The continuous advancement in wireless communications technology demands new approaches to improving the capacity of existing radio links. The high data throughput required can be achieved by the complete utilization of space, time and polarization diversities inherent in any propagation environment. Among the different propagation scenarios, the indoor channels represent a particularly challenging problem given the number and complexity of interactions between the transmitted signal and the environment. This dissertation explores the interrelation between propagation physics and space-time-polarization diversity based on a novel high resolution channel sounding and reconstruction technique. First, a method to reconstruct the indoor complex channel response based on a limited set of samples and the elimination of the interference using deconvolution techniques is presented. Then, the results for the joint angle-of-arrival, delay characterization and depolarization of electromagnetic waves are presented. Finally, a novel approach to using depolarized multipath signals to boost the receiver signal-to-noise performance is presented. The current study shows that full utilization of the diversities of channel novel wireless systems can be proposed with significant improvement in capacity.

The continuous advancement in wireless communications technology demands new approaches to improving the capacity of existing radio links. The high data throughput required can be achieved by the complete utilization of space, time and polarization diversities inherent in any propagation environment. Among the different propagation scenarios, the indoor channels represent a particularly challenging problem given the number and complexity of interactions between the transmitted signal and the environment. This dissertation explores the interrelation between propagation physics and space-time-polarization diversity based on a novel high resolution channel sounding and reconstruction technique. First, a method to reconstruct the indoor complex channel response based on a limited set of samples and the elimination of the interference using deconvolution techniques is presented. Then, the results for the joint angle-of-arrival, delay characterization and depolarization of electromagnetic waves are presented. Finally, a novel approach to using depolarized multipath signals to boost the receiver signal-to-noise performance is presented. The current study shows that full utilization of the diversities of channel novel wireless systems can be proposed with significant improvement in capacity.

Accurate indoor positioning of narrowband Internet-of-Things (IoT) sensors has drawn more attention in recent years. The introduction of Bluetooth Low Energy (BLE) technology is one of the latest developments of IoT and especially applicable for Ultra-Low Power (ULP) applications. BLE is an attractive technology for indoor positioning systems because of its low-cost deployment and reasonable accuracy. Efficient indoor positioning can be achieved by deducing the sensor position from the estimated signal Angle-of-Arrival (AoA) at multiple anchors. An anchor is a base station of known position and equipped with a narrowband multi-antenna array. However, the design and implementation of indoor positioning systems based on AoA measurements involve multiple challenges. The first part of this thesis mainly addresses the impact of hardware impairments on the accuracy of AoA measurements. In practice, the subspace-based algorithms such as Multiple Signal Classification (MUSIC) suffer from sensitivity to array calibration errors coming from hardware imperfections. A detailed experimental implementation is performed using a Software Defined Radio (SDR) platform to precisely evaluate the accuracy of AoA measurements. For this purpose, a new Over-the-Air (OTA) calibration method is proposed and the array calibration error is investigated. The experimental results are compared with the theoretical analysis. These results show that array calibration errors can cause some degrees of uncertainty in AoA estimation. Moreover, we propose iterative positioning algorithms based on AoA measurements for low capacity IoT sensors with high accuracy and fair computational complexity. Efficient positioning accuracy is obtained by iterating between the angle and position estimation steps. We first develop a Data-Aided Maximum a Posteriori (DA- MAP) estimator based on the preamble of the transmitted signal. DA-MAP estimator relies on the knowledge of the transmitted signal which makes it impractical for narrowband communications where the preamble is short. For this reason, a Non-Data- Aided Maximum a Posteriori (NDA-MAP) estimator is developed to improve the AoA accuracy. The iterative positioning algorithms are therefore classified as Data-Aided Iterative (DA-It) and Non-Data-Aided Iterative (NDA-It) depending on the knowledge of the transmitted signal that is used for estimation. Both numerical and experimental analyses are carried out to evaluate the performance of the proposed algorithms. The results show that DA-MAP and NDA-MAP estimators are more accurate than MUSIC. The results also show that DA-It comes very close to the performance of the optimal approach that directly estimates the position based on the observation of the received signal, known as Direct Position Estimation (DPE). Furthermore, the NDA-It algorithm significantly outperforms the DA-It because it can use a much higher number of samples; however, it needs more iterations to converge. In addition, we evaluate the computational savings achieved by the iterative schemes compared to DPE through a detailed complexity analysis. Finally, we investigate the performance degradation of the proposed iterative algorithms due to the impact of multipath and NLOS propagation in indoor environments. Therefore, we develop an enhanced iterative positioning algorithm with an anchor selection method in order to identify and exclude NLOS anchors. The numerical results show that applying the anchor selection strategy significantly improves the positioning accuracy in indoor environments. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

