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System design and deployment of millimeter-wave(mmWave) wireless communication systems for outdoor cover- age need sophisticated channel models to describe the wave interaction with illuminated physical objects. However, the con- ventional physical-statistical model cannot accurately predict the site-specific mmWave channel characteristics when involving complex system configurations and geometric information of transceiver relative locations. A framework of machine learning assisted channel modeling approach is proposed, in which the statistical models are leveraged for inter-cluster level channel characterization and the propagation properties within each kind of clusters are predicted using a hybrid physics-based and data-driven approach. In particular, with a focus on the mmWave through-vegetation scattering effect, a set of dedicated directional channel measurements and ray-tracing simulations is performed in an identical vegetated street canyon environment at 28 GHz for the performance evaluation of the proposed approach. Moreover, the training results and model validation in different environments show that comparing with the physical-statistical model, the proposed hybrid model, which adds the environment features to the artificial neural network as inputs, has higher prediction accuracy and greater generalization ability in terms of the site-specific through-vegetation cluster parameters, such as vegetation attenuation, delay spread, and angular spread.

This two-part paper investigates the application of artificial intelligence (AI) and, in particular, machine learning (ML) to the study of wireless propagation channels. In Part I of this article, we introduced AI and ML and provided a comprehensive survey on ML-enabled channel characterization and antenna-channel optimization, and in this part (Part II), we review the state-of-the-art literature on scenario identification and channel modeling here. In particular, the key ideas of ML for scenario identification and channel modeling/prediction are presented, and the widely used ML methods for propagation scenario identification and channel modeling and prediction are analyzed and compared. Based on the state of the art, the future challenges of AI-/ML-based channel data processing techniques are given as well.

This paper aims to estimate the exposure in 5G massiveMIMO networks using a stochastic geometric approach.The paper investigates the effect of beamforming, and theeffect of multi-user massive MIMO on the exposure of a5G network. The massive MIMO antenna patterndistribution is obtained by fitting the radiation patternobtained by running a large amount of channel simulationson NYUSIM. The distribution is then implemented into ananalytical framework based on stochastic geometry, so wecan obtain a close form expression of the downlinkexposure., info:eu-repo/semantics/inPress

Inhomogeneous plane waves (IPWs) based on a linear contour deformation provide an accurate and concise field representation for scattering problems in which source and observation domains are visible within a limited angular sector. Given an error level, we derive closed-form expressions for the truncation limit, the sampling step, and the contour slope that minimize the number of plane waves. A relation is drawn between the required number of plane waves and the degrees of freedom of the field, defined as the minimum number of orthonormal basis functions that represents scattered fields. The sampling rules are then applied to an efficient evaluation of the physical optics radiation integral.

This paper proposes a hybrid modeling approach for the prediction of the room-to-room radio propagation channel. The model combines ray tracing (RT) with the propagation graph (PG). The PG vertices are obtained at each room by RT with the assumption that the receive antenna (or transmit antenna) virtually locates on the surface of the separating wall between the two rooms. Rays transmitted from one room to the other through the separating wall are deterministically calculated by Snell's law of refraction. Predictions by the proposed model are compared with the measurement data from an office-to-office scenario. The results show that the proposed modeling works with the simplest parameter settings, i.e., assuming no propagation from the room containing receive antenna to the room containing transmit antenna, RT applied separately in each room only involves mechanism of line of sight and first-order specular reflection.

Radio channel modeling has been an important research topic, since the performance of any communication system depends on channel characteristics. So far, most existing clustering algorithms are conducted based on the multipath components (MPCs) extracted by using a high-resolution parameter estimation approach, e.g., SAGE or MUSIC, etc. However, most of the estimation approaches require prior information to extract MPCs. Moreover, the high-resolution estimation approaches usually result in relatively high complexity, and thus, the clusters can only be identified by using an offline approach after the measurements. Therefore, a power-angle-spectrum (PAS) based clustering and tracking algorithm (PASCT) is proposed in this paper. First, a PAS is extracted from measurement data by using a Bartlett beamformer. For each PAS, the potential targets are selected from the background and separated into clusters by using image processing approaches. The recognized clusters are characterized by the following three attributes: size, position, and shape feature, where an orientation histogram is developed to describe the shape feature of the clusters. Moreover, a cost minimizing tracking approach based on Kuhn-Munkres method is proposed to accurately identify the clusters in non-stationary channels. The proposed PASCT algorithm is validated based on both simulations and measurements. It is found that the dominating clusters in both line-of-sight and non-line-of-sight environments can be well recognized and tracked with the proposed algorithm. By using the proposed algorithm, the dynamic changes of the clusters in real-time channel measurements, e.g., number, birth-death process, and size of the clusters, can be well observed. Through the experiments, the proposed algorithm can achieve fairly good accuracy on the cluster identification with lower complexity compared to the conventional solution.

