Next generation cellular deployments are expected to exploit the 6–24 GHz frequency range 3 (FR3) and extremely large-scale multiple-input multiple-output (XL-MIMO) to enable ultra-high data rates and reliability. However, the significantly enlarged antenna apertures and higher carrier frequencies make the far-field and spatial stationarity assumptions in the existing 3rd generation partnership project (3GPP) channel models no longer valid, giving rise to new features such as near-field propagation and spatial non-stationarity (SNS). Despite extensive prior research, incorporating these new features within the standardized channel modeling framework remains an open issue. To address this, this paper presents a channel modeling framework for XL-MIMO systems that incorporates both near-field and SNS features, adopted by 3GPP. For the near-field propagation feature, the framework models the distances from the base station (BS) and user equipment to the spherical-wave sources associated with clusters. These distances are used to characterize element-wise variations of path parameters, such as nonlinear changes in phase and angle. To capture the effect of SNS at the BS side, a stochastic-based approach is proposed to model SNS caused by incomplete scattering, by establishing power attenuation factors from visibility probability and visibility region to characterize antenna element-wise path power variation. In addition, a physical blocker-based approach is introduced to model SNS effects caused by partial blockage. The near-field and SNS channel modeling approaches are validated against ray-tracing simulations. Finally, a simulation framework for near-field and SNS is developed based on the existing 3GPP channel model. Performance evaluations demonstrate that the near-field model captures higher channel capacity potential compablack to the far-field model. Coupling loss results indicate that SNS leads to more pronounced propagation fading relative to the spatial stationary model.

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.

Terahertz (THz) extremely large-scale MIMO (XL-MIMO) is considered a key enabling technology for 6G and beyond due to its advantages such as wide bandwidth and high beam gain. As the frequency and array size increase, users are more likely to fall within the near-field (NF) region, where the far-field plane-wave assumption no longer holds. This also introduces spatial non-stationarity (SnS), as different antenna elements observe distinct multipath characteristics. Conventional far-field stationary models with fixed path parameters fail to capture these variations. Therefore, this paper proposes a THz XL-MIMO channel model that accounts for both NF propagation and SnS, validated using channel measurement data. In this work, we first conduct THz XL-MIMO channel measurements at 100 GHz and 132 GHz using 301- and 531-element ULAs in indoor environments, revealing pronounced NF effects characterized by nonlinear inter-element phase variations, as well as element-dependent delay and angle shifts. Moreover, the SnS phenomenon is observed, arising not only from blockage but also from inconsistent reflection or scattering. Based on these observations, a hybrid NF channel modeling approach combining the scatterer-excited point-source model and the specular reflection model is proposed to capture nonlinear phase variation of different types of non-line-of-sight (NLoS) paths. For SnS modeling, amplitude attenuation factors (AAFs) are introduced to characterize the continuous variation of path power across the array. By analyzing the statistical distribution and spatial autocorrelation properties of AAFs, a statistical rank-matching-based method is proposed for their generation. Finally, the model is validated using measured data. Evaluation across metrics such as entropy capacity, condition number, spatial correlation, channel gain, Rician K-factor, and RMS delay spread confirms that the proposed model closely aligns with measurements and effectively characterizes the essential features of THz XL-MIMO channels.

Multiple-input-multiple-output (MIMO) has been a promising technology in wireless communication systems. Channel models are of great importance for the development and assessment of system. With the increase of carrier frequency and MIMO size, the channel model needs to consider near-field spherical wave and spatial non-stationary characteristics, which is different from conventional far-field planar-wave-based geometry-based stochastic model (GBSM) in the 3rd Generation Partnership Project (3GPP). This paper focuses on comparing the channel characteristics and modeling in the far- and near-field region. In this work, we design the measurement campaign in the 6 GHz band (5.9-6.1 GHz) involving the unlicensed spectrum. The uniform planar array (UPA) is adopted from far-field to near-field, where the communication distance is decreasing from 21 m to 6 m (Rayleigh distance is about 14.8 m). Compared to the far-field, the spatial non-stationary phenomenon of channel parameters can be more clearly observed along the array in the near-field region. Then, we propose the extension channel model based on the channel modeling of 3GPP TR 38.901. The array domain is introduced to characterize the spatial non-stationarity of channel parameters (e.g., power, delay, angle). Subsequently, the channel characteristic parameters along the array are analyzed in the near-field range, and the non-stationary model related to the antenna array is established, including power, path loss, delay spread, angular spread, and Ricean K-factor. Finally, the model validation and parametrization are presented in detail with the actual indoor near-field MIMO channel measurements in the 6 GHz band, such as power, angle, and so on. The design and scheme of antenna array spacing are given under the influence of spatial non-stationary characteristics. These work will be helpful for the development and operation of MIMO technology in unlicensed spectra for wireless communication systems.

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.