In this paper, a novel adaptive near-field channel model with an extremely large-scale multiple-input multiple-output (XL-MIMO) uniform planar array (UPA) is proposed for sixth generation (6G) multiple-uncrewed aerial vehicle (multi-UAV) cooperative communications. In the proposed model, a novel selective near-field area (SNA) of the XL-MIMO UPA, where the transmission is regarded as spherical wavefront, is proposed to balance complexity and accuracy of near-field channel modeling. To jointly model the non-stationarity on the array, and in the space, time, and frequency domains, an adaptive UPA-UAV-time-frequency non-stationary algorithm is developed, which mimics the non-stationarity on the XL-MIMO UPA for the first time. The channel parameters related to the three-dimensional (3D) continuously arbitrary trajectory and self-rotation of multi-UAVs are also taken into account in the proposed model and the developed algorithm. To explore the channel statistics and validate the proposed model, a new XL-MIMO-UPA-to-multi-UAV channel dataset at low-terahertz (low-THz) frequency band under National Stadium scenario is built. Key UPA-to-multi-UAV channel statistics, such as the array-space-time-frequency correlation function (ASTF-CF), time stationary interval (TSI), Doppler power spectral density (DPSD), and singular value spread (SVS), are obtained. The close agreement between the simulation results and ray-tracing results in National Stadium scenario is achieved, demonstrating the accuracy of proposed channel model.

Understanding wireless channels is crucial for the design of wireless systems. For mobile communication, sounders and antenna arrays with short measurement times are required to simultaneously capture the dynamic and spatial channel characteristics. Switched antenna arrays are an attractive option that can overcome the high cost of real arrays and the long measurement times of virtual arrays. Optimization of the switching sequences is then essential to avoid aliasing and increase the accuracy of channel parameter estimates. This paper provides a novel and comprehensive analysis of the design of switching sequences. We first review the conventional spatio-temporal ambiguity function, extend it to dual-polarized antenna arrays, and analyze its prohibitive complexity when applied to ultra-massive antenna arrays. We thus propose a new method that uses the Fisher information matrix to tackle the estimation accuracy. We also propose to minimize the ambiguity by choosing a switching sequence that minimizes side lobes in its Fourier spectrum. In this sense, we divide the sequence design problem into Fourier-based ambiguity reduction and Fisher-based accuracy improvement, and coin the resulting design approach as Fourier-Fisher. Simulations and measurements show that the Fourier-Fisher approach achieves identical performance and significantly lower computational complexity than that of the conventional ambiguity-based approach.

Distributed multiple-input multiple-output (MIMO), also known as cell-free massive MIMO, emerges as a promising technology for sixth-generation (6G) systems to support uniform coverage and reliable communication. For the design and optimization of such systems, measurement-based investigations of real-world distributed MIMO channels are essential. In this paper, we present a sub-6 GHz indoor channel measurement campaign, featuring eight distributed antenna arrays with 128 elements in total. Multi-link channels are measured at 50 positions along a 12-meter user route. A clustering algorithm enabled by interacting objects is proposed to identify clusters in the measured channels. The algorithm jointly clusters the multipath components for all links, effectively capturing the dynamic contributions of common clusters to different links. In addition, a Kalman filter-based tracking framework is introduced for cluster prediction, tracking, and updating along the user movement. Using the clustering and tracking results, cluster-level characterization of the measured channels is performed. First, the number of clusters and their visibility at both link ends are analyzed. Next, a maximum-likelihood estimator is utilized to determine the entire cluster visibility region length. Finally, key cluster-level properties, including the common cluster ratio, cluster power, shadowing, spread, among others, are statistically investigated. The results provide valuable insights into cluster behavior in typical multi-link channels, necessary for accurate modeling of sub-6 GHz distributed MIMO channels.

Hybrid analog-digital beamforming stands out as a key enabler for future communication systems with a massive number of antennas. In this paper, we investigate the hybrid precoder design problem for angle-of-departure (AoD) estimation, where we take into account the practical constraint on the limited resolution of phase shifters. Our goal is to design a radio-frequency (RF) precoder and a base-band (BB) precoder to estimate AoD of the user with high accuracy. To this end, we propose a two-step strategy where we first obtain the fully digital precoder that minimizes the angle error bound, and then the resulting digital precoder is decomposed into an RF precoder and a BB precoder, based on the alternating optimization and the alternating direction method of multipliers. Furthermore, we derive the quantization error upper bound and provide convergence conditions for the proposed algorithm. Numerical results demonstrate the superior performance of the proposed method over state-of-the-art baselines.

