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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.

The objective of this Special Issue is to showcase a unified vision for the applications of AI in radio propagation for communications and other relevant aspects. More specifically, the initial announcement encouraged emphasis in, but not limited to, the following areas: novel AI or AI-enabled techniques for radio propagation characterization and analysis; AI-enabled data analysis and parameter extractions of wireless channels; clustering analysis for radio channel characterization and modeling; AI-enabled channel modeling and communication system simulation; AI algorithm design in radio propagation for the applications in communications.

In order to provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G network are development with various artificial intelligence techniques. In this twopart paper, we investigates the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. It firstly provides a comprehensive overview of ML for channel characterization and ML-based antenna-channel optimization, and then gives state-of-the-art literature review of channel scenario identification and channel modeling in Part II. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML methods for channel data processing, propagation channel estimation, and characterization are analyzed and compared. A discussion of challenges and future research directions for ML enabled next generation networks rounds off the paper.

In this paper, we investigate the impact of the use of Transmitting Reconfigurable Intelligent Surfaces (T-RISs) on the indoor millimeter wave (mmWave) radio channel characteristics. To this purpose, two measurement campaigns were conducted by employing a T-RIS array and a virtual cubic array (VCA). The multipath component extraction was performed to embed the TRIS based antenna pattern and evaluate the effect on large-scale parameters such as the path loss, delay and angular spreads.

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.

In recent years and in the scope of Fifth Generation (5G) and Sixth Generation (6G) technologies, the joint deployment of Massive MIMO (mMIMO) and millimeter waves (mmWave) technologies has shown very promising results in the quest to meet the very high consumer data rate demands. To design real mmWave mMIMO wireless systems and predict their performances under certain conditions, it is imperative to have accurate mmWave mMIMO wireless channel models that capture the key characteristics for different scenarios. Very high gain and directive Transmitting Reconfigurable Intelligent Surface (T-RIS)s with electronic beamscanning capabilities are also expected to be ubiquitous in 5G and 6G wireless communication systems and hence the need to investigate the channel and T-RIS joint effect. This thesis presents different channel characterizations and models for indoor mmWave mMIMO radio propagation channels based on stochastic approaches. Different channel measurement campaigns were carried out in Indoor Laboratory (InLab), Indoor Open Office (InOpOffice), Indoor Mixed Office (InMixOffice), corridors and Indoor Factory (InF) environments. Massive virtual arrays and T-RISs were employed in the channel measurements. Line of Sight (LOS), Obstructed Line of Sight (OLOS) and Non Line of Sight (NLOS) propagation conditions were investigated in these environments. For the large virtual arrays’ measurements, an investigation on the impact of the array size and geometry on the mmWave mMIMO channel characteristics is performed. Spatial channel correlation over the massive array is investigated and Multipath Components (MPCs) are estimated by means of a high resolution detection algorithm. The estimated specular MPCs parameters are then used for further analysis. Dense Multipath Components (DMCs) are quantified. Common MPCs between different sub-arrays of a massive array are identified following a joint temporal, power and angular threshold criterion and their distributions are then a, (FSA - Sciences de l'ingénieur) -- UCL, 2022

In the recent years, the saturation of the smartphone market penetration fostered the development of new applications to connect human beings with smart objects. However, the explosive growth in the number of connected devices brings some concerns about the highly growing traffic and the environmental effect of batteries production and disposal. Simultaneous wireless information and power transfer (SWIPT) technique has been emerged as a solution to mitigate with these concerns. However, SWIPT also comes with some obstacles such as the limitations on the energy transfer efficiency due to the propagation losses and the constraints on the minimum received power imposed by the current radio frequency (RF) harvesting technologies. This restricts the range of application of wirelessly powered devices by limiting them to the execution of very simple computational tasks. Following this line of thought, the integration of SWIPT with mobile edge computing (MEC) has appeared as an enticing emerging research direction to increase the computational capabilities of SWIPT enabled systems. This dissertation focuses on studying the performance of SWIPT enabled MEC system in the presence of spatially correlated shadowing. The first step taken towards this objective is the analysis of the performance of SWIPT enabled systems with special emphasis on the impact of spatially correlated shadowing on the achievable rate-energy trade-off. Stochastic geometry is used to model the random location of the network nodes and the geometry of the propagation environment. Capitalizing on the analytical tool developed in the first part of the dissertation, the analysis of the achievable performance trade-offs has been extended to SWIPT-enabled computing systems. The last part of the dissertation addresses the study of SWIPT-MEC systems and provides an analytical tool to compute the probability of successfully execute a task as a function of the network parameters. The last chapter is devoted to a co, (FSA - Sciences de l'ingénieur) -- UCL, 2022

