This paper studies the near-field localization problem under dynamic scenarios, which harnesses the non-line-of-sight (NLoS) components, in a reconfigurable intelligent surface (RIS)-aided system equipped with extremely large-scale multi-input multi-output (XL-MIMO). To reduce the complexity of the position estimation, the subarray far-field model is employed to approximate the near-field channel. A factor graph within a Bayesian framework is constructed to detail the probability transition relationship among the relevant variables. Based on the message passing in this factor graph, a near-field localization algorithm is developed to estimate the marginal probability distributions of the UE’s and scatterers’ positions in each time slot. The misspecified Cramér-Rao Lower Bound (MCRLB) is derived to evaluate the performance of the algorithm under the subarray far-field model. To explore the localization potential of the system, a closed-form solution for a low-complexity directional beamforming design and a robust beamforming design based on the gradient descent method (GDM) are further proposed. Numerical results demonstrate that the proposed algorithm outperforms the benchmark schemes, and validate the performance gain of harnessing the NLoS components.

With the rapid development of emerging wireless communication technologies, there is a concurrent surge in data volume alongside urging demands for real-time data processing in time-varying channels. Existing environment sensing methods can hardly realize real-time dynamic sensing due to the large amount of post-processing requirements on the static channel components. Therefore, the decomposition of static and dynamic channels has become a great challenge, contributing to efficient dynamic sensing. In this paper, a subspace-based method is proposed to separate the common and non-common components of the channel matrix, which is further used for static and dynamic channel sensing, respectively. The spatial and frequency subspaces of two channel state informations (CSIs) are extracted within the coherence time by eigenbasis projection, respectively, from which the common and non-common subspaces are separated to perform compressed sensing (CS) based CSI feedback. Spectrum-based analysis of the separated results by simulation and measurement is elaborated and the performance is numerically evaluated by correlation coefficients.

In current Intelligent Transportation Systems, 4-dimensional (4D) millimeter-wave radar is gradually conquering more important roles. However, the rich multipath phenomena of radar waves can cause virtual images to be mistaken for actual targets. Most existing solutions aim to identify distinguishable features within a single signal frame to differentiate virtual targets from actual ones, but they fail to fully leverage temporal evolution information. Moreover, they are often limited to simple scenarios with low-order bounces and cannot handle extreme high-order bounce cases. To address this gap, in this paper we establish a research methodology based on topology and propagation graph theory, and propose a time-varying 4D trajectory-based physical target discrimination (TPTD) method. This method can handle multipath virtual images formed by arbitrary-order bounces. Its effectiveness and superiority over advanced methods have been qualitatively and quantitatively validated through traceable simulated and measured data. We also discuss in detail the scalability of the algorithm and its potential bottlenecks. The proposed method can be applied to fields such as precise radar sensing and channel modeling, laying the foundation for integrated sensing and communication and autonomous driving.

Clustering characterizes the grouping of multipath components (MPCs) in radio channels. Accurate clustering is a prerequisite for cluster-based channel characterization and sensing in the beyond fifth-generation (B5G) and sixth-generation (6G) communication. However, existing clustering algorithms commonly depend on thresholds and initializations, and are not fully consistent with the characteristics of MPC distributions in the radio channel. Additionally, clustering based on power spectrum has not been thoroughly researched. In this paper, we propose a unified clustering method named power-weighted nearest-neighbor robust mean-shift (MP-NN-RMS) algorithm, which is a kernel density estimation (KDE)-based method. The K-nearest neighbor (KNN) kernel is utilized to adapt to the changes in local density. Two variants of this clustering method for the power spectrum and MPCs are provided. Both simulation and measurement-based verifications demonstrate the effectiveness of the proposed algorithms. Compared with traditional clustering methods, the proposed algorithm can achieve more accurate and robust clustering results without requiring prior information of predefined parameters or models. Moreover, mathematical proof on the convergence guarantees the rationality of the proposed algorithm. This advancement is beneficial for the development of future wireless communication systems.

