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.

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.

Reconfigurable intelligent surface (RIS) is able to manipulate the wireless environment smartly and has been exploited for assisting the wireless communications, especially at high frequency band. However, it suffers from hardware impairments (HWIs) in practical design, manufacturing and deployment, which inevitably degrades its performance and thus limits its full potential. To address this practical issue, we first propose a new RIS reflection model involving phase-shift errors, which is verified by the measurement results from field trials. With this beamforming model, various phase-shift errors caused by different HWIs can be analyzed. The phase-shift errors are classified into three categories: 1) globally independent and identically distributed errors; 2) grouped independent and identically distributed errors; and 3) grouped fixed errors. The impact of typical HWIs, including frequency mismatch, PIN diode failures and panel deformation, on RIS beamforming ability are studied with the theoretical model and are verified with numerical and field test data. The impact of frequency mismatch are discussed separately for narrow-band and wide-band beamforming. Finally, useful insights and guidelines on the RIS design and its deployment are highlighted for practical wireless sytsems.

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.

Industrial Internet of Things (IIoT) is an emerging area that fifth-generation (5G) mobile communication system penetrates industrial manufacturing applications. The indoor factory has larger space and there are a lot of metal machine tools distributed in it, which makes its radio propagation characteristics and corresponding channel models significantly different from those of the indoor office and indoor hotspot. To support the design and the evaluation of the IIoT techniques, the 3rd Generation Partnership Project (3GPP) released the first 5G IIoT standard model in October 2019. In this article, we give a detailed explanation of this IIoT model and compare it with other indoor models. First, we introduce the standardization of 3GPP IIoT channel model and its motivation. Second, four IIoT subscenarios, which are classified according to the clutter density and antenna height, are described. Third, the potential frequency bands of 5G IIoT are summarized. Fourth, the models of channel parameters, including the path loss and the line-of-sight (LOS) probability, the root mean-square (RMS) delay spread, and the angular spread, are given. Among them, the models of path loss and LOS probability take the antenna height and clutter density into consideration. The model of RMS delay spread changes from frequency-dependent to volume-dependent in order to catch the size variation of factories. Finally, two newly added key channel characteristics, dual mobility and absolute time of arrival, which help to describe the robot movement and positioning, are also presented.

In this paper, we study the degrees of freedom (DoF) of a new network information flow model named the circular multirelay multiple-input multiple-output interference channel (CMMI). In this model, there are two clusters and each of them contains three users. Each user equipped with M antennas in one cluster intends to deliver data streams to another user in the same cluster in a circular one-way transmission via the common distributed K N-antenna relay nodes. The CMMI network model can be considered as a basic component to construct the complicated Internet of Things networks. By assuming linear processing at the users and the relays, we show that the original analysis of DoF comes down in finding solutions of some nonlinear matrix equations with rank constraints. Toward this end, by using linear precoding and post-processing techniques, we propose two different approaches to solve the nonlinear matrix equations based on different antenna configurations. We show that a √ DoF of max{min{M, (√6K/12)}, min{(M/3), (KN/2)}} is achievable for ∀(M/N) ∈ (0, +∞). In addition, to assess the optimal DoF, the cut-set approach is used for deriving the DoF upper bound by innovatively separating certain users to form two-pair two-way relay channels. We show that the DoF of CMMI is upper bounded by max{min{M, (KN/3)}, min{(2M/3), (KN/2)}}. By combining the achievable DoF and the upper bound, we finally show that the optimal DoF of CMMI can be achieved √ for (M/N)∈[0, (√6K/12)]∪[(3K/2), +∞), ∀K ≥ 1.

For the design, performance evaluation, and optimization of potential 3-D multiple input and multiple output (3-D MIMO) algorithms, an accurate stochastic channel model is indispensable. Simultaneously, it is desirable for the new 3-D MIMO channel model to be a further expansion of already well developed 2-D MIMO channel models to allow easy implementation and facilitate possible $3^{rd}$ generation partnership project standardization. This paper first shows our recent outfield channel measurement campaigns in urban macrocell scenarios with a planar antenna array and a crown-shaped antenna array installed on the base station and the user sides, respectively. A comprehensive methodology from the statistical analysis perspective to characterize the 3-D MIMO channel, particularly, in the elevation domain, is then elaborated upon. The framework of the new approach coincides with mainstream parametric stochastic models. Moreover, it is observed that, by recalculating the statistics of the large scale parameters as well as their cross-correlation matrix to rectify existent non-positive definite problem, adding distance dependent elevation angular spread and introducing a mixture of Von Mises Fisher distributions to describe the interdependence between azimuth and elevation, the accuracy of current standardized 3-D channel models can be improved upon. Finally, numerical results verify the validity of our proposal.

Propagation channel model is crucial for the design of high-frequency communication systems. Measurement-based stochastic modeling approach which was commonly adopted in low frequencies is no more efficient or flexible due to the limitation of antenna gain and sounding efficiency. In this contribution, we propose a hybrid method which utilize both the measurements and ray-based deterministic simulations to study the channel characteristics in specified indoor environments. First, the ray-tracing tool is evaluated by using a small amount of measured data. And then we adopt the validated tool to generate massive samples of channel impulse responses. Fianlly, statistical models are constructed based on the proposed approach. The results show good compatibility with the recommended model suggested by METIS. Furthermore, the importance of diffuse scattering at high-frequency is also investigated.

Based on a ring, a disk and an elliptical scattering models, the power spectrum densities (PSDs) are derived and investigated for fixed-to-fixed (F2F) propagation scenarios where a local scatterer is moving in any direction with random velocity at the predefined geometries of the models. The velocity distributions of the scatterers are assumed to follow uniform, exponential and mixed Gaussian, and the transceiver vehicles are moving either with low- and high-speed. The results show that the one-ring, disk scattering model and the model in [15] are very close in describing the PSDs for the F2F radio channels. Moreover, different shape factors have little effect on the PSDs in the disk model. As the shape factor is large enough, the disk model tends to be the same as the one-ring model. The PSDs derived from the elliptical model are different from the one-ring and disk models because the scatterers are distributed not only close to the transceiver ends, but also between the transceiver.