Cognitive radio (CR) is the solution to the spectrum scarcity issue faced in wireless communications. It allows unlicensed secondary users (SUs) to opportunistically access the spectrum assigned to licensed primary users (PUs), but unused, on a non-interfering basis. Spectrum sensing is the process by which the CR node becomes aware of the spectrum occupancy, in order to decide which frequency bands to use. In the first part of this thesis, we consider the Bayesian changepoint detection theory to propose a new spectrum sensing algorithm that exploits the mobility of the SU and works for practical scenarios where the PU’s signal power is unknown to the SU. Simulation results show that the derived algorithm is a good choice when the SU could be required to detect very low signal-to-noise ratio (SNR) signals. In the second part, we introduce a new framework for joint transmission and sensing in mobile CR, by using changepoint detection for spectrum sensing. The optimal system parameters (sensing time, transmission time and detection threshold) that maximize the spectrum utilization are numerically computed and analyzed. In the last part, a new spectrum sensing algorithm, that exploits geolocation information about existing PUs by using the Bayesian block-based detection theory, is introduced. An approximate closed-form expression, based on Taylor series expansion, is provided. Numerical results show that the derived algorithm gives the minimum error probability when compared with an algorithm that does not use geolocation information., (FSA - Sciences de l'ingénieur) -- UCL, 2022

In this paper, the impact of interference between multiple phase-coded Frequency Modulated Continuous Wave (FMCW) Dual Function Radar Communication (DFRC) systems is analysed. For the RADAR receiver, interference occurs when another nearby DFRC system is active. For the communication receiver, RADAR echoes from the same DFRC system interfere with the receiver. The performance achieved by interference cancellation at both functions is studied in different scenarios. Finally, stochastic geometry is used to analyse in a two-way automotive scenario which signal component (RADAR or communication) is dominant, in order to evaluate whether the interference should be cancelled at the RADAR receiver based on the communication receiver output or the opposite.

The proposed work tries to quantify how relevant it is to use a deterministic channel model likeray tracing at a network deployment level in a Manhattan-like environment. More precisely, the goal isto compare it with simplier and more computationally efficient models to see if similar results can beobtained at a lower cost. In the framework of the analysis, ray tracing is thus compared to a stochasticgeometry-based model in terms of coverage probability and exposure at a centric user for which we wereable to derive semi-closed form expressions. In addition, a good lower bound for the joint coverage exposure distibution was found. In order to easily mimic the propagation mechanisms implementedin the UCLouvain RT software and enable fair comparison, the Berg recursive model was consideredas channel model. Finally, the analytical expressions were further enhanced to take into account theantenna height as well as the line-of-sight probability in the typical street and to distinguish a typicaluser located at a crossroad from a typical user located inside a street., info:eu-repo/semantics/published

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

info:eu-repo/semantics/inPress, 1

Future networks are envisioned to host enormous amount of wirelessly connected devices that could possibly harvest energy too. This dual demand led researchers to propose a new concept: simultaneous wireless information and power transfer (SWIPT), which allows WPT and WIT to coexist in the same system using the same resources. In the first part of the thesis, we propose a novel waveform design where the power signal is composed of a high peak modulated rectangular pulse sent during the cyclic prefix (CP) of the cyclic prefix orthogonal frequency-division multiplexing (CP-OFDM) system. We investigate through theoretical analysis, simulations and real measurements the interference caused by the channel dispersion as well as the energy harvesting performance. In the second part, we investigate the possibility of maximizing the harvested energy when the time-domain phases are optimized adapting to the channel. We formulated the optimization problem using the nonlinearity model of the rectifier. In the third part, we use stochastic geometry (SG) to analyze the interference coming from multiple transceivers coexisting in the same propagation environment considering an indoor environment. We study the impact of potential desynchronization errors, the rate-energy trade-off as function of the nodes density, and the influence of frequency selective channels. In the last part, we propose a new setup using a Gabor expansion to create a time-frequency lattice, enabling to assign dynamically the resources. We investigate a frequency domain interference analysis theoretically and using simulations., (SC - Sciences) -- UCL, 2021

The constant evolution of wireless technologies has favoured the development of antenna systems. The increasingly restrictive market requirements are pushing towards the development of larger and more complex antenna arrays. This trend calls for fast design procedures which require the efficient and accurate characterization of antennas. Although analytical thinking allows the determination of initial rules of thumb, recent and future designs are almost impossible to analytically characterize and a crucial role is played by computational electromagnetics (CEM). CEM combines innovative numerical methods and computational resources to enable the fast validation of new designs and their optimization under the constraints imposed by the designated applications. Although CEM has come a long way since its creation, many advanced numerical methods have been developed under simplifying hypotheses such as the regularity of antenna arrays, the infinite ground plane assumption, canonical shapes of (parts of) the antennas, or ideal representations of the feeding structures. While these hypotheses lead to efficient numerical methods, they represent a setback when confronted to the constraints encountered in industry. This is why this thesis aims to contribute to the advance in CEM by developing numerical methods capable of effectively simulating electrically large arbitrarily-shaped structures while accounting for practical constraints that have been often neglected before. Doing so, numerical techniques are developed for two classes of problems involving layered media. The first class concerns the simulation of large irregular antenna arrays on a finite ground plane and the second class concerns the simulation of arbitrarily-shaped metasurface antennas. The first part of this thesis is directed towards the analysis of large irregular antenna arrays on a finite ground plane devoted but not limited to radio astronomy applications. The resolution of new radio telescopes targeting, (FSA - Sciences de l'ingénieur) -- UCL, 2021

Inclusive Radio Communication Networks for 5G and Beyond is based on the COST IRACON project that consists of 500 researchers from academia and industry, with 120 institutions from Europe, US and the Far East involved. The book presents state-of-the-art design and analysis methods for 5G (and beyond) radio communication networks, along with key challenges and issues related to the development of 5G networks.