Integrated Sensing and Communications (ISAC) has been identified as a key technology in the Sixth-Generation (6G) as an enabler for environment-aware communications. Although many communications-centric channel models have been developed for dynamic scenarios, they can hardly be used for sensing applications since they rely on propagation characteristics not directly related with the geometry and trajectories of the objects in the environment. On the other hand, the available sensing models are mostly deterministic, and hence not providing a stochastic description of the sensing characteristics. In this paper, we propose a sensing channel model based on geometric and kinematic parameters, such as the size, shape, position and velocity of the objects. By distinguishing between single- and multi-bounce components, the proposed channel model is able to accurately characterize propagation environments even in dense-scattered scenarios. Moreover, a novel definition is provided for the birth and death processes (BDPs), able to accurately capture the space-time correlation properties of the channels. An example of the model parametrization is provided based on actual measured data. With the channel realization approach, we exhibit the consistence between simulated and measured data and validate the efficacy of the proposed model. The work in this paper is of great significance to construct the bidirectional bridge between channel and the environment, a key point of interest for the future 6G systems.

The Deterministic Riemann Sum Method (DRSM) and the Randomized Riemann Sum Method (RRSM) are two novel parameter computation methods for the design of sum-of-cisoids-based simulators of narrowband multiple-input multiple-output (MIMO) mobile fading channels. The performance of both methods regarding the approximation of important statistical functions of MIMO fading channels has been analyzed extensively in other papers. Nevertheless, their effectiveness in approximating relevant system performance metrics, such as the bit error probability (BEP), has not been investigated so far. This paper aims to close the gap by presenting a comparative analysis of the DRSM and RRSM with respect to (w.r.t.) the BEP of an antenna diversity system. A variety of simulation scenarios comprising isotropic and non-isotropic scattering conditions in a three-dimensional propagation environment, as well as uniform linear and uniform circular antenna arrays, are considered for the analysis. The results show a good fit between the bit error rate (BER) curves obtained by applying both methods and the theoretical BEP curves that serve as benchmarks. However, it was observed that the RRSM outperforms in general the DRSM. In addition to evaluating the BER performance of the DRSM and RRSM, guidelines are provided for those interested in applying these new channel simulation methods to the analysis of modern MIMO wireless communication systems.

In this paper, the correlation matrix distance (CMD) metric is employed to characterize the non-stationarity of the Vehicle-to-Vehicle (V2V) channel under three different driving states, namely following, overtaking and meeting. The analysis is based on measurements carried out at 5.9 GHz using a single-input multiple-output (SIMO) system. Firstly, the spatial structure of V2V channel under three different driving states is analyzed through power angle spectrum (PAS). Subsequently, based on the analysis results of CMD, we further calculate the quasi-stationary distance for the three different driving states. The analysis results indicate that the quasi-stationary distance can effectively reflect the spatial structural changes in the V2V channel. Moreover, the statistical characteristics of the quasi-stationary distance for the three driving states are also characterized. The analysis reveals that among the three driving states, the V2V channel in the following driving state exhibits the highest stationarity, followed by overtaking driving states. Compared to the meeting state, the V2V channel in overtaking exhibits greater stationarity.

In order to realize the future Sixth-Generation (6G), the research on Integrated Sensing and Communication (ISAC) channel modeling has attracted considerable attention. In classical cluster-based models, it is difficult to effectively build high density point clouds to outline object features. As an alternative, slightly-distributed-scatterer-based signal models are utilized in this paper, and a first-order generalized array manifold is used to approximate the spread features of channel. By combining this with the space-alternating generalized expectation-maximization (SAGE) algorithm, we achieve a framework for the efficient estimation of the dispersive-path components including the spread on the angle and the Doppler frequency domains. Based on measured data in vehicular environments, the effectiveness of the algorithm is verified, and a time-varying statistical channel model is formulated. The results obtained can help to accurately model vehicular channels and provide guidance for other applications of autonomous driving.

