High-mobility and large-bandwidth applications at high frequencies make the acquisition of doubly-selective channels costly and complex, as the fast fading channel causes a surge in pilot overheads and inter-carrier interference. Accurate estimation of the complex channel gain (CG) and the carrier frequency offset (CFO) is required to support subsequent high-accuracy channel prediction that alleviates the heavy pilot burden. The emerging technology of the wireless channel map (WCM) can approximately reproduce the actual propagation environment digitally with customized channel parameters, offering an opportunity to accurately estimate the complex CG and CFO. In this paper, a WCM-based prior distribution construction method and a joint complex CG and CFO estimation algorithm are proposed. Specifically, a parameterized linear estimation problem for the complex CG is generated based on a nonuniform delay-domain off-grid channel representation, and with more realistic prior distributions constructed by the knowledge from the WCM-provided angular-delay power spectrum density, the joint estimation problem is solved under the Bayesian inference framework. Simulation results demonstrate the superiority of the proposed algorithm, with a better performance in terms of estimation accuracy and bit error rates (BER) than existing baselines. It is also verified that the proposed WCM-based algorithm is highly adaptable to different WCM precision and robust to different user speeds.

Traditional geometry-based positioning methods suffer from low accuracy in non-line-of-sight environments, whereas fingerprint-based positioning methods incur high maintenance costs due to the need for continuous database updates. Channel charting-based positioning methods adopt unsupervised learning to infer user positions from channel state information, but often suffer from low accuracy under complex propagation conditions. Channel maps enable the prediction of real-world positions by providing a priori channel information associated with the propagation environment. Building on this concept, channel charting-based positioning methods leverage this prior knowledge to enhance positioning accuracy and reduce reliance on large labeled datasets. In this paper, we propose a novel channel charting-based semi-supervised positioning algorithm with co-training, which utilizes both labeled data from the channel map and unlabeled data from practical communication systems to predict real-world geographical positions. This algorithm leverages a covariance-based channel feature and a corresponding dissimilarity metric to enhance robustness against noise and timing advance interference. Through ray tracing simulations calibrated by real-world measurements, the proposed algorithm is compared with state-of-the-art positioning methods under different noise conditions, demonstrating its effectiveness and superiority.

Conventional wireless channel measurements and modeling typically study channels via discrete spatial sampling. As the sixth-generation (6G) wireless communication places higher demands on the accuracy of channel state information, this discrete approximation becomes insufficient and motivates research on continuous-space channels. In this work, 28 GHz indoor continuous-space channel measurements are conducted, and key channel characteristics are analyzed. Based on the analysis, the necessity of continuous-space channel research is validated, and the role of channel maps is demonstrated. Furthermore, a continuous-space channel map construction method using the Graph SAmple and aggreGatE (GraphSAGE) algorithm is proposed. By the learning on spatial aggregation function rather than performing a passive weighted sum, the proposed GraphSAGE-based map construction method can reduce the performance bias caused by discrete spatial sampling and recover the continuous-space channels accurately. Continuous-space measurements are used as benchmarks to compare the performance of the GraphSAGE-based channel map. Extensive experiments confirm the superiority of the proposed GraphSAGE-based method over existing artificial intelligence (AI) algorithms, providing a robust approach for the 6G continuous-space channel map construction.

To address sparse channel measurement data and inadequate predictive capabilities in conventional channel models, predictive channel modeling employs joint generative and predictive architectures to enhance robustness. In this paper, we propose an enhanced diffusion-driven predictive framework that integrates generative augmentation and prior-aware prediction into a unified learning pipeline. We first introduce a space-time-frequency (STF) coupled diffusion network based on transformers that generates synthetic channel data preserving critical channel statistical properties. Additionally, we compress measured channel state information into a low-dimensional manifold via a latent encoder and introduce an innovative composite training scheme that couples diffusion-driven prior generation with prediction, equipping the predictive module with rich latent features that lift its performance ceiling and markedly improve generalization across diverse scenarios. Extensive experiments confirm the superiority of our algorithm, and its performance is further validated using channel measurement data, thereby demonstrating its robustness for advanced wireless communications in real-world deployment scenarios.