Dans un environnement intérieur, le problème de l'extraction du composant l'Angle de Arrivée de la Line-of-Sight entre un émetteur et un récepteur Wi-Fi utilisant un lien SIMO est la principale préoccupation de cette thèse. Un des principaux défis à relever est dû au riche canal multipath que les environnements intérieurs apprécient. C'est ainsi parce que multipath résulte du fait que le canal de propagation se compose de plusieurs obstacles et réflecteurs. Ainsi, le signal reçu arrive comme un ensemble imprévisible de réflexions et / ou d'ondes directes avec son degré d'atténuation et de retard. D'autres défis sont la limitation des ressources, telles que le nombre d'antennes, la bande passante disponible et le rapport Signal / Bruit; sans parler des «imperfections» Wi-Fi, telles que les disparités de gain / phase entre les antennes et les problèmes de synchronisation entre l'émetteur et le récepteur. Dans cette thèse, notre objectif principal est de mettre en place un système en temps réel qui pourrait mesurer l'angle entre un émetteur et un récepteur en présence de tous les défis. En particulier, nous avons pris en compte tous les facteurs qui perturbent le problème d'estimation de l'angle articulaire et du délai et formulé un modèle de système en conséquence. Ces facteurs sont les suivants: Sampling Frequency offset (SFO), Carrier Frequency Offset (CFO), et Phase/Delay offsets à chaque antenne. Pour compenser l'efficacité de ces facteurs critiques, nous proposons une méthode d'étalonnage optimale pour compenser tous leurs effets. Cette thèse comprendra également d'autres méthodes théoriques qui doivent faire face au problème d'estimation de l'angle d'arrivée, à partir du point de vue de la compression et du traitement du signal. / In an indoor environment, the problem of extracting the Angle-of-Arrival of the Line-of-Sight component between a transmitter and Wi-Fi receiver using a SIMO link is the main concern of this thesis. One main challenge in doing so is due to the rich multipath channel that indoor environments enjoy. This is so because multipath results from the fact that the propagation channel consists of several obstacles and reflectors. Thus, the received signal arrives as an unpredictable set of reflections and/or direct waves each with its own degree of attenuation and delay. Other challenges are limitation of resources, such as number of antennas, available bandwidth, and Signal-to-Noise-Ratio; not to mention the Wi-Fi ”imperfections”, such as gain/phase mismatches between antennas and synchronisation issues between transmitter and receiver. In this thesis, our main focus is implementing a real-time system that could measure the angle between a transmitter and receiver in the presence of all challenges. In particular, we have taken into account all factors that perturb the Joint Angle and Delay estimation problem and formulated a system model accordingly. These factors are: Sampling Frequency offset (SFO), Carrier Frequency Offset (CFO), Phase and Delay offsets at each antenna. To compensate for the effect of these critical factors, we propose an offline calibration method to compensate for all their effects. This thesis will also include other theoretical methods that have to deal with Angle-of-Arrival Estimation problem from compressed sensing and signal processing point of views.

Dans le cadre d'un suivi régulier de patients âgés pouvant souffrir de maladie d'Alzheimer, de nombreuses applications, dont leur localisation, s'avèrent utiles. Un système de localisation compact dédié à un environnement en intérieure est nécessaire. Cette thèse est dédiée à la réalisation d'un système de localisation pouvant répondre à cette attente. Le système développé (Indoor Localisation System, ILS) permet la localisation en trois dimensions d'un badge actif (Active Tag, AT) relativement à une ancre unique (Localisation Base Station, LBS). Le système utilise le principe de radar monopulse multistatique FMCW(Frequency Modulation Continuos Wave) et exploite la bande de fréquence Européenne ULB (6-8.5 GHz). La méthode employée pour l'ILS est une méthode goniométrique se basant sur la mesure conjointe de la différence de fréquence d'arrivée (FDoA) et la différence de phase d'arrivée (PDoA) pour l'estimation de la distance radiale et des angles de direction (azimut et élévation) de l'AT relativement au plan formé par l'ILS. Afin de valider ce système, un prototype d'ILS a été réalisé à Télécom ParisTech.L'objectif de cette thèse est d'obtenir un système de localisation compact permettant de localiser un badge actif avec une précision submétrique dédié pour les environnements en intérieurs exposés aux problèmes de multi-trajets. / Regular and accurate position monitoring of elderly suffering from dementia related problems (Alzheimer) may be required. To assist their monitoring a compact and a less complex indoor localization system is compulsary. This thesis is dedicated to design a Line-of-Sight (LoS) system to allow the indoor localization. The thesis aims to develop an Indoor Localization System (ILS) for three-dimension position estimates with respect to single Localization Base Station as an anchor. The designed ILS uses an Active-Tag (AT) as remote targel. The system uses the monopulse multistatic FMCW radar principle and covers the European UWB (6-8.5 GHz) frequency band. The designed ILS is based on the frequency-difference of arrival (FDoA) and the phase-difference-of-arrival (POoA) techniques for the radial-distance and the angles (azimuth and elevation) estimates. In order to validate this system, a prototype of the ILS is designed at Telecom ParisTech, France.The objective of the designed ILS is to have a localization system with an accuracy in few centimeters in Line-of-Sight condition. The system is designed to need a single anchor, and simultaneously addressing the indoor challenges such as multipaths, strong signal attenuations, reflections, etc.

