Accurate radar cross section (RCS) modeling is crucial for characterizing target scattering and improving the precision of Integrated Sensing and Communication (ISAC) channel modeling. Existing RCS models are typically designed for specific target types, leading to increased complexity and lack of generalization. This makes it difficult to standardize RCS models for 3GPP ISAC channels, which need to account for multiple typical target types simultaneously. Furthermore, 3GPP models must support both system-level and link-level simulations, requiring the integration of large-scale and small-scale scattering characteristics. To address these challenges, this paper proposes a unified RCS modeling framework that consolidates these two aspects. The model decomposes RCS into three components: (1) a large-scale power factor representing overall scattering strength, (2) a small-scale angular-dependent component describing directional scattering, and (3) a random component accounting for variations across target instances. We validate the model through mono-static RCS measurements for UAV, human, and vehicle targets across five frequency bands. The results demonstrate that the proposed model can effectively capture RCS variations for different target types. Finally, the model is incorporated into an ISAC channel simulation platform to assess the impact of target RCS characteristics on path loss, delay spread, and angular spread, providing valuable insights for future ISAC system design.

Integrated Sensing and Communication (ISAC), as a fundamental technology of 6G, empowers Vehicle-to-Everything (V2X) systems with enhanced sensing capabilities. One of its promising applications is the reliance on constructed maps for vehicle positioning. Traditional positioning methods primarily rely on Line-of-Sight (LOS), but in urban vehicular scenarios, obstructions often result in predominantly Non-Line-of-Sight (NLOS) conditions. Existing researched indicate that NLOS paths, characterized by one-bounce reflection on building wall with determined delay and angle, can support sensing and positioning. However, experimental validation remains insufficient. To address this gap, channel measurements are conducted in an urban street to explore the existence of strong reflected paths in the presence of a vehicle target. The results show significant power contribution from NLOS paths, with large Environmental Objects (EOs) playing a key role in shaping NLOS propagation. Then, a novel model for EO reflection is proposed to extend the Geometry-Based Stochastic Model (GBSM) for ISAC channel standardization. Simulation results validate the model's ability to capture EO's power and position characteristics, showing that higher EO-reflected power and closer distance to Rx reduce Delay Spread (DS), which is more favorable for positioning. This model provides theoretical guidance and empirical support for ISAC positioning algorithms and system design in vehicular scenarios.

As the underlying foundation of a digital twin network (DTN), digital twin channel (DTC) can accurately depict the electromagnetic wave propagation in the air interface to support the DTN-based 6G wireless network. Since electromagnetic wave propagation is affected by the environment, constructing the relationship between the environment and radio wave propagation is the key to implementing DTC. In the existing methods, the environmental information inputted into the neural network has many dimensions, and the correlation between the environment and the channel is unclear, resulting in a highly complex relationship construction process. To solve this issue, we propose a unified construction method of radio environment knowledge (REK) inspired by the electromagnetic wave property to quantify the propagation contribution based on easily obtainable location information. An effective scatterer determination scheme based on random geometry is proposed which reduces redundancy by 90%, 87%, and 81% in scenarios with complete openness, impending blockage, and complete blockage, respectively. We also conduct a path loss prediction task based on a lightweight convolutional neural network (CNN) employing a simple two-layer convolutional structure to validate REK’s effectiveness. The results show that only 4 ms of testing time is needed with a prediction error of 0.3, effectively reducing the network complexity.

Visible light communication (VLC) is considered a promising technology for enabling Internet of Everything (IoE) applications in the sixth generation (6G), owing to its specific advantages over radio frequency (RF) communications. A comprehensive understanding of VLC channel characteristics and models is imperative for optimizing VLC technology, designing systems, and evaluating performance. This article presents an overview of ongoing research in channel characterization and modeling for VLC-IoE applications in the context of 6G. Recent advancements are systematically summarized, encompassing channel modeling methods, application scenarios, emerging combining technologies, such as reconfigurable intelligent surfaces (RISs) and integrated sensing and communication (ISAC), and distinctive channel characteristics. Additionally, future research directions in these domains are outlined to provide insights into forthcoming investigations for VLC-IoE applications in 6G.

Extremely-large-scale multiple-input multiple-output (XL-MIMO) technology, offering vast spatial degrees of freedom by deploying a huge number of antennas, is a promising enabling technology to empower sixth-generation mobile networks (6G). The XL-MIMO channel model is a prerequisite of XL-MIMO technology optimization, system design, and performance evaluation. In this paper, we provide an overview of challenges and ongoing research in XL-MIMO channel measurement, characterization, and modeling. In particular, characterizing and modeling near-field effects and spatial non-stationarity (SnS) are discussed. Also, the channel modeling methods that can describe these new channel characteristics are surveyed. Furthermore, open issues in XL-MIMO channel measurement, characterization, and modeling are presented to give insights into future XL-MIMO channel research.