Cell-free (CF) massive multiple-input multiple-output (M-MIMO) provides an almost uniformly high data rate for all user equipment (UE) through multiple access points (APs), with a non-negligible signal processing burden. Angle domain transmission and channel maps promise to alleviate this burden by reducing channel dimensions in the angle domain and providing$a$priori channel information, respectively. In this paper, we propose a channel map-based angle domain multiple access scheme for uplink CF M-MIMO communications. First, we propose a twostage data reception and pilot assignment scheme constituting receive combining and large-scale fading decoding (LSFD) to reduce overall interference and maximize spectral efficiency (SE). Furthermore, we construct two channel map-based transmission mechanisms by wielding different levels of channel information, where a tailored data reception scheme with a newly derived SE upper bound is also proposed for quantitative evaluation. Simulation results show that the proposed schemes outperform both their space domain alternatives and those without using channel maps in terms of SE.

The current propagation scenario classification methods of standardized channel models are rough and the definitions are unclear, despite the diverse propagation scenarios in the sixth-generation (6G) communications technology era. This paper proposes an unsupervised classification algorithm based on the self-organizing map (SOM) to establish a more refined propagation scenario classification for the 6G era. First, the physical environment information of the actual scenario is extracted based on digital maps. Then, the SOM neural network parameters are determined and the propagation scenario classification is implemented. Finally, the effectiveness of the proposed propagation scenario classification is verified by the data collected from the practical communication network in the following three aspects: physical environment characteristics, evaluation indicators, and channel characteristics. The results show that the proposed SOM scenario classification algorithm outperforms K-means, DBSCAN, and the Gaussian mixture model (GMM), among other algorithms, providing an effective solution for the detailed classification of 6G propagation scenarios.

Traditional geometry-based positioning methods (GPMs) suffer from low accuracy in non-line-of-sight (NLOS) environments, while fingerprint-based methods (FPMs) encounter high maintenance costs due to the need for continuous database updates. As an emerging unsupervised technique, channel charting can address these challenges by mapping channel state information (CSI) into a low-dimensional virtual space that represents pseudo-positions of user equipments (UEs). In this paper, a channel charting-based positioning algorithm that leverages a covariance-based channel feature and a corresponding dissimilarity metric is proposed to enhance robustness against noise and timing advance (TA) interference. Additionally, an improved Triplet neural network algorithm is introduced, which enables channel charting to directly and accurately predict real geographical positions. Through ray tracing simulations, the proposed algorithm is compared with state-of-the-art channel charting-based positioning methods, demonstrating its effectiveness and superiority.

The sixth-generation (6G) wireless system will involve large-scale wireless channels, but traditional channel acquisition methods are resource-consuming. Digital twin online channel models have been proposed to facilitate efficient 6G network optimization, which maps physical systems to the virtual world, thereby enabling the prediction of physical data. In this paper, we built a Swin Transformer Aided Urban Digital Twin Online Channel Modeling Platform (SmarTwin-OCMP). The platform begins by importing data from the geographic information system (GIS) to obtain the original static urban models based on Unity, then develops a user-friendly interface functionality, capable of providing the channel characteristics of the corresponding communication scenarios. Swin Transformer is adopted to segment various urban communication scenarios on our remote sensing imagery dataset and achieves satisfactory recognition accuracy. Subsequently, we perform electromagnetic parameter matching and prediction of real-time channel characteristics based on the 6G pervasive channel model (6GPCM) according to the recognition and segmentation results. Finally, we integrate all these functionalities into our Unity platform to enable users to interactively explore urban channel information in a more flexible and intuitive manner.

