode waveguide mechanism inside tunnels has been presented. Three measurement campaigns validate the model in different tunnels at different frequencies. Furthermore, the condition of the validity of the free space model used in tunnel environment has been discussed in some specific situations. Finally, through mathematical derivation, the seemingly conflicting viewpoints on the free space mechanism and the multi-mode waveguide mechanism have been unified in some specific situations by the presented generic model. The results in this paper can be helpful to gain deeper insight and better understanding of the propagation mechanism inside tunnels.

This paper focuses on three vital aspects in propagation characteristics inside tunnels: break point; near zone and far zone in a practical environment. Firstly, all the distinguishing features of radio channel in the areas before and after break point at different angles such as modal theory, ray-tracing theory, statistics and propagation model have been summarized. By collectively analyzing the research perspectives above, a panoramic view on the propagation characteristics of break point; far zone and near zone has been proposed. Based on a measurement carried out in Madrid operational subway environment at 2.4 GHz, some new radio channel characters of the break point; the regions before and after break point at higher frequency have been discussed and analyzed.