The sensitivity of millimeter-wave (mmWave) to blockage and the requirement for the communication system to support mobility scenarios makes mmWave blockage and beam prediction necessary. In this paper, the effect of diffraction characteristics on improving blockage and beam prediction is investigated. A dataset with diffraction is created, and a recurrent neural network (RNN) is designed to capture the diffraction characteristics in the dataset. Further, the generalization ability of the RNN for different scenarios is researched. The results show that the accuracy of both blockage and beam prediction is further improved by utilizing diffraction characteristics. In addition, the blockage and beam prediction accuracy of the designed RNN is increased by 1.5% and 9.7% compared with a reference deep neural networks (DNN). Finally, applying the RNN model trained in the outdoor scenario to the prediction in the indoor scenario, the indoor blockage and beam prediction accuracy respectively reach up to 99.8% and 98.3% of the outdoor’s.

The loss characteristics caused by human bodies blockage have a significant impact on millimeter-wave (mmWave) band. In this paper, the influence of the number of human bodies on blockage loss is firstly analyzed in both corridor and classical indoor scenarios at the mmWave band. Then, in order to represent the blockage effect caused by different numbers of human bodies, the line-of-sight (LOS) outage probability is introduced. Finally, a multi-person blockage loss model is proposed based on the LOS outage probability and human model. The model demonstrates that the multi-person blockage loss can be modeled as Gaussian by the LOS outage probability. These results try to give some insights into the modeling of human blockage, especially in multi-person cases.

Integrated Sensing and Communication (ISAC) is an emerging technology that realizes both communication and sensing functionalities on the same sets of hardware as well as same frequency bands. Such technique will bring tremendous benefits such as spectrum efficiency improvement and cost reduction. In this paper, we firstly define three different classes of ISAC with the integration of both resources and functionalities for communication and sensing as the ultimate form. Furthermore, target at the full-integration level, we propose a downlink-pilot-based OFDM ISAC signal processing algorithm for base-station-orientated sensing. The OFDM architecture guarantees forward compatibility, and downlink reference signals including DMRS and CSI-RS ensure the communication throughput and address privacy concerns as well. Simulation results show that the proposed method is able to achieve outstanding sensing performance with range estimation error of 0.15m and velocity estimation error of 0.2m/s in the low SNR region.

Acquisition and feedback of accurate downlink (DL) channel state information (CSI) bring up high overhead. So a convolutional neural network (CNN) based multi-input-multi-output (MIMO) channel prediction method is proposed in this paper. Based on the space correlation, we design the proposed prediction method that predicts the MIMO channel concurrently. The proposed CNN method extracts the feature of UL-CSI through convolutional layers and predicts DL-CSI by the feature through deconvolutional layers. Simulation results demonstrate that the proposed channel prediction method leads to higher accuracy than existing methods. The bit error rate (BER) in orthogonal frequency division multiplexing (OFDM) system of proposed method is 82.4% and 26.2% lower than linear minimum mean squared error (LMMSE) method in two different simulated datasets. And the proposed method is tested under different user moving speeds and different antenna scale.

Antenna selection has attracted more and more attention in massive multiple input multiple output (MIMO) systems to balance the performance and computational complexity. In this paper, we design a top-N transmit antenna selection algorithm for massive MIMO systems. This algorithm can reach the optimization rather the sub-optimization which traditional algorithms usually achieve. Specifically, we build a top $-N -$ output ResNet which selects the top-N optimal schemes to improve the accuracy compared to the single output network. And then we design a N-to-one selection algorithm that acquires the optimal scheme from the top-N optimization. With the help of the top-N antenna selection algorithm, our proposed algorithm reaches 95% accuracy with only 2.23% complexity compared to exhaustive-search-based antenna selection.

Millimeter-wave technology is the key technology to support the connection and throughput of the Industrial Internet of Things (IIoT). And the channel model is the basis for the evaluation, optimization, and deployment of the wireless communication system. To model the millimeter-wave channel characteristics in the IIoT scenario, we conduct measurements in a factory-like scenario at 28 GHz. In measurements, antenna heights are set to be higher or lower than most of the metal machines, separately. Based on the collected data, we analyze the large-scale fading and small-scale fading characteristics, including path loss, delay spread (DS), and angular spread (AS). These channel characteristics are statistically modeled. The effects of antenna heights on these channel characteristics are also investigated. Furthermore, we study the distance dependence of the DS and AS.

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.