For optical propagation through the turbulent atmosphere, the angle of arrival (AOA) cross-correlation function obtained from two spatially separated light sources carries information regarding the transverse wind velocity averaged along the propagation path. Two methods for the retrieval of the beam transverse horizontal wind velocity, v_t, based on the estimation of the time delay to the peak and the slope at zero lag of the AOA cross-correlation function, are presented. Data collected over a two week long experimental campaign conducted at the Boulder Atmospheric Observatory (BAO) site near Erie, CO was analyzed. The RMS difference between 10 s estimates of v_t retrieved optically, and 10 s averages of the transverse horizontal wind velocity measured by an ultrasonic anemometer, was found to be 14 cm/s for the time-delay-to- peak method and 20 cm/s for the slope-at-zero-lag method, for a 2 h period beginning 0345 MDT on 16 June, 2010, during which the transverse horizontal wind velocity varied between -1 m/s and 2 m/s.

The angle of arrival estimation of multiple sources plays a vital role in the field of array signal processing as MIMO systems can be employed at both the transmitter and the receiver end and the system capacity, reliability and throughput can be significantly increased by using array signal processing. Almost all applications require accurate direction of arrival (DOA) estimation to localize the sources of the signals. Another important parameter of localization systems is the array geometry and sensor design which can be application specific and is used to estimate the DOA. In this work, various array geometries and arrival estimation algorithms are studied and then a new scheme for multiple source estimation is proposed and evaluated based on the performance of subspace and non-subspace decomposition methods. The proposed scheme has shown to outperform the conventional Multiple Signal Classification (MUSIC) estimation and Bartlett estimation techniques. The new scheme has a better performance advantage at low and high signal to noise ratio values (SNRs). The research work also studies different array geometries for both single and multiple incident sources and proposes a geometry which is cost effective and efficient for 3, 4, and 5 antenna array elements. This research also considers the shape of the ground plane and its effects on the angle of arrival estimation and in addition it shows how the mutual couplings between the elements effect the overall estimation and how this error can be minimised by using a decoupling matrix. At the end, a novel miniaturised multi element reconfigurable antenna to represent the receiver base station is designed and tested. The antenna radiation patterns in the azimuth angle are almost omni-directional with linear polarisation. The antenna geometry is uniplanar printed logspiral with striplines feeding network and biased components to improve the impedance bandwidth. The antenna provides the benefit of small size, and re-configurability and is very well suited for the asset tracking applications.

