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.

With the development of sixth generation (6G) networks toward digitalization and intelligentization of communications, rapid and precise channel prediction is crucial for the network potential release. Interestingly, a dynamic ray tracing (DRT) approach for channel prediction has recently been proposed, which utilizes the results of traditional RT to extrapolate the multipath geometry evolution. However, both the priori environmental data and the regularity in multipath evolution can be further utilized. In this work, an enhanced-dynamic ray tracing (E-DRT) algorithm architecture based on multipath bidirectional extrapolation has been proposed. In terms of accuracy, all available environment information is utilized to predict the birth and death processes of multipath components (MPCs) through bidirectional geometry extrapolation. In terms of efficiency, bidirectional electric field extrapolation is employed based on the evolution regularity of the MPCs’ electric field. The results in a Vehicle-to-Vehicle (V2V) scenario show that E-DRT improves the accuracy of the channel prediction from 68.3% to 94.8% while reducing the runtime by 7.2% compared to DRT.

Due to the fact that the scale and construction of 6G communication systems are progressing toward unprecedentedly large and complex, accuracy and robustness of channel modeling method are vital to ensure that communication systems can work efficiently. Ray-Tracing (RT), as a precise deterministic modeling method, can meet most of these requirements. Shooting and Bouncing Rays (SBR) is a forward RT algorithm with high efficiency and wide usage. Traditional SBR utilizes environmental prior information, like distribution of scatters, only in path-finding. Channel parameters like quantity of rays are determined artificially, which affects SBR’s performance especially in 6G systems. In order to enable SBR to conduct 6G modeling stably and efficiently, we propose an advanced SBR algorithm that utilizes environmental prior information globally for both path-finding progress and parameter-determination. Space-division, face-classification are some environmental-information-assisted processes of the proposed algorithm, which help to calculate channel parameters quantitatively. Additionally, our new algorithm accelerates path-finding progress by decreasing unnecessary intersections through space-division. Simulation reveals our new algorithm reduces computational complexity by 34.53% in comparison of traditional SBR. Furthermore, with Image Method (IM) as a standard, new algorithm has smaller Normalized Mean Squared Error (NMSE) of 78.09% less in LOS-path ignoring power-receiving, compared to traditional SBR. Other metrics such as Power-Delay Profile (PDP) are also demonstrated in our work.

Smart factory scenarios in the Industrial Internet of Things (IIoT) introduce novel challenges to wireless communication, characterized by large-scale device interaction, strong reflection, and dense scattering, diverging from typical factories. To ascertain the channel disparities between smart factories and typical factories, we analyze channel characteristics across sub-6 GHz (e.g., 6 GHz) and mmWave frequencies (e.g., 28 and 39 GHz) in smart factory scenarios, establishing a multi-frequency path loss model. Furthermore, the comparison between the large-scale fading characteristics, including path loss and root mean square delay spread (RMS DS), observed in smart factory scenarios and those in the typical indoor factory (InF) scenarios defined by the 3rd-Generation Partnership Project (3GPP). Additionally, channel characteristics of the metal and non-metal scatterer areas in the smart factories are extracted to analyze the impact of metal scatterers on angle spread (AS) and RMS DS.

Traditional path loss methods typically employ statistical or empirical models, without fully considering the dynamic propagation environment. In this paper, we introduce a method called Visual Sensing-Based Path Loss Prediction (VSB-PLM), which predicts path loss using visual data obtained from multi-view sensing cameras. Specifically, we deploy multi-view cameras in real-world scenarios. Then, a Convolutional Neural Network (CNN) is designed to integrate environmental image features, the existence of the Line-Of-Sight (LOS) path, and the distance between the Transmitter (Tx) and Receiver (Rx) for path loss prediction. Finally, optimal path loss prediction results are obtained utilizing a multi-view selection algorithm. Simulation results demonstrate that the proposed algorithm has successfully improved path loss prediction accuracy by 9% compared to single-view sensing, achieving a Root Mean Squared Error (RMSE) of 3.66 dB.

Massive Multiple Input Multiple Output (MIMO) at millimeter-Wave (mmWave) frequencies is envisioned as a key technology for beyond 5G communication. Accurate channel modeling is crucial for the design and evaluation of such systems. Ray-Tracing (RT) can be used to accurately simulate the propagation channel. However, state-of-the-art RT for multi-antenna systems typically employs plane-wave extension under far-field conditions, failing to capture Near-Field (NF) and Spatial non-Stationary (SnS) properties that are observed in real-world mmWave massive MIMO channel measurements. This work aims at accurate and efficient RT simulations for massive MIMO systems, with a proposed coarse-refinement strategy capable of capturing NF and SnS. The channel is simulated using RT on a few sparsely located array elements and then interpolated onto other elements using spherical/astigmatic-wave approximation and the Uniform Theory of Diffraction, thus significantly reducing simulation complexity while maintaining accuracy. The proposed strategy is demonstrated to offer almost the same simulation accuracy as the brute-force method, with a dramatic reduction in complexity through experimental validation. The significance, novelty, effectiveness, and simplicity of the proposed framework make it highly valuable for massive MIMO channel research.