Indoor sensing is challenging because of the multi-bounce effect, spherical wavefront, and spatial nonstationarity (SNS) of the near-field effect. This paper addresses radio-based environment sensing considering these issues. Specifically, graph theory (GT) is used to model the multi-bounce propagation of the near field. In this manner, indoor reflectors/scatterers are modeled as vertices in a propagation graph, the multi-bounce paths are modeled by the edges linking the vertices. Besides, the coupled multipath parameters in the near field, i.e., range and angles, are denoted directly by the coordinates of vertices. Then, the space-alternating generalized expectation-maximization (SAGE) algorithm is adapted to the proposed GT-based dictionary-aided multi-bounce SAGE (GM-SAGE), where the searching parameters including range and angle of departure/arrival (AoD/AoA) are transformed to the coordinates of vertices in the graph. To accelerate the two-bounce sensing, a recursive strategy is proposed. Furthermore, geometric information-based radio-sensing ambiguities are analyzed. The proposed algorithm is validated through a synthetic scattering channel and realistic ray tracing (RT) in a complex indoor office. The results demonstrate that the proposed GM-SAGE can deal with multi-bounce channels, where the one-bounce paths and near-field two-bounce paths are used to reconstruct the scatterers in the environment, the residual higher-bounce paths are identified and hence avoid ghost/mirror detection. Numerical simulations also show the influence of signal-noise ratio (SNR), grid size of vertices, aperture size, and near-far-field effects.

A deep understanding of double-directional wireless channels is imperative for wireless system design. This necessitates the development of precise channel models through channel sounding. Given the dynamic nature of wireless channels, a short measurement time is highly desirable. In this paper, we present and validate a dual-polarized millimeter-wave channel sounder that uses a rotating mirror mechanism. This enables quick sweep of the azimuthal plane to capture spatial channel characteristics at 27.5-28.5 GHz in less than a second. This is radically faster than conventional virtual-array channel sounders. Following a detailed discussion of the sounder implementation and the rotating mirror concept, we present an indoor verification measurement scenario. The results demonstrate several discernible propagation paths with parameters that agree with the actual geometry of the indoor measurement environment.

User grouping based on geographic location in the fifth generation (5G) New Radio (NR) systems has several applications that can significantly improve network performance, user experience, and service delivery. We demonstrate how Sounding Reference Signals channel fingerprints can be used for dynamic user grouping in a 5G NR commercial deployment based on outdoor positions and heading direction employing machine learning methods such as neural networks combined with clustering methods.

Millimeter-wave (mmWave) technology holds the potential to revolutionize head-mounted displays (HMDs) by enabling high-speed wireless communication with nearby processing nodes, where complex video rendering can take place. However, the sparse angular profile of mmWave channels, coupled with the narrow field of view (FoV) of patch-antenna arrays and frequent HMD rotation, can lead to poor performance. We introduce six channel performance metrics to evaluate the performance of an HMD equipped with mmWave arrays. We analyze the metrics using analytical models, discuss their impact for the application, and apply them to 28 GHz channel sounding data, collected in a conference room using eight HMD patch-antenna arrays, offset by 45° from each other in azimuth. Our findings confirm that a single array performs poorly due to the narrow FoV, and featuring multiple arrays along the HMD’s azimuth is required. Namely, the broader FoV stabilizes channel gain during HMD rotation, lessens the attenuation caused by line of sight (LoS) obstruction, and increases the channel’s spatial multiplexing capability. In light of our findings, we conclude that it is imperative to either equip the HMD with multiple arrays or, as an alternative approach, incorporate macroscopic diversity by leveraging distributed access point (AP) infrastructure.