In recent years, unmanned aerial vehicles (UAV), also called drones, have experienced a rapid increase, which leads to the concern of illegal use of them. Passive RF is one of the effective ways to detect drones by receiving drones’ communication signals. After receiving the signal from drones, one can utilize the prior knowledge of signal characteristics for identifying and locating the drones. The angle of arrival (AoA) measured by multiple passive RF sensors can be used for localization by triangulation. However, the accuracy of the AoA measured by the passive RF sensors is strongly affected by the environment. In particular in urban areas, the multipath effect is prominent due to the building blockage and complicated terrestrial conditions that introduce certain errors to the result. So the service provider of the sensors needs a tool to perform the environment analysis to understand the quality of the service. A fast tool that can simulate the sensor network and surrounding environment can offer a flexible solution to optimize the sensor coverage and indicate the blind zone of detection. Especially when the sensors are deployed on the mobile platform, such tool can significantly improve the defensive quality of the drone detection system by optimizing real-time deployment and indicating low observable areas. In order to plan the sensor locations and assess the performance after the deployment of the sensor at a fast speed, We propose a multipath-based model to calculate the AoA error. The model is able to utilize the input of geometrical information for simulating the AoA error within a region. In this thesis, we investigate the outdoor channel at 2.4GHz using the ray-tracing method as it is the most used channel for UAVs. Massive simulations have been carried out and real test flights have been conducted to evaluate the accuracy of the modeling. Both simulations and test flights are carried out in Kista center where buildings are from high-rises to one-floor houses with various heights. In the simulation, the AoA is obtained by MUltiple SIgnal Classification (MUSIC) algorithm. Test flights are conducted using an existing Software-defined radio (SDR) based RF sensor. We tried our best to carry out the same trajectories in both simulations and test flights to provide fair comparisons. The simulation results show that the multipath model can predict the trend of AoA error when the height changes, while not sufficient to predict the error when the 2D position changes. Thus, to more accurately characterize the signal transmission, it is essential to extend this thesis to include more detailed environmental information and adaption based on measurement. / Under de senaste åren har obemannade flygfarkoster (UAV), även kallade drönare, ökat snabbt, vilket leder till oro för olaglig användning av dem. Passiv RF är ett av de effektiva sätten att upptäcka drönare genom att ta emot drönarnas kommunikationssignaler. Efter att ha tagit emot signalen från drönare kan man använda den tidigare kunskapen om signalegenskaperna för att identifiera och lokalisera drönarna. AoA som mäts av flera passiva RF-sensorer kan användas för lokalisering genom triangulering. Noggrannheten hos AoA som mäts av de passiva RF-sensorerna påverkas dock starkt av miljön. Särskilt i stadsområden är multipath-effekten framträdande på grund av byggnadsblockering och komplicerade markförhållanden som medför vissa fel i resultatet. Därför behöver leverantören av sensorer ett verktyg för att utföra miljöanalysen för att förstå tjänstens kvalitet. Ett snabbt verktyg som kan simulera sensornätverket och den omgivande miljön kan erbjuda en flexibel lösning för att optimera sensortäckningen och ange den blinda zonen för upptäckt. Särskilt när sensorerna placeras på en mobil plattformkan ett sådant verktyg avsevärt förbättra drönardetektionssystemets försvarskvalitet genom att optimera utplaceringen i realtid och ange områden med låg observationsgrad. För att planera sensorernas placering och bedöma prestandan efter att sensorn har placerats ut i snabb takt föreslår vi en multipath-baserad modell för att beräkna AoAfelet. Modellen kan utnyttja inmatningen av geometrisk information för att simulera AoA-felet inom ett område. I denna avhandling undersöker vi utomhuskanalen vid 2:4 GHz med hjälp av raytracing- metoden eftersom det är den mest använda kanalen för UAV:er. Massiva simuleringar har utförts och verkliga testflygningar har genomförts för att utvärdera modelleringens noggrannhet. Både simuleringar och testflygningar har utförts i Kista centrum där byggnaderna är allt från höghus till envåningshus med olika höjd. I simuleringen erhålls AoA med hjälp av MUSIC-algoritmen. Testflygningar genomförs med hjälp av en befintlig SDR-baserad RF-sensor. Vi gjorde vårt bästa för att utföra samma banor i både simuleringar och testflygningar för att ge rättvisa jämförelser. Simuleringsresultaten visar att multipathmodellen kan förutsäga trenden för AoA-felet när höjden ändras, medan den inte är tillräcklig för att förutsäga felet när 2D-positionen ändras. För att mer exakt karakterisera signalöverföringen är det därför viktigt att utöka denna avhandling till att omfatta mer detaljerad miljöinformation och anpassning baserad på mätning.

Due to the massive increase of active transmitters broadcasting over wideband frequencies, such as 5G wireless systems, LEO/MEO satellites, satellite constellations, and the increase of IoT devices in the average home, the radio frequency spectrum is becoming more and more congested by interference. Passive receivers face additional challenges due to the growing use of wideband frequency transmissions aimed at boosting communication system throughput. As a result, passive receivers must adopt more robust and intricate techniques to mitigate radio frequency interference. A proposed RFI removal system, known as the true time delay Hadamard projection algorithm, has been introduced in previous work to eliminate a single RFI source while preserving a narrowband signal of interest. An RF frontend is developed to assess the effectiveness of the Hadamard projection algorithm implemented on an RFSoC ZCU216. Additionally, the TTD Hadamard projection algorithm is expanded to enable the cancellation of multiple RFI sources rather than just a single source for a uniform linear array and a uniform rectangular array. Over-the-air tests are conducted to verify the performance of the interference cancellation algorithms and demonstrate the algorithms' ability to preserve the signals of interest while removing the wideband interference. Multiple algorithms are proposed to estimate the time delays used by the interference cancellation algorithm to effectively eliminate wideband interference. These algorithms address diverse scenarios encompassing interference sources ranging from strong to weak SNR. Detailed reports of algorithm performance provide insights into their effectiveness and suitability across specific interference conditions.