To reduce the downlink beam sweep overhead of mmWave systems, we propose a deep reinforcement learning based dynamic beam selection (DRL-DBS) method. A new learning motivation is presented by analyzing the dynamic change laws of high- and low-frequency channels in the spatial domain: to learn the index offset between the optimal beam of mmWave and sub-6 GHz spatial spectrum. In the DRL-DBS method, we propose a novel action space where actions can dynamically adjust the size of the beam sweep subset according to the high-and low-frequency channel propagation laws. Hence, the DRL-DBS method can predict a mmWave downlink beam sweep subset with dynamic size, and the optimal beamforming index is from beam sweep results on the subset. A dual-input dueling Q-network with noisy networks and prioritized experience replay is designed to select the optimal action. The DRL-DBS method can achieve a dynamic trade-off between mmWave beam selection quality and beam sweep overhead based on the reward function. Simulation results demonstrate the superior performance of the DRL-DBS method compared with the existing strategies. Especially, the DRL-DBS method outperforms the exhaustive search algorithm in achievable rate because the overhead of mmWave beam sweep is considered.

The over-the-air (OTA) testing based on multi-probe anechoic chamber (MPAC) is an efficient solution to evaluate the performance of 5G multiple-input multiple-output (MIMO) capable devices, which can reconstruct the wireless channel within the lab in a controlled manner. For different test requirements, the probe layouts of the MPAC may be varied, bringing a lot of additional hardware overhead. In this paper, a novel design of the MPAC test system is proposed and constructed to meet various mainstream 5G OTA test solutions. By adopting switch matrixes and the three-dimensional (3D) quick probe selection algorithm, a 3D MPAC can be easily adjusted to different probe layouts. The solution can save at most 3/4 of the hardware port resources while ensuring the channel emulation accuracy. Channel validation and 5G terminals performance testing are carried out in the new system. The result comparison demonstrates the effectiveness of the probe simplification scheme and the system design.

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.

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.

Environmental modeling errors have significant influence on the accuracy of ray tracing (RT) based channel modeling method. The aim of this paper is to provide reference for determining the appropriate accuracy of indoor databases to achieve prediction results within tolerance. Therefore in this paper, we discuss the influence of modeling errors on the RT-based channel modeling accuracy for millimeter-wave frequency bands. Different from the previous work, our discussion mainly foucus on the indoor environments and try to analyze the relationship between the channel parameters including received power, delay spread (DS), angle spreads (AS) and the types of scatterers. Simulation results show the RT results are more sensitive to the geometric errors caused by the room boundary in the line-of-sight (LOS) case while indoor scatterers have a greater impact in the non-LOS (NLOS) case. Additionally, AS is more sensitive to geometric errors of environment than DS. These results have guiding significance for channel modeling and simulation work using RT.

Previous studies have shown that introducing real principal component analysis (PCA) into wireless MIMO channel modeling can effectively improve its accuracy. However, wireless channel data is mostly in the form of complex-valued channel impulse response (CIR) and thus real PCA is very difficult to recover the phase of channel. Under such a motivation, this paper proposes a novel complex PCA-based MIMO channel modeling methodology. First, we prove that PCA can be used to analyze the measured complex-valued CIR data with the principle of maximizing the converted channel power. Then the representative channel features are extracted, which are utilized to reconstruct the CIR finally. An indoor measurement with 32 × 56 antennas is performed for validation. The results clearly show that the proposed channel model is more accurate compared to the real PCA-based model, and is tightly close to the measured channel. Moreover, the proposed methodology is robust with the increase of antennas, which provides insights into massive MIMO channel modeling in the future.

By taking advantage of the elevation domain, three-dimensional (3-D) multiple input and multiple output (MIMO) with massive antenna elements is considered as a promising and practical technique for the fifth Generation mobile communication system. So far, 3-D MIMO is mostly studied by simulation and a few field trials have been launched recently. It still remains unknown how much does the 3-D MIMO meet our expectations in versatile scenarios. In this paper, we answer this based on measurements with 56 × 32 antenna elements at 3.5 GHz with 100-MHz bandwidth in three typical deployment scenarios, including outdoor to indoor (O2I), urban microcell (UMi), and urban macrocell (UMa). Each scenario contains two different site locations and 2-5 test routes under the same configuration. Based on the measured data, both elevation and azimuth angles are extracted and their stochastic behaviors are investigated. Then, we reconstruct two dimensional and 3-D MIMO channels based on the measured data, and compare the capacity and eigenvalues distribution. It is observed that 3-D MIMO channel which fully utilizes the elevation domain does improve capacity and also enhance the contributing eigenvalue number. However, this gain varies from scenario to scenario in reality, O2I is the most beneficial scenario, then followed by UMi and UMa scenarios. More results of multiuser capacity varying with the scenario, antenna number and user number can provide the experimental insights for the efficient utilization of 3-D MIMO in future.

In this paper, we investigate the statistical characteristics of the three dimensional (3D) multiple- input multiple-output (MIMO) channels and their dependency on the dynamic range in channel impulse responses (CIRs). Our study is based on empirical results obtained from the 3D MIMO channel measurement campaigns at 6 GHz in a typical urban macrocell outdoor-to-indoor scenario. The space-alternating generalized expectation-maximization (SAGE) algorithm is employed to estimate parameters of multipath components from the measured data. The measured data is divided into groups based on the dynamic range in the measured CIRs. The second-order statistics in time and spatial domains are calculated for each group. It is found that the channel statistics shows strong dependency on the dynamic range in the measured CIRs.