With the rapid evolution of 6G wireless communication technology, the granular classification of communication scenarios becomes increasingly sophisticated. This necessitates a deeper exploration of the intrinsic relationships between environmental information and channel characteristics. Consequently, the development of efficient and accurate methods for communication scenario reconstruction emerges as a critical imperative. Leveraging comprehensive three-dimensional (3D) spatial information from point cloud data, we propose a two-stage workflow for processing massive unstructured point cloud data to generate triangular mesh models of large indoor communication environments. The first stage implements an enhanced RANdom SAmple Consensus (RANSAC) algorithm with adaptive thresholding for robust wall structure extraction. Subsequently, we employ a hybrid reconstruction method combining template-based deformation for furniture elements with a zero-shot semantic segmentation network for wall opening detection. The geometric information extracted through the aforementioned process is utilized to generate mesh models, and ray-tracing (RT) is adopted to simulate channel characteristics. Finally, the efficiency and accuracy of the proposed scenario reconstruction and channel modeling method is demonstrated by comparing its simulated channel characteristics with those of channel measurements.

Indoor positioning is an important application of integrated sensing and communication technology in the sixth generation (6G) wireless communications. To address the limitations of existing fingerprint-based positioning methods (FPMs) under severe multipath effects, a novel channel state information (CSI)-based channel fingerprint structure and a novel line-of-sight (LoS)/non-LoS (NLoS) identification-assisted positioning method (IAPM) is proposed for 6G indoor multiple-input multiple-output (MIMO) communications. The proposed channel fingerprint structure uses the proposed maximum received power path to enhance the feature discrimination. The proposed IAPM incorporates a LoS/NLoS identification module and an improved weighted random forest (IWRF) positioning algorithm for accurate positioning. Evaluations on both channel measurement data and channel synthetic data generated by ray tracing demonstrate that the proposed method achieves superior accuracy and robustness compared with widely used FPMs.

Due to the agile three-dimensional (3D) mobility and flexibility, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications in sixth-generation (6G) wireless networks. In UAV communication systems, 3D beamforming is an effective technique for performance enhancement. To take full advantage of 3D beamforming in UAV communication systems, we need to accurately characterize and efficiently predict highly dynamic UAV channels in the beam domain. This paper first proposes a novel beam domain channel model (BDCM) considering random UAV trajectories and fuselage vibrations. Then, we propose a beam domain channel tracking algorithm to capture variations of multipath component (MPC) parameters in UAV channels. Finally, we put forward a novel beam domain channel prediction scheme for UAV communication systems utilizing channel sparsity and high temporal correlation in the beam domain. The proposed channel prediction scheme can extract channel variation trends utilizing the echo state network (ESN) and predict channel parameters in subsequent time blocks based on the history channel information. Simulation results show that the proposed channel prediction scheme outperforms the conventional prediction scheme based on angular speed estimation in terms of both prediction accuracy and communication system performance.

Urban areas serve as one of the most important scenarios in sixth generation (6G) wireless communications, necessitating comprehensive and in-depth wireless channel characteristics studies. The wireless channels in various 6G urban communication scenarios usually show different characteristics. To provide robust identification across a wide range of 6G urban communication scenarios, we propose a novel intelligent scenario identification algorithm. The proposed algorithm leverages an enhanced multi-layer perceptron (MLP) and autoencoder, utilizing easily accessible environmental physical features as inputs. To enhance identification performance, we employ a series of data pre-processing methods and hyperparameter optimization algorithm. Subsequently, experimental results demonstrate the superior performance of our proposed algorithm compared to the benchmark algorithm. Ultimately, the channel characteristics in all domains, including spatial, temporal, frequency, angle, Doppler, and delay, are studied for these scenarios.

