Cell-free massive MIMO (CF-mMIMO) has emerged as a promising technology for next generation wireless systems, combining the benefits of distributed antenna systems (DAS) and traditional MIMO technology. In this work, we present the first extensive channel measurements for CF-mMIMO in the mid-band (FR3, 6-24 GHz), using a virtual widely distributed antenna array comprising 512 elements in the urban Macrocell (UMa) environment. Based on the measurement data, this paper compares the channel capacity of CF-mMIMO and Conventional mMIMO under both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions across a range of signal-to-noise ratios (SNRs). We then analyze how channel capacity varies with Rx positions from the perspectives of the full array and of individual subarrays. Finally, we conclude that the 64-element array configuration yields the greatest advantage in channel capacity for CF-mMIMO in the measurement environment considered, with gains of 14.02\% under LOS and 24.61\% under NLOS conditions. This in-depth analysis of channel capacity in the FR3 band provides critical insights for optimizing CF-mMIMO systems in next generation wireless networks.

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far-field and spatial stationarity assumptions in the existing 3rd generation partnership project (3GPP) channel models no longer valid, giving rise to new features such as near-field propagation and spatial non-stationarity (SNS).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.

With the full automation transformation of the railway industry and its close connection to the goals of high data rate, high reliability, and ultra-low latency communication, the demand for wireless communication technology in railway communication is increasing, especially in terms of accuracy in electromagnetic environment reconstruction. The accuracy of ray tracing (RT)-based electromagnetic environment reconstruction depends on the accuracy of electromagnetic (EM)) properties. Therefore, we propose an enhanced algorithm, which combines the advantages of logistic chaotic (LC) algorithm and simulated annealing (SA) algorithm. The proposed algorithm first utilizes the traversal property of LC algorithm for global search, and then uses SA algorithm for fine search for optimal solution. We validate the accuracy and applicability of algorithm in railway scenario by comparing the measurement results with the simulation results based on RT. After calibration, it will reduce the root mean square error between the measured parameters and the parameters of RT, and quickly converge to the minimum value.

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.

Extremely large-scale massive multiple-input multiple-output (XL-MIMO) is a potential enabling technology for 6th-generation (6G) communication. The XL-MIMO channel research will be important for XL-MIMO system development. In this paper, the measurement of XL-MIMO channels from 32 to 512 transmitting antenna elements in the 6 GHz band is carried out in an indoor corridor scenario. The delay spread, angular spread, and channel capacity are investigated. The results are compared with the indoor channel model in the Third Generation Partnership Project (3GPP) TR 38.901. We find that the number of antenna elements has a small impact on the delay spread and angular spread. So the spatial non-stationary effect does not need to be considered specifically in the far-field range in this scenario. In addition, the special structure of the corridor leads to a difference in the comparison of the angular spread with the 3GPP model in each dimension. The closed environment of the corridor also results in a significant gap in channel capacity performance from the i.i.d. channel. This work can provide insights into XL-MIMO applications in the 6G era.

Extremely Large-Scale Multiple-Input-Multiple-Output (XL-MIMO) communication, is recognized as a potential enabling technology for sixth-generation (6G) communication. Due to the large antenna aperture of XL-MIMO, spatial non-stationary (SnS) phenomena may occur on the array domain during deployment. This paper, relying on Ray-tracing (RT) simulations, analyzes the SnS phenomena from various perspectives of channel characteristics. Meanwhile, to accurately model the SnS phenomenon, this paper proposes a method for stationary sub-interval partitioning based on channel characteristics. It is assumed that the channel is stationary within each sub-interval, while it is non-stationary across different intervals. The method comprehensively considers factors such as channel correlation, delay spread (DS), azimuth angle spread of departure (ASD), and multipath components (MPCs) birth-death for sub-interval partitioning. By analyzing the independence of sub-intervals, this paper demonstrates that the proposed method performs better in sub-interval partitioning compared to the traditional averaging approach.