Automotive radar sensors are widely used in vehicles so as to allow the “advanced driver assistance systems (ADAS)” to work. As far as we are concerned, no mandatory regulations were established so far for interference control in the cases where multiple vehicles equipped with automotive radars exist in close proximity. Consequently, those inter-vehicle cross-interference easily result in the radar performance degradation, e.g. incapability of target detection and identification. In this paper, an interference suppression algorithm is proposed which utilizes two Recursive-Least-Square (RLS) adaptive filters to estimate, the interference signals caused by the “aggressor” radar and the echo signals from the “ego” radar. The algorithm operates in the iterative mode with its initial input being the originally received beat signals involving interference. In each iteration, the estimates of the interference signals and of the clean signals are calculated sequentially by the two RLS filters, and the beat signal is updated with parts of the fragments substituted with their interference-mitigated counterparts. Such iterative operations stop when the convergence criterion, e.g. none of the fragments in the beat signals are considered being interfered, is achieved or the iteration number reaches the limit predefined due to the practical constraints. Simulation and measurement results demonstrate that the proposed dual-RLS-based algorithm outperforms the existing methods in terms of lower interference-to-signal ratio resultant and superior capability of retrieving the original range-Doppler profile.

In this paper, the novel extended space-alternating generalized expectation-maximization (SAGE) algorithm, providing joint propagation channel multi-path component (MPC) estimation and scatterer localization underlying spherical-wavefront multipath model, is proposed. Two geometry-based models are aided for estimating the first and last hop scatterers under different bouncing orders. The performance of the proposed algorithm, as called geometry-aided SAGE (GA-SAGE), is illustrated by means of the Cramér-Rao lower bound derived for parameter estimates and the root-mean-square-estimation-errors (RMSEEs) obtained through and Monte-Carlo simulations, which shows the applicability both near- and far-field estimation. Finally, the GA-SAGE is applied to processing the experimental data obtained from measurements in an indoor office environment by using a single-input multiple-output (SIMO) configuration. The obtained results show that the method proposed outperforms the traditional SAGE algorithm in terms of MPCs estimation accuracy, convergence rate, and the extra capability of localizing scatterers involved in different bouncing order propagation paths. It is considered useful in environment sensing alike applications and makes the GA-SAGE an efficient and effective tool for the development of geometry-based stochastic channel models capable of reproducing channel realizations of the so-called spatial consistency or spatial non-stationarity, which are the basis for the design of transmission technologies using extremely-large antenna array (ELAA) for 5G and beyond.

With the rapid development of the sixth generation (6G) communication systems, the extremely large antenna arrays (ELAAs) have attracted substantial attention in both academia and industry. As the array size increases, the so-called “near-field” effect, i.e. channel parameters varying with respect to the antenna’s position becomes evident. Conventional channel models fail to describe such effects as the parameters of multi-path components (MPCs) specified in these models are fixed regardless of array configuration. Due to the mismatch between the constant MPC assumption and the actual variant behavior observed, the parameterization of the models aiming to describe the spatial non-stationary channels cannot be conducted effectively. In order to mitigate the model mismatch and reproduce the channels with spatial non-stationarity specifically for ELAA applications, in this work, a novel measurement-based stochastic channel modeling method is proposed and applied to establishing Scatterer-based Spatial Channel Model (SSCM) that can be used to unify the far- and near-field models using spherical wave and generate the spatial non-stationary channels for ELAAs by integrating the traditional spatial channel model (SCM) with “clusters-of-scatterers (CoS)”. To establish the models based on measurements, the locations of first- and last-hop scatterers existing along propagation paths in a channel are estimated based on the spherical-wave assumption. A novel clustering algorithm is used to group the MPCs taking into account those scatterers’ locations. With a channel measurement campaign conducted using a$32\!\!\times \!\!32$Rx planar antenna array at carrier frequency of 10 GHz, exemplary SSCMs are established. The SSCM is capable of reproducing the parts of the environment influencing wave propagation, and therefore can be used for the research of integrated sensing and communication (ISAC) applications.