The sixth generation (6G) wireless communication envisions global coverage, all spectra, and full applications, which correspondingly creates many new communication scenarios. As the foundation of 6G communication system design, network planning, and optimization, more intelligent scenario identification algorithms are necessitated in wireless channel modeling to automatically match suitable parameters for various scenarios. With channel statistics and the efficient channel attention (ECA) mechanism, we propose an improved residual network (ResNet) to identify scenarios in the 6G space–air–ground–sea framework. Datasets from both channel measurements and 6G pervasive channel model (6GPCM) simulations are collected to establish a scenario channel characteristic database, including the numbered scenarios and channel statistical properties such as root mean square (RMS) delay spread (DS), RMS angle spread (AS), and stationary distance/time/bandwidth, etc. During the training and verification process, the proposed algorithm is optimized for 29 scenarios, and the identification accuracy of the proposed ECA–ResNet is higher than the convolutional neural network (CNN) and recurrent neural network (RNN). Finally, the cumulative distribution functions (CDFs) of RMS AS and RMS DS for interoffice main road, office outdoor, office, and industrial Internet of Things (IIoT) scenarios are verified according to the measurement data.

To provide customized high-quality services for all users in the sixth-generation (6G) wireless communication systems, it is fundamental to study all 6G channel scenarios and establish accurate channel models for these scenarios correspondingly. However, the absence of comprehensive 6G scenario categorization and the difficulties of modeling the channels for all scenarios bring huge challenges. In this article, we aim to give a thorough overview of channel scenarios, identification algorithms, and intelligent channel modeling theories. First, different standardized scenario categorization principles are reviewed. A unified and exclusive scenario categorization method is elaborated with detailed 6G scenario definitions. Second, scenario features, feature selection principles, ML-based identification algorithms, as well as data preprocessing methods are surveyed for the benefit of accurate scenario identification. Third, the intelligent scenario adaptive channel modeling theory based on 6GPCM is specified. Statistical properties for industrial IoT and HST scenarios are simulated and compared with those from measurements. Finally, future research directions and challenges are addressed.

The development of sixth-generation (6G) wireless communication systems brings significant challenges in channel modeling. Conducting channel measurements for 6G communications is highly expensive and cannot cover all scenarios and frequency bands. Moreover, existing conventional channel models fail to accurately predict channel characteristics in unknown frequency band. As a result, predictive channel modeling has emerged as a promising solution for addressing these challenges in 6G channel modeling. In this study, we propose a frequency domain predictive channel model that combines an autoencoder with a coupling Convolution Gated Recurrent Unit (Conv-GRU) cells. The proposed model aims to predict channel characteristics in unknown frequency bands. The proposed predictive channel model is validated by using data collected from multiple frequency bands channel measurements. To evaluate its performance, several commonly used prediction networks, i.e., a general LSTM network, a GRU-based predictive network, and a Conv-LSTM-based predictive network, are conducted as benchmarks for comparison. Based on evaluation results, our proposed predictive channel model achieves the highest level of accuracy in predicting channels. Additionally, we provide a performance bound for extrapolation predictability using a Ray Tracing simulator.

The research on 6G wireless communications has become a focal point in the global technological competition. For 6G wireless systems, channel modeling is the foundation for the design and deployment of wireless communication systems. With unexplored channel characteristics expected to emerge from new frequency bands and scenarios in 6G wireless channels, predictive channel modeling becomes particularly important. This paper proposes an image-based real-time predictive channel modeling using image processing and machine learning (ML) algorithms, which extracts effective channel information from images and has been validated through channel measurement data and synthetic channel data. The proposed channel model considers large-scale identification and small-scale feature extraction. The former aspect is accomplished by training DeepLabv3+ on our image dataset and classifying the scenarios. For feature extraction, we use VGG-16 as the backbone network and validate it with the 3GPP Uma path loss model for comparison. The results show that predictive channel modeling using segmented images is superior to that of the 3GPP Uma path loss model. Even more, it performs better than the original image-based prediction.

Artificial intelligence (AI) is a promising solution to achieve channel prediction under limited channel data. In this paper, a novel scatterer density-based predictive channel model is proposed to predict channels in multiple scenarios. By exploring the graph attention networks (GAT) and gated recurrent unit (GRU), the proposed model captures multi-domain information in dynamic scenarios. Besides, it extracts highly space-time correlated data characteristics, captures channel dynamic evolutional patterns, and predicts channels in different scenarios. The space-time graph channel datasets are constructed based on the ray tracing (RT) simulation channels. In the prediction experiments, the proposed method is validated on the datasets to predict channels with good performance. Compared with the 3GPP TR 38.901 channel model, the proposed model obtains more accurate channel statistical properties in different scenarios.