This paper presents an empirical study of the uplink and downlink azimuth angle of arrival (AoA) in an urban micro (UMi) scenario at 28 GHz. At present, most UMi measurements are conducted in the downlink and then the uplink situation is inferred assuming channel reciprocity. Although the channel correlation coefficient of the uplink and downlink can be as high as 0.8, this does not mean that they are the same. Only a real uplink measurement can accurately describe its channel conditions, and this is what this study does. A receiver equipped with a rotatable horn antenna is mounted at the base station and the user terminal, respectively, in simulating the uplink and downlink. To improve the angular resolution, we extract the multipath components (MPCs) using the space-alternating generalized expectation-maximization algorithm. Also, a spatial lobe approach is used to cluster the MPCs in the power angular spectrum. By matching MPCs with objects in the environment, we find that direct propagation and first-order reflections are dominant in line-of-sight and non-line-of-sight cases. By comparing our measurement with those in standard channel models, we verify that the AoA of clusters follows a Gaussian distribution in the uplink and downlink. In addition, a two-dimensional Gaussian distribution for ray AoA and power is established to reflect their correlation.

With the commercialization of fifth generation networks worldwide, research into sixth generation (6G) networks has been launched to meet the demands for high data rates and low latency for future services. A wireless propagation channel is the transmission medium to transfer information between the transmitter and the receiver. Moreover, channel properties determine the ultimate performance limit of wireless communication systems. Thus, conducting channel research is a prerequisite to designing 6G wireless communication systems. In this paper, we first introduce several emerging technologies and applications for 6G, such as terahertz communication, industrial Internet of Things, space-air-ground integrated network, and machine learning, and point out the developing trends of 6G channel models. Then, we give a review of channel measurements and models for the technologies and applications. Finally, the outlook for 6G channel measurements and models is discussed.

Identifying Line-of-Sight (LOS) and Non-LOS (NLOS) channel conditions is a big concern in many works. In this paper, we aim to achieve channel classification for millimeter wave (mmWave) systems. The results of mmWave measurement conducted at 28 GHz in typical shopping mall scenario are presented. Specifically, we analyzed the identification performance of single- valued feature extracted from channel impulse response (CIR), and confirmed that rise-time is an attractive features for identification, particularly precise for wideband mmWave signal. Furthermore, a classification algorithm Gradient Boosting Decision Tree (GBDT) is used for LOS/NLOS identification. Experimental results demonstrate an overall detection accuracy of 97.9\%, which shows that our method has a better classification performance than the single feature classifier. Moreover, extensive experimental evaluation considering various propagation distances and obstruction diversity have validated the feasibility of this method.

The millimeter-wave communication is a key component in future broadband cellular networks. In this paper,a virtual multi-antenna measurement was conducted in an indoor line-of-sight scenario using a broadband channel sounder with 400 MHz bandwidth at 28 GHz. In the measurement,with an omni- directional antenna located in a fixed position at transmitter, horn antenna at receiver is rotated 360° in azimuth with the step of 2°, 3°, 5°, 8° and 10°, respectively. Then, the space- alternating generalized expectation-maximization (SAGE) algorithm was utilized to extract channel parameters from the measured results, including number of multipath components (MPCs) in azimuth domain, power azimuth spectrum (PAS) and root mean square azimuth spread of arrival angles (ASA). Finally, we compare these estimated parameters. This work aims to reveal the effects of the rotating step on the estimated results using SAGE algorithm in the virtual multi- antenna measurement. It is found that the number of estimated MPCs in azimuth domain decreases obviously as the rotating step rises, which further affects PAS and ASA. When with the step of 5°, the estimated ASA value is closest to the reference value of 3GPP TR 38.900.

The millimeter-wave band will be one of the most key components in the next generation wireless communication system. In this paper, a radio channel measurement was conducted in an indoor office environment at 28 GHz with 500 MHz bandwidth. The channel sounder with the clock synchronization was used to measure in both line-of-sight (LoS) and none- line-of-sight (NLoS) scenarios. The channel impulse responses (CIRs) are recorded with an omnidirectional antenna at TX and a horizontal-rotating horn antenna fixed at the same height at RX as references. The space-alternating generalized expectation-maximization (SAGE) algorithm was applied to extract the channel characteristics of multipath components (MPCs) from the synthesized CIRs, and then the direct synthesized CIRs and the CIRs constructed from SAGE results were compared in terms of power delay profiles (PDPs) and power angular spread (PAS). It is found that the reconstructed results closely approximate real results. In addition, the cluster numbers and the inner-cluster root mean square (RMS) angle spreads are drawn after the clustering analysis and they cohere with the changes of the corresponding surrounding environment of the point, regardless of LoS or NLoS. The wireless channel propagation at 28 GHz is heavily dependent on the environment because the linear and reflective propagation are the main mode of transmission.