In this paper, a novel three-dimensional (3D) irregular shaped geometry-based stochastic model (IS-GBSM) is proposed for sixth-generation (6G) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) channels. To investigate the impact of vehicular traffic density (VTD) on channel statistics, clusters are divided into static clusters and dynamic clusters, which are further distinguished into static/dynamic single/twin-clusters to capture the mixed-bouncing propagation. A new method, which integrates the visibility region and birth-death process methods, is developed to model space-time-frequency (S-T-F) non-stationarity of V2V channels with time-space (T-S) consistency. The continuously arbitrary vehicular movement trajectory (VMT) and soft cluster power handover are modeled to further ensure channel T-S consistency. From the proposed model, key channel statistics are derived. Simulation results show that S-T-F non-stationarity of channels with T-S consistency is modeled and the impacts of VTD and VMT on channel statistics are analyzed. The generality of the proposed model is validated by comparing simulation results and measurement/ray-tracing (RT)-based results.

High-precision cellular-based localization is one of the key technologies for next-generation communication systems. In this paper, we investigate the potential of applying machine learning (ML) to a massive multiple-input multiple-output (MIMO) system to enhance localization accuracy. We analyze a new ML-based localization pipeline that has two parallel fully connected neural networks (FCNN). The first FCNN takes the instantaneous spatial covariance matrix to capture angular information, while the second FCNN takes the channel impulse responses to capture delay information. We fuse the estimated coordinates of these two FCNNs for further accuracy improvement. To test the localization algorithm, we performed an indoor measurement campaign with a massive MIMO testbed at 3.7 GHz. In the measured scenario, the proposed pipeline can achieve centimeter-level accuracy by combining delay and angular information.

Channel sounding is essential for the development of radio systems. One flexible strategy is the switched-array-based channel sounding, where antenna elements are activated at different time instants to measure the channel spatial characteristics. Although its hardware complexity is decreased due to fewer radio-frequency (RF) chains, sequentially switching the antenna elements can result in aliasing in the joint estimation of angles and Doppler frequencies of multipath components (MPCs). Therefore, pseudo-random switching has been proposed to mitigate such aliasing and increase estimation accuracy in both angular and Doppler domains. Nevertheless, the increased Doppler resolution could cause additional post-processing complexity of parameter estimation, which is relevant when the Doppler frequencies are not of interest, e.g., for spatial channel modeling. This paper proposes an improved hybrid sequential and random switching scheme. The primary purpose is to maintain the estimation accuracy of angles of MPCs while decreasing the resolution of Doppler frequencies for minimized complexity of channel parameter estimation. A simulated-annealing algorithm is exploited to obtain an optimized switching sequence. The effectiveness of the proposed scheme is also demonstrated with a realistic antenna array.

The performance and reliability of wireless communications at sea are often limited by the deep fades caused by the coherent sea surface reflection. In this paper, we show that by employing multiple antennas at the base station, the deep fades can be mitigated within a large communication range if the antennas are carefully spaced in the vertical direction. We derive a bound for the range where mitigation of deep fading is guaranteed and evaluate this bound through numerical searching methods utilizing a realistic channel model that considers the curvature of the earth. The numerical results show that the proposed scheme leads to a better system performance compared with a free space scenario in terms of signal-to-noise ratio, if four or more antennas are employed at the base station.

Cellular-connected unmanned aerial vehicles (UAVs) have recently attracted a surge of interest in both academia and industry. Understanding the air-to-ground (A2G) propagation channels is essential to enable reliable and/or high-throughput communications for UAVs and protect the ground user equipments (UEs). In this contribution, a recently conducted measurement campaign for the A2G channels is introduced. A uniform circular array (UCA) with 16 antenna elements was employed to collect the downlink signals of two different Long Term Evolution (LTE) networks, at the heights of 0-40m in three different, namely rural, urban and industrial scenarios. The channel impulse responses (CIRs) have been extracted from the received data, and the spatial, including angular, parameters of the multipath components in individual channels were estimated according to a high-resolution-parameter-estimation (HRPE) principle. Based on the HRPE results, clusters of multipath components were further identified. Finally, comprehensive spatial channel characteristics were investigated in the composite and cluster levels at different heights in the three scenarios.