Urban wireless communications are one of most important scenarios in the sixth generation (6G) global communication networks. To provide customized high-quality services for all users in various 6G urban wireless communication scenarios, it is necessary and fundamental to study all kinds of 6G urban wireless channel scenarios and establish corresponding channel models for each scenario. However, existing standardized channel models are insufficient to cover all 6G urban communication scenarios. This paper aims to extend the conventional urban communication scenarios with detailed definitions and environmental parameters to accurately establish corresponding channel models. Specifically, the statistical properties of high speed train (HST) scenario are simulated and compared with those of measurement data. The channel model simulation results match well with the channel measurement data, which demonstrates the correctness of the channel model. Then, by applying the proposed precise scenarios and the corresponding model parameters to the channel model, corresponding channel characteristics can be quickly provided. Finally, the channel characteristics of several urban scenarios are simulated and analyzed.

Compared with conventional passive channel modeling, artificial intelligence (AI) based channel models show great advantages in solving real-time prediction problems in wireless communications. In this paper, a generative adversarial network (GAN) and long short-term memory (LSTM) based channel prediction framework is proposed to model indoor wireless channels. By using GAN and LSTM, the model not only enriches the channel data but also achieves the sequence prediction, which can solve the problem of the shortage of training data and prediction channels in the space domain. The prediction performance is evaluated by comparing the root mean square error (RMSE) and mean absolute percentage error (MAPE) of measured data and predicted data. By comparing the statistical properties of the channel measurement data and of the synthetic data, it can be found that the proposed model can predict unknown information in the space domain.

Due to the indoor none-line-of-sight (NLoS) propagation and multi-access interference (MAI), it is a great challenge to achieve centimeter-level positioning accuracy in indoor scenarios. However, the sixth generation (6G) wireless communications provide a good opportunity for the centimeter-level positioning. In 6G, the millimeter wave (mmWave) and terahertz (THz) communications have ultra-broad bandwidth so that the channel state information (CSI) will have a high resolution. In this paper, a weighted random forest (WRF) based indoor positioning algorithm using CSI-based channel fingerprint feature is proposed to achieve high-precision positioning for 6G indoor communications. In addition, ray-tracing (RT) is used to improve the efficiency of establishing channel fingerprint database. The simulation results demonstrate the accuracy and robustness of the proposed algorithm. It is shown that the positioning accuracy of the algorithm is stable within 6 cm in different indoor scenarios when the channel fingerprint database is established at 0.2 m intervals.

Vehicle-to-vehicle (V2V) wireless communication systems are fundamental in many intelligent transportation applications, e.g., traffic load control, driverless vehicle, and collision avoidance. Hence, developing appropriate V2V communication systems and standardization require realistic V2V propagation channel models. However, most existing V2V channel modeling studies focus on car-to-car channels; only a few investigate truck-to-car (T2C) or truck-to-truck (T2T) channels. In this paper, a hybrid geometry-based stochastic model (GBSM) is proposed for T2X (T2C or T2T) channels in freeway environments. Next, we parameterize this GBSM from the extensive channel measurements. We extract the multipath components (MPCs) by using a joint maximum likelihood estimation (RiMAX) and then determine the cluster types based on their evolution patterns. We classify the determined clusters into line-of-sight, single-bounce reflections from static interaction objects (IOs), single-bounce reflections from mobile IOs, multiple-bounce reflections, and density multipath components (DMCs). Particularly, we model multiple-bounce reflections as double clusters following the COST 273/COST2100 method. This paper presents the complete parameterization of the channel model. We validate this model by comparing the delay spread and the angular spreads of arrival/departure obtained from the proposed model with the measurement data.