We consider a relay network based on Unmanned Aerial Vehicles (UAV). Terrestrial Base Stations (TBS) and UAV Relay Nodes (RN) are modelled using two Homogeneous Poisson Point Processes (HPPP). UAVs can hover at a fixed position or move following specific displacement schemes. The Coverage Probability (CP) of a typical user equipment (UE) is derived, either when it communicates via a direct link (from the TBS to the UE) or via a relay link (from the TBS to the UE through a UAV RN). Every link can be in Line-of-Sight (LoS) or Non Line-of-Sight (NLoS), and suffers from Rician fading with distance-dependent parameters. This coverage is calculated by means of both stochastic geometry (SG) and Monte-Carlo (MC) simulations. The benefits of the use of UAV as RNs are analysed depending on their altitude, density, and mobility scheme.

Ray tracing is a powerful tool to obtain deterministic and dynamic descriptions of communication channels. However, performing ray tracing simulations at each discrete time instant is computationally expensive. Instead, a new approach is proposed to extrapolate results obtained from a single ray tracing simulation. It relies on the geometric tracking of interaction points (i.e. reflection or diffraction points), enabling analytical or numerical predictions of the evolution of any ray identified during an initial ray tracing simulation. The performance of this new approach is studied on several canonical vehicle-to-vehicle configurations, as part of a statistical study. Focus is also given to the time horizon during which dynamic ray tracing is possible, related to the lifetime of the main rays. This time horizon can be directly estimated based on the knowledge of the geometry and its evolution. It is found to be the main parameter influencing the accuracy and the computational gain of the presented approach.

Vehicle-to-vehicle (V2V) wireless communications have many envisioned applications for ensuring traffic safety and for addressing traffic congestion. However, developing suitable communication systems and standards for this purpose requires developers to have accurate models for the V2V propagation channel. Likewise, the dynamic evolution of multipath components (MPCs) in V2V channels has not been well modeled in existing models. In this paper, we propose a geometry-based stochastic channel model for a lightly built-up urban environment and then parametrize the model from measurements. The MPCs are extracted based on a high-resolution parameter estimation; they are tracked and clustered through a joint algorithm. The identified clusters are classified as line-of-sight, reflections from static scatterers, reflections from mobile scatterers, multiple-bounce reflections, and diffuse scattering. Specifically, the multiple-bounce reflections are modeled as twin clusters that follow the COST 273/COST2100 approach. The paper gives a full parameterization of the channel model and supplies a step-by-step implementation recipe. We verify the model by comparing two second-order statistics, i.e., the root-mean-square (RMS) delay spread and the angular spreads of arrival/departure derived from the channel model, to the results obtained directly from the measurements. Furthermore, we also identify several key factors that strongly impact the synthetic channel performance

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 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.

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.

Exposure to electromagnetic fields due to cellular networks in an urban environment is studied using stochastic geometry. We describe a simple but functional and realistic semi-empirical model based on the modeling of base station patterns as Poisson Point Processes and applied to two Brussels municipalities. Using this model, a study of the impact of base station densification is performed according to two theoretical scenarios. From simple assumptions, it is concluded that global exposure is expected to remain approximately constant in the case of intelligent network evolution involving the densification of macro cells and the addition of a network of low-power small cells.

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.

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.

In the present study, a measurement setup utilizing mm-wave transceivers with steerable directive antennas, mounted on both a customized UAV and a ground station has been used to study Air-to-Ground (A2G) radio links and, more generally, full-3D mm-wave propagation in urban environment. We evaluate the double-directional characteristics of the channel by rotating the antennas, deriving Power-Angle Profiles at both link ends. Preliminary results provide useful understanding of A2G propagation, e.g. the influence of the antenna tilt angles, or the mechanisms allowing for the signal to propagate from street canyons to the air.

In this work, the results of Ultra-Wideband air-to-ground measurements carried out in a real-world factory environment are presented and discussed. With intelligent industrial deployments in mind, we envision a scenario where the Unmanned Aerial Vehicle can be used as a supplementary tool for factory operation, optimization and control. Measurements address narrow band and wide band characterization of the wireless radio channel, and can be used for link budget calculation, interference studies and time dispersion assessment in real factories, without the usual limitation for both radio terminals to be close to ground. The measurements are performed at different locations and different heights over the 3.1-5.3 GHz band. Some fundamental propagation parameters values are determined vs. distance, height and propagation conditions. The measurements are complemented with, and compared to, conventional ground-to-ground measurements with the same setup. The conducted measurement campaign gives an insight for realizing wireless applications in smart connected factories, including UAV-assisted applications.