Downlink exposure to electromagnetic fields due to cellular base stations in urban environments is studied using the stochastic geometry framework. A two-dimensional Poisson Point Process is assumed for the base station distribution. Mathematical expressions of statistics of exposure are derived from a simple propagation model taking into account the height of the base stations. The error on exposure made by taking a limited number of base stations, instead of the whole set, is quantified. The relative impact of the model parameters on the statistics of exposure is highlighted. The method is then applied and the model parameters are calibrated using experimental data obtained by drive-tests in two Brussels municipalities, in Belgium, for the 2100 MHz and 2600 MHz frequency bands. It is shown that the proposed model fits experimental values, paving the way to a new methodology to assess general public exposure to electromagnetic fields, for any frequency band. An insight is given on how to apply the methodology to a real case without access to experimental data., info:eu-repo/semantics/published

Exposure to electromagnetic fields due to cellularnetworks in an urban environment is studied using stochasticgeometry. We describe a simple but functional and realisticsemi-empirical model based on the modeling of base stationpatterns as Poisson Point Processes and applied to two Brusselsmunicipalities. Using this model, a study of the impact of basestation densification is performed according to two theoreticalscenarios. From simple assumptions, it is concluded that globalexposure is expected to remain approximately constant in thecase of intelligent network evolution involving the densificationof macro cells and the addition of a network of low-power smallcells., info:eu-repo/semantics/published

In the nearest future, connected vehicles will be widely spread due to ongoing standardization activities of vehicle-to-everything communications. However, exchanging real-time traffic information is challenging in urban environments with dense traffic where the line-of-sight might be blocked. In this work, we present the measurement results of the automotive communication and radar sensing through the obstructing vehicle, when the transmitting and receiving antennas are located very close to the ground, and the radio waves propagate near-ground, i.e. between the bottom of the car and the road pavement. It is shown that communication between two vehicles is undoubtedly feasible at distances starting from 1.5 m and antenna heights of 0.3 m with 16 dB loss. Almost the same losses are observed in a radar sensing for distances larger than 2 m. acceptedVersion

The future communication systems will use higher frequencies, Ka and Q band. For the implementation of fade mitigation techniques, it is important to have representative time series representing the degradations of the received signal for GEO and LEO satellites. This paper presents a model combining numerical weather prediction software WRF and MultiEXCELL rain cells model in order to have more accurate representation of the dynamics of the attenuation for GEO and non-GEO satellite links.

A physical-statistical ray-based model is applied to model the UAV-to-ground radio channel in urban areas. The modeling method relies on simple input data, such as input distribution parameters (e.g. mean building height, building height variance). The physical part of the model being based on ray-tracing, it is also applicable over very wide parameter ranges at a very low computation time thanks to a pre-calculation procedure.

The classical Physical Optics (PO) formulation of the scattered fields suffers from loss of accuracy when the observation angle widely deviates from the specular direction. This is even worse in the “forward region”, i.e. for bistatic angles between 90 and 270 degrees. The method presented in this paper aims at improving the accuracy of the fields in this region by finding currents induced on the non-illuminated part of the object, where classical PO assumes zero currents. The proposed approach reformulates the initial problem using equivalent currents over a domain surrounding the object. The equivalent currents then act as new sources which induce electrical currents computed via the classical PO formulation. The computation of the equivalent problem is accelerated using the multipole expansion of the Green’s function, including appropriate singularity extraction in the very near-field. This approach provides an error that is significantly lower in the forward direction, as compared to the classical PO formulation. The principles of this new approach are presented and validated for two-dimensional scenarios.

In this paper we present a millimeter wave radio channel sounding campaign, enabled by Reconfigurable Intelligent Surfaces (RISs). The measurement setup is based on an electronically reconfigurable Transmitting-RIS (T-RIS) composed by 400 unit-cells. Optimal phase distributions were implemented in order to operate a beam scan over 120 degrees. Two indoor scenarios were investigated in the Ka Band, exploiting the beam scanning capabilities of the antenna. The results in terms of path loss and delay spread are presented and discussed.

With the deep integration between the unmanned aerial vehicle (UAV) and wireless communication, UAV-based air-to-ground (AG) propagation channels need more detailed descriptions and accurate models. In this paper, we aim to perform cluster-based characterization and modeling for AG channels. To our best knowledge, this is the first study that concentrates on the clustering and tracking of multipath components (MPCs) for time-varying AG channels. Based on measurement data at 6.5 GHz with 500 MHz of bandwidth, we first estimate potential MPCs utilizing the space-alternating generalized expectation-maximization (SAGE) algorithm. Then, we cluster the extracted MPCs considering their static and dynamic characteristics by employing K-Power-Means (KPM) algorithm under multipath component distance (MCD) measure. For characterizing time-variant clusters, we exploit a clustering-based tracking (CBT) method, which efficiently quantifies the survival lengths of clusters. Ultimately, we establish a cluster-based channel model, and validations illustrate the accuracy of the proposed model. This work not only promotes a better understanding of AG propagation channels but also provides a general cluster-based AG channel model with certain extensibility.

This paper analyzes the time correlation properties of vehicle-to-pedestrian (V2P) channels based on measurement campaign carried out at 3.8 GHz. Several propagation conditions and different mobility patterns were investigated. Subsequently, an analytical auto-correlation function (ACF) is proposed and successfully compared to the measurement data.

This paper exploits the Doppler and delay spreads for pedestrian-to-pedestrian (P2P) communications based on a measurement campaign carried out at 3.8 GHz. Several propagation conditions and different mobility patterns were investigated. In particular, the impact of vehicle passing in the vicinity of a P2P links was considered. It was found that the delay spread does not only depends on the propagation environment but also on the body mutual orientation whereas the presence of a moving vehicle in the vicinity of P2P links can have an impact on the Doppler spread.