During the last few years, with the decrease of their sizes and costs, Unmanned Aerial Vehicles (UAVs) became more feasible for their use in general-purpose applications. As an important basis for UAV applications, the Air-to-Ground (A2G) radio propagation channel has gained attention in the channel modeling literature. However, whereas the A2G propagation channel is inherently dynamic (time-varying), the majority of the available models do not consider such a time-variability. This paper proposes a channel multi-path component (MPC) tracking algorithm and shows its ability to analyze the data collected by a real A2G measurement campaign in a suburban environment. Based on the obtained results, a time-varying statistical channel model for A2G communications in realistic suburban scenarios is proposed. The model is able to stochastically characterize parameters related to the birth and time-of-life of the multi-path components (MPCs), as well as their evolution in terms of delay, Doppler frequency or magnitude. Correlation coefficients to relate different channel characteristics are also obtained. Our work shows that the MPCs evolution over time for the UAV A2G channel can be described by simple regular patterns.

In parallel with the decrease in cost, size and weight of Unmanned Aerial Vehicles (UAVs) and the increase of their flight autonomy, many commercial applications are rapidly arising. Most of those applications rely on a communications system between a terrestrial base station and the UAV. Due to the UAV movement, time-variant channel models are required. In this article, we propose a geometrical model for the channel Multipath Components (MPCs) evolution with the UAV flight that supports MPCs that are born and die in several occasions due to blockages. Based on this model, the novel Geometry-Based Spatial-Consistent MPC Tracking Method (GSTM) is proposed and its performance on channel MPCs tracking was shown both by simulations and by an Air-to-Ground (A2G) low-height UAV measurement campaign. The GSTM also provides the parameters of a geometrical model of the evolution of the main MPCs of the channel, which allows to identify the scatterers that lead to the MPCs and greatly contributes to the understanding of the propagation mechanisms in A2G environments. The correctness of the MPC tracking is proven to be higher than 90% and the results show that the model obtained by the GSTM includes more than 95% of the received power.

In this contribution, a recently conducted measurement campaign for indoor millimeter-wave (mm-wave) propagation channels is introduced. A vector network analyzer (VNA)-based channel sounder was exploited to record the channel characteristics at the frequency band from 28-30 GHz. A virtual uniform circular array (UCA) with a radius of 0.25 m was formed using a rotator with 360 steps. Moreover, by taking advantage of fiber-optic technique applied in the channel sounder, measurements at 50 positions were performed from an indoor hall to an indoor corridor along a long pre-defined route. A low-complexity high-resolution propagation estimation (HRPE) algorithm is exploited to estimate the propagation parameters of multipath components (MPCs). Based on the HRPE estimation results, a novel clustering identification and tracking algorithm is proposed to trace clusters. Composite channel characteristics, cluster-level characteristics and dynamic (or birth-death) behaviours of the clusters are investigated, which constitute a dynamic model for the indoor mm-wave channel.

Magnetic object localization technology is emerging to bring substantial benefits for human medical investigation, such as tracking wireless endoscopic devices or catheters with embedded permanent magnets. Towards accurate magnetic localization, we propose a novel method of fitting the magnetic field intensity and magnetic gradient tensor (MGT) with artificial neural network (ANN) for magnetic object localization, which permits accurate localization of the magnetic object in a certain range. In the simulation, we analyzed and compared the capability of the proposed method and a traditional method based on tensor module gradient. The results show that the average localization error of the proposed method is at least 8 times more accurate than the traditional method in the noisy environment. Besides, a magnetic localization system based on a sensor array was used to carry out a localization experiment and the result has shown the feasibility of the proposed method with the average localization error of 2.15 cm when the magnetic target distance was from 61 to 79 cm.

Millimeter wave (mm-wave) communication with large-scale antenna array configuration is seen as the key enabler of the next generation communication systems. Accurate knowledge of the mm-wave propagation channels is fundamental and essential. In this contribution, a novel complexity-efficient high resolution parameter estimation (HRPE) algorithm is proposed for the mm-wave channel with large-scale uniform circular array (UCA) applied. The proposed algorithm is able to obtain the high-resolution estimation results of the spherical channel propagation parameters. The prior channel information in the delay domain, i.e., the delay trajectories of individual propagation paths observed across the array elements, is exploited, by combining the high-resolution estimation principle and the phase mode excitation technique. Fast initializations, effective interference cancellations, and reduced searching spaces achieved by the proposed schemes significantly decrease the algorithm complexity. Furthermore, the channel spatial non-stationarity in path gain across the array elements is considered for the first time in the literature for propagation parameter estimation, which is beneficial to obtain more realistic results as well as to decrease the complexity. A mm-wave measurement campaign at the frequency band of 28-30GHz using a large-scale UCA is exploited to demonstrate and validate the proposed HRPE algorithm.