In recent years, with the rapid development of Advanced Driver Assistance Systems (ADAS), vehicular millimeter-wave (mmWave) radar plays an important role in urban intelligent transportation system. Self-interference caused by multipath in radar channel is a widely concerned problem. However, the research on radar channel propagation characteristics is very limited and there are few models for radar channel available. In this paper, we propose a pioneering research for radar channel model. Based on measurement data, channel characteristics are analysed using the space-alternating generalized expectation-maximization (SAGE) algorithm. An empirical statistical model is established for the 77GHz mmWave radar channel in the underground parking lot scenario, which is one of the most challenging environments because of time-variation and abundant multipaths, filling the gap in the field. A path-association algorithm is used to obtain multipath component (MPC) trajectories in delay, Doppler frequency and power domains. Additionally, some trajectory (in range domain) features are analysed. Moreover, based on geometric analysis of trajectories in range domain, the deterministic component of trajectories is modeled, and then a novel method for discrimination between actual targets and ghost images is proposed and verified by simulation and measurement data.

Presentación realizada en el marco Proyecto PINV18-765: Vehículos aéreos no tripulados en aplicaciones para la agricultura de precisión para el monitoreo de cultivos agrícolas. Cuyo objetivo general fue: el Estudio, investigación, análisis y validación experimental de, por un lado, avanzadas estrategias de control de vuelo de vehículos no tripulados (UAV) y, por otro, de algoritmos de procesamiento de imágenes obtenidas por el UAV, orientada al análisis de cultivos agrícolas enfocado al desarrollo del sector agropecuario.

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.

The use of millimeter-wave bands is undoubtedly one of the key technologies considered by the Fifth Generation Communication Systems (5G). However, the performance of millimeter-wave communication systems can be severely affected by the propagation environment. In this paper, the propagation channel characteristics in scatterer-rich environments for the 73GHz millimeter-wave band is studied based on measurements. The channel characteristics under study include the propagation loss and its variation depending on the elements around the receiver, the channel dispersion in delay represented by the Root-Mean-Square (RMS) channel delay spread and the multipath behavior in the form of multi-path component (MPC) clusters. The proposed models for the mentioned channel characteristics are of great value as reference for millimeter-wave 5G deployments.

Massive multiple-input multiple-output (MIMO) transmission is one of the key technologies utilized in the fifth generation (5G) wireless communication system. For the algorithm design and the performance evaluation of massive-MIMO, channel characterization based on real measurements is necessary. In this paper, we introduced a measurement campaign conducted in a Line-of-Sight (LoS) scenario of a suburban outdoor environment. The channel sounder is equipped with a 64-element transmitter array and a 64-element receiver array. Multiple aspects of massive-MIMO channel characteristics are considered, including the power delay profile, the K-factor and the delay-spread. We found that for the outdoor LoS scenario considered, the polarization plays an important role generating orthogonal channels observed through large-scale antenna arrays.

Multimedia and data-based services experienced a nonstopping growth over the last few years. People are continuously on the move using devices to access multimedia contents or other data-based services. Due to this, railway companies are showing a great interest in deploying broadband mobile wireless networks in high-speed-trains with the aim of supporting both passenger services provisioning as well as automatic train control and signaling. Nowadays, the most widely used technology for communications between trains and the railway infrastructure is GSM for Railways (GSM-R); however, it has limited capabilities to support such advanced services. Due to its success in the mass market, Long Term Evolution (LTE) seems to be the best candidate to substitute GSM-R. In this paper, we experimentally characterize the downlink between an LTE Evolved NodeB (eNodeB) and a high-speed train in a commercial high-speed line. We consider two links: the one between the eNodeB and the antennas placed outdoors on the train roof, and the direct link between the eNodeB and a receiver inside the train. Such a characterization consists in assessing the path loss, the Signal to Noise Ratio, the K -Factor, the Power Delay Profile, the delay spread, and the Doppler Power Spectral Density.