Scenario identification plays an important role in improving communication system performance. Considering that the scenarios of vehicle communications are dynamic due to movements of vehicles, and there are obvious differences in channel characteristics, vehicle speeds, traffic densities between various scenarios, the requirement for real-time scenario identification of vehicular communications is increasingly urgent. Vehicular communication systems can select appropriate channel models and transmission mode by correctly identifying the current scenarios to maintain an effective and reliable operating state. This paper presents a machine-learning-based scenario identification model for intelligent vehicular communications. Channel characteristics extracted from channel measurements in different scenarios form the datasets used to training, then a back-propagation neural network (BPNN) is trained, and a scenario identification model is obtained. Furthermore, the model configuration scheme is explored and presented which can make the proposed identification model achieves optimal performance. Subsequently, identification accuracy is verified by using validation data of the corresponding scenarios. The results show that the identification accuracies are all above 98 % in four typical scenarios of urban areas, highways, tunnels, and vehicle obstructions, which indicates that the model proposed in this paper shows good performance in scenario identification for intelligent vehicular communications.

Vehicle-to-vehicle (V2V) wireless communications have many envisioned applications for ensuring traffic safety and for addressing traffic congestion. However, developing suitable communication systems and standards for this purpose requires developers to have accurate models for the V2V propagation channel. Likewise, the dynamic evolution of multipath components (MPCs) in V2V channels has not been well modeled in existing models. In this paper, we propose a geometry-based stochastic channel model for a lightly built-up urban environment and then parametrize the model from measurements. The MPCs are extracted based on a high-resolution parameter estimation; they are tracked and clustered through a joint algorithm. The identified clusters are classified as line-of-sight, reflections from static scatterers, reflections from mobile scatterers, multiple-bounce reflections, and diffuse scattering. Specifically, the multiple-bounce reflections are modeled as twin clusters that follow the COST 273/COST2100 approach. The paper gives a full parameterization of the channel model and supplies a step-by-step implementation recipe. We verify the model by comparing two second-order statistics, i.e., the root-mean-square (RMS) delay spread and the angular spreads of arrival/departure derived from the channel model, to the results obtained directly from the measurements. Furthermore, we also identify several key factors that strongly impact the synthetic channel performance.

In the real propagation environments, multipath components (MPCs) in wireless channel usually exist as clusters. Cluster based structure of MPCs has been widely used in wireless channel modeling. In this paper, a novel MPC clustering algorithm is proposed based on spectral clustering. Considering that MPCs having strong power should usually be grouped into different clusters, the algorithm introduces a power-weighted processing to identify the similarity of each MPC. The process of weighting the power is conducted by generating similarity matrix using the full link method of Gaussian kernel function in the traditional spectral clustering algorithm. In order to achieve high clustering accuracy, the dimensionality reduction method of Laplacian Eigenmap is decomposed by using the normalized cut method, the obtained eigenvectors are re-clustered to cut the generated similarity matrix for MPC clustering. In the simulation results, it is found that this algorithm can well separate MPCs with high powers into different clusters and achieves better clustering performance compared with KPowerMeans, Kmeans, and the traditional spectral clustering algorithms.

Vehicle obstruction is a special scenario in vehicle-to-vehicle (V2V) communications. In this case, channel characteristics including path loss and spatial distributions are obviously different from other typical vehicular communication scenarios. However, the vehicle obstruction scenario is difficult to be identified by global navigation satellite systems (GNSS) or radars, so it is difficult for V2V communication systems to respond to sudden changes in channel characteristics due to vehicle obstructions. Therefore, by correctly identifying the vehicle obstruction scenarios, V2V communication systems can select appropriate propagation channel models to maintain an effective and reliable operating state. For this reason, this paper presents a machine-learning-based vehicle obstruction scenario identification approach for V2V communications. Channel characteristics extracted from measurements form the datasets used to training, then the back-propagation neural network (BPNN) is trained and a scenario identification model is obtained. Subsequently, identification accuracy is verified by using validation data. The results show that the identification accuracy for vehicle obstruction scenarios is more than 97%, which indicates that the approach proposed in this paper shows good performance in vehicle obstruction scenario identification in V2V communications.