This paper presents a measurement-based analysis of 39 GHz millimeter wave (mm-wave) radio propagation through vegetation using an ultra-wideband (UWB) channel sounder. The measurement campaign was conducted in a suburban area on campus of Tongji University, Shanghai, China. Two different kinds of measurements were performed to investigate the foliage influence on the characteristics of radio propagation in the mm-wave bands. A direction-scan sounding (DSS) approach was used in the measurements aiming to explore the vegetation attenuation as well as dispersive characteristics of the propagation channel. Approximately 6.7 to 8.7 dB single-tree attenuation and 1.86 to 2.23 dB/m vegetation attenuation were observed at the so-called boresight aligned direction (BAD) of a transmitter (Tx) and a receiver (Rx). The presence of vegetation leads to dispersion in delay and angular domains. Both delay spread and azimuth of arrival spread were proportional to the number of trees existing between the Tx and the Rx. Measurements with the purpose of investigating the relationship between the foliage loss and Tx/Rx distances away from trees were also conducted.

In this contribution, a measurement campaign for high-speed-train (HST) channels is introduced, which collects the down-link signals of an in-service Long Time Evolution-Advanced (LTE-A) network deployed along the HST railway from Beijing to Shanghai, China. The channel impulse responses (CIRs) are extracted from the received signals, and the concatenated power delay profiles (CPDPs) of the CIRs are illustrated. Measurement scenarios of interest are separated into tunnel and non-tunnel propagation categories according to the delay trajectories of dominant components in channels. The statistics of the delay spreads and K-factor of the channels are investigated for both scenarios. The results illustrate clear distinctions of these characteristics for the two scenarios.

Recently, a measurement campaign for characterizing the channels in underground subway environments was conducted in Shanghai, China. Downlink signals transmitted by 46 universal mobile telecommunication system cells deployed along a 34-km-long subway were collected. Channel impulse responses are extracted from the data received in the common pilot channels, based on which parameters of multipath components are estimated by using a high-resolution parameter algorithm derived using the space-alternating generalized expectation-maximization principle. Multiple time-evolving clusters are obtained, each representing the channel from a remote-radio-unit of a base station to the receiver. Based on a total of 98 time-evolving clusters, channels observed in the station scenario and the tunnel scenario are modeled separately for their distinctive behaviors in many aspects, particularly in the variations of clusters’ trajectories. Intracluster characteristics parameterized by cluster delay and Doppler frequency spreads,$K$-factor, and dependences among these parameters are investigated. Intercluster parameters, including coexisting cluster number, delay offset, power offset, and cross correlations, are investigated for the station scenario. A path loss model is established for the tunnel scenario.

In this paper, a recently conducted measurement campaign for high-speed-train (HST) channels is introduced, where the downlink signals of an in-service Universal Mobile Terrestrial System (UMTS) deployed along an HST railway between Beijing and Shanghai were acquired. The channel impulse responses (CIRs) are extracted from the data received in the common pilot channels (CPICHs). Within 1318 km, 144 base stations (BSs) were detected. Multipath components (MPCs) estimated from the CIRs are clustered and associated across the time slots. The results show that, limited by the sounding bandwidth of 3.84 MHz, most of the channels contain a single line-of-sight (LoS) cluster, and the rest consists of several LoS clusters due to distributed antennas, leaking cable, or neighboring BSs sharing the same CPICH. A new geometry-based random-cluster model is established for the clusters' behavior in delay and Doppler domains. Different from conventional models, the time-evolving behaviors of clusters are characterized by random geometrical parameters, i.e., the relative position of BS to railway, and the train speed. The distributions of these parameters, and the per-cluster path loss, shadowing, delay, and Doppler spreads, are extracted from the measurement data.