Massive multiple-input multiple-output (MIMO) is envisioned as a promising technology in 5G-Advanced and sixth-generation (6G) communication. Channel characteristics are important for the development and performance assessment of massive MIMO systems. Besides, the 6 GHz band (5925- 7125 MHz) has rapidly become the focus band of industry and academia at present. Therefore, this paper conducts the massive MIMO channel measurements in 6 GHz band in typical urban scenarios, and focuses on the massive MIMO channel characteristics in 6 GHz band. The important channel characteristic parameters such as power delay profile (PDP), delay spread, angular spread, channel capacity and data transmission rate are obtained and investigated, which are statistically different from Third Generation Partnership Project (3GPP) TR 38.901 model parameters. Besides, the impact of bandwidth on the system is important, and the great bandwidth allocation has the ability to provide higher transmission rates for future communication systems. Based on deeply exploration of the channel characteristics in 6 GHz band, this work hopes to provide some reference for the design and optimization of 6G communication systems.

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.

Abstract Near-field technology is increasingly recognized due to its transformative potential in communication systems, establishing it as a critical enabler for sixth-generation (6G) telecommunication development. This paper presents a comprehensive survey of recent advancements in near-field technology research. First, we explore the near-field propagation fundamentals by detailing definitions, transmission characteristics, and performance analysis. Next, we investigate various near-field channel models—deterministic, stochastic, and electromagnetic information theory based models, and review the latest progress in near-field channel testing, highlighting practical performance and limitations. With evolving channel models, traditional mechanisms such as channel estimation, beamtraining, and codebook design require redesign and optimization to align with near-field propagation characteristics. We then introduce innovative beam designs enabled by near-field technologies, focusing on non-diffractive beams (such as Bessel and Airy) and orbital angular momentum (OAM) beams, addressing both hardware architectures and signal processing frameworks, showcasing their revolutionary potential in near-field communication systems. Additionally, we highlight progress in both engineering and standardization, covering the primary 6G spectrum allocation, enabling technologies for near-field propagation, and network deployment strategies. Finally, we conclude by identifying promising future research directions for near-field technology development that could significantly impact system design. This comprehensive review provides a detailed understanding of the current state and potential of near-field technologies.

In the 5G-Advanced and beyond systems, multi-frequency cooperative networking will become an inevitable development trend. However, the channels have not been fully investigated at multi-frequency bands and in multi-scenarios by using the same channel sounder, especially for the sub-6 GHz to millimeter-wave (mmWave) bands. In this paper, we carry out channel measurements at four frequency bands (3.3, 6.5, 15, and 28 GHz) in two scenarios including Urban Micro (UMi) and Outdoor-to-Indoor (O2I) with the same channel sounder. The channel characteristics are extracted and modeled, including path loss (PL), shadow fading, frequency dependence of cluster features, root mean square (RMS) delay spread (DS), Ricean K-factor, and the correlation properties. We mainly focus on the analysis of large-scale parameters and more consideration of the link budget coverage problem. We present the frequency dependence model of the channel characteristics. Among them, in the non-line-of-sight (NLoS) condition, it is found that except for theoretical value brought by higher frequency, additional path loss increment will be generated. Based on these channel characteristics, the impact on performance of the wireless system is analyzed including cell coverage radius, data rate and bit error rate (BER). The results can give an insight into the spectrum selection and optimization in 5G-Advanced and beyond multi-frequency communication systems.

In this paper, the vehicle‐to‐vehicle (V2V) channel characteristics in peak hours at the 5.9 GHz band in two typical urban road scenarios, the urban straight road and the intersection, are investigated. The channel characteristics, such as path loss, root mean square (RMS) delay spread, and angular spread, are derived from the ray‐tracing (RT) simulations. Due to the low height of antennas at both the transmitter (Tx) and the receiver (Rx), the line of sight (LOS) between the Tx and the Rx will often be obstructed by other vehicles. Based on the RT simulation results, the shadowing loss is modelled by the multimodal Gaussian distribution, and path loss models in both LOS and non‐LOS (NLOS) conditions are obtained. And the RMS delay spread in two scenarios can be modelled by the Weibull distribution. In addition, the deployment of an antenna array is discussed based on the statistics distribution of the angular spread.