Discriminating between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, orLOS identification, is important for a variety of purposes in wireless systems, including localization and channel modeling. LOS identification is especially challenging in vehicle-to-vehicle (V2V) networks since a variety of physical effects that occur at different spatial/temporal scales can affect the presence of LOS. This paper investigates machine learning techniques for LOS identification in V2V networks using an extensive set of measurement data and then develops robust and efficient identification solutions. Our approach exploits several static and time-varying features of the channel impulse response (CIR), which are shown to be effective. Specifically, we develop a fast identification solution that can be trained by using the power angular spectrum. Moreover, based on the measurement data, we also compare three different machine learning methods, i.e., support vector machine, random forest, and artificial neural network, in terms of their ability to train and generate the classifier. The results of our experiments conducted under various V2V environments, which were then validated using$K$-fold cross-validation, show that our techniques can distinguish the LOS/NLOS conditions with an error rate as low as 1%. In addition, we investigate the impact of different training and validating strategies on the identification accuracy.

A reliable vehicle-to-vehicle (V2V) channel model is necessary for intelligent transportation systems (ITSs) design. Due to the high mobility of vehicles and the low heights of antennas, the line-of-sight (LOS) propagation paths in V2V communications are more likely to be obstructed by large vehicles such as buses. Therefore, it is worthwhile to conduct in-depth investigations on obstructed line-of sight (OLOS) propagation channels caused by vehicle obstructions. In this paper, actual V2V channel measurements with large vehicle obstructions at 5.9 GHz band are conducted. Based on the measured data, it can be found that the obstructions of large vehicles not only cause additional attenuation, but also significantly affect the angular distribution of multipath components (MPCs). In addition, a cluster-based dynamic V2V channel model is proposed for OLOS scenarios. In the proposed model, the influences of vehicle obstructions on path loss, delay and angle dispersion are intuitively embodied as changes in the statistical distribution of MPCs clusters. Finally, the rationality and accuracy of the proposed model is validated by comparing the measured and simulated channels. The results in the paper are useful for enriching the understanding of V2V channels and provide supports for vehicular communication systems design and performance evaluation.

The wideband channel measurements at 580 MHz, 2.35 GHz and 4.90 GHz have been performed in the urban micro- and macrocell scenarios in the cities of China with multiple-input multiple-output (MIMO) channel sounder. The measured cases include line-of-sight (LOS) and non-line-of-sight (NLOS) propagation. Statistical results and comparative analysis for both scenarios are presented in this paper, including path loss (PL), root mean square (rms) delay spread (DS) and maximum excess delay (maxED), and angular spread (AS). In NLOS case, the frequency dependent factor (FDF) is observed as 32.1 by fitting the PL model of 2.35 GHz and 4.90 GHz. Moreover, a larger rms DS in urban microcell and AS at both base station (BS) and mobile subscriber (MS) are found for the denser and higher buildings in the cities of China. As the frequency varying from 580 MHz to 4.90 GHz, the median rms DS is decreased from 330 ns to 130 ns for NLOS case.

In orthogonal frequency division multiplexing (OFDM) systems, some subcarriers at the borders of the allocated bandwidth are not used for transmission as virtual subcarriers. Because of these subcarriers, some conventional channel estimators are not applicable, especially for multiple input multiple output (MIMO) systems. In this paper, our analysis on the effect of virtual subcarriers is briefly presented based on discrete Fourier transform (DFT) estimator in the delay domain. Then, a novel channel estimation approach, which can recover the dispersive distortion of estimated channel impulse response (CIR) caused by virtual subcarriers, is proposed for MIMO-OFDM systems. The proposed channel estimation does not need to know the path delay and is insensitive to the delay tap number. And it can easy be realized. The simulations demonstrate the effectiveness of the proposed approach in both narrow band and wide band mobile wireless channels.