In the sixth generation of wireless communication systems (6G), there exist multiple candidate enabling technologies that help the wireless network satisfy the ever-increasing demand for speed, coverage, reliability, and mobility. Among these technologies, reconfigurable intelligent surfaces (RISs) extend the coverage of a wireless network into dead zones, increase capacity, and facilitate integrated sensing and communications tasks by consuming very low power, thus contributing to energy efficiency as well. RISs are meta-material-based devices whose electromagnetic reflection characteristics can be controlled externally to cater to the needs of the communication links. Most ubiquitously, this comes in the form of adding a desired phase shift to an incident wave before reflecting it, which can be used to phase-align multiple incident waves to increase the strength of the signal at the receiver and provide coverage to an area that otherwise would be a dead zone. While this portrays an image of a dream technology that would boost the existing wireless networks significantly, RISs do not come without engineering problems. First of all, the individual elements do not exhibit ideal reflection characteristics, that is, they attenuate the incident signal in a fashion depending on the configured phase shift. This creates the phenomenon called "phase-dependent amplitude". Another problem caused by RISs is the channel estimation overhead. In a multiple-antenna communication system, the channel between two terminals is as complex as the product of the number of antennas at each end. However, when an RIS comes into the equation, the cascade of the transmitter-RIS and RIS-receiver channels has a complexity further multiplied by the number of RIS elements. Consequently, the channel estimation process to utilize the RIS effectively becomes more demanding, that is, more pilot signals are required to estimate the channel for coherent reception. This adversely affects the effective data rate within a communication system since more resources need to be spent for pilot transmission and fewer resources can be allocated for data transmission. While there exists some work on reducing the channel dimensions by exploiting the channel structure, this problem persists for unstructured channels. In addition, for the wireless networks using multiple RISs, a new kind of pilot contamination arises, which is the main topic of this thesis. In the first part of this thesis, we study this new kind of pilot contamination in a multi-operator context, where two operators provide services to their respective served users and share a single site. Each operator has a single dedicated RIS and they use disjoint frequency bands, but each RIS inadvertently reflects the transmitted uplink signals of the user equipment devices in multiple bands. Consequently, the concurrent reflection of pilot signals during the channel estimation phase introduces a new inter-operator pilot contamination effect. We investigate the implications of this effect in systems with either deterministic or correlated Rayleigh fading channels, specifically focusing on its impact on channel estimation quality, signal equalization, and channel capacity. The numerical results demonstrate the substantial degradation in system performance caused by this phenomenon and highlight the pressing need to address inter-operator pilot contamination in multi-operator RIS deployments. To combat the negative effect of this new type of pilot contamination, we propose to use orthogonal RIS configurations during uplink pilot transmission, which can mitigate or eliminate the negative effect of inter-operator pilot contamination at the expense of some inter-operator information exchange and orchestration. In the second part of this thesis, we consider a single-operator-two-RIS integrated sensing and communication (ISAC) system where the single user is both a communication terminal and a positioning target. Based on the uplink positioning pilots, the base station aims to estimate both the communication channel and the user's position within the indoor environment by estimating the angle of arrival (AoA) of the impinging signals on both RISs and then exploiting the system and array geometries to estimate the user position and user channels respectively. Although there is a single operator, due to the presence of multiple RISs, pilot contamination occurs through the same physical means as multi-operator pilot contamination unless the channel estimation process is parameterized. Since the communication links are considered to be pure line-of-sight (LOS), their structure allows the reduction of the number of unknown parameters. Consequently, the reduction of information caused by pilot contamination does not affect the channel estimation procedure, hence the pilot contamination is overcome. On the other hand, the position of the user is determined by intersecting the lines drawn along the AoA estimates. We adopt the Cramér-Rao Lower Bound (CRLB), the lower bound on the mean squared error (MSE) of any unbiased estimator, for both channel estimation and positioning. Our numerical results show that it is possible to utilize positioning pilots for parametric channel estimation when the wireless links are LOS. / <p>QC 20240416</p>