The radio access technology for railway communications is expected to migrate from GSM for Railways (GSM-R) to a more suitable generation of communication systems for the services offered nowadays, like the fourth generation (4G) or the fifth generation (5G) ones. Moreover, taking into account the evolution of general-purpose communication standards and devices, 5G systems seem to be good candidates to substitute GSM-R as the basis technology for railway communications in the long term. Recently, considerable attention has been devoted to high-speed trains since this particular environment poses challenging problems in terms of performance simulation and measurement. In order to considerably decrease the cost and complexity of high-speed measurement campaigns, we have proposed a technique to induce effects caused by highly-time varying channels on multicarrier signals while conducting measurements at low speeds. This technique has been proved to be accurate for the cases of WiMAX and Long Term Evolution (LTE) signals, as well as the waveforms proposed for 5G systems, such as Filter Bank Multicarrier (FBMC). In this work, we employ the technique to evaluate experimentally the performance of modulation schemes proposed for 5G systems at high speeds. More specifically, we compare the performance obtained by transmitting FBMC schemes with two different prototype filters at different velocities up to 500 km/h. We also include the results obtained for Orthogonal Frequency-Division Multiplexing (OFDM) signals for comparison purposes.

Long Term Evolution (LTE) is expected to substitute the Global System for Mobile Communications (GSM) as the radio access technology for railway communications. Recently, especial attention has been devoted to high-speed trains since this particular environment poses challenging problems in terms of performance simulation and measurement. In order to severely decrease the cost and complexity of high-speed measurement campaigns, we have proposed a technique to induce effects caused by highly-time varying channels on Orthogonal Frequency-Division Multiplexing (OFDM) signals while conducting measurements at low speeds. In this work, we employ this technique to evaluate LTE transmissions in a Time-Division Duplex (TDD) fashion, which is more convenient for the railway environment due to a number of reasons (e.g., better flexibility in frequency choice and the importance of the uplink transmissions for railway control communications). We assess the performance obtained in high- speed scenarios up to 500 km/h.

LTE is designed to support user velocities of up to 500 km/h where experiments are expensive, time-consuming and dangerous. Fortunately, such experiments can be emulated at lower velocities by time-stretching the transmit signals. This method preserves the spatial properties of the mobile radio channel but performs a spectral compression. In this paper, we propose a new set of methods that preserve the spectral properties by inserting additional subcarriers. Next, we compare the method of time-stretching to the new set of methods by applying them to LTE downlink signals. We evaluate the properties and the main drawbacks of the proposed methods by simulations using standard-compliant LTE signals. Finally, we highlight practical aspects to be considered when implementing the proposed high speed emulation techniques.

Long Term Evolution (LTE) is expected to substitute the Global System for Mobile Communications (GSM) as the radio access technology for railway communications. Recently, especial attention has been devoted to high-speed trains since this particular environment poses challenging problems in terms of performance simulation and measurement. In order to severely decrease the cost and complexity of high-speed measurement campaigns, we have proposed a technique to induce effects caused by highly-time varying channels on Orthogonal Frequency-Division Multiplexing (OFDM) signals while conducting measurements at low speeds. In this work, we evaluate this technique by comparing the results of LTE measurements at different velocities as well as by simulations. Additionally, we use this technique to show the performance of LTE for high-speed train scenarios up to 600 km/h. To accomplish this, we use both a controlled high-speed measurement setup as well as a channel model developed according to the guidelines of the ITU Radiocommunication Sector (ITU-R) for the evaluation of radio interface technologies for IMT- Advanced systems.

LTE is expected to substitute GSM as the basis technology for railway communications. Recently, special attention has been deserved to HST as this particular environment (mainly due to the high speed condition) can severely impact wireless systems performance. Although several channel models have been derived during the few last years, most of them are not accurate enough as they are not supported by measurement campaigns. In this paper, the main requirements for HST environments are analyzed and a flexible, cost-affordable, and easily-scalable software and hardware architecture for a test bed suitable for assessing LTE at high speeds is proposed.