In wireless networks, an essential step for precise range-based localization is the high-resolution estimation of multipath channel delays. The resolution of traditional delay estimation algorithms is inversely proportional to the bandwidth of the training signals used for channel probing. Considering that typical training signals have limited bandwidth, delay estimation using these algorithms often leads to poor localization performance. To mitigate these constraints, we exploit the multiband and carrier frequency switching capabilities of wireless transceivers and propose to acquire channel state information (CSI) in multiple bands spread over a large frequency aperture. The data model of the acquired measurements has a multiple shift-invariance structure, and we use this property to develop a high-resolution delay estimation algorithm. We derive the Cramér-Rao Bound (CRB) for the data model and perform numerical simulations of the algorithm using system parameters of the emerging IEEE 802.11be standard. Simulations show that the algorithm is asymptotically efficient and converges to the CRB. To validate modeling assumptions, we test the algorithm using channel measurements acquired in real indoor scenarios. From these results, it is seen that delays (ranges) estimated from multiband CSI with a total bandwidth of 320 MHz show an average RMSE of less than 0.3 ns (10 cm) in 90% of the cases.

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Over the last two decades, we have witnessed a tremendous evolution of wireless communication systems. For example, the data rates in mobile wireless systems have increased from a few tens of kilobits per second to 10 gigabits per second between the first and last, i.e., fifth generation (5G). The main enablers for this growth are signal processing and radio frequency (RF) hardware innovations, which led to more efficient modulation and coding schemes and high-performance RF transceivers. Following these trends, future wireless systems such as 6G and WiFi-7 aim for even higher data rates, requiring higher frequency ranges, wider bandwidths, and massive antenna arrays. These developments pave the way toward joint communication and sensing RF systems with very high range, Doppler, and angular resolutions. In particular, favorable signal and RF transceiver properties such as large bandwidth will enable precise RF localization in rich scattering environments such as indoor or urban canyons where multipath effects severely impair the performance of traditional localization systems like GNSS (Global Navigation Satellite Systems). At the same time, the wide range of emerging applications in areas of autonomous navigation, assisted living, and Internet-of-Things require precise localization, often to cm-level degree accuracy. Therefore, it is evident that new localization approaches and signal processing algorithms that can exploit signal and transceiver properties of emerging wireless systems are needed to solve the problem of precise localization in multipath environments and lead the way to novel applications. The goal of this thesis is to design signal processing algorithms and protocols that will enable precise ranging in multipath environments while using practical single-antenna RF transceivers. In the first part of this thesis, we introduce a multiband channel model to describe multipath channel measurements collected over multiple separate frequency bands using narrowband and wideband RF transceivers. This model shows that multiband channel measurements have multiple shift-invariance property and that by increasing the frequency aperture of the multiband measurements, we can improve the resolution of multipath time-delay estimation. We use this property of the measurements to develop high-resolution time-delay estimation algorithms based on subspace estimation. To illustrate the performance of these algorithms, we perform extensive numerical experiments which demonstrate that the proposed algorithms are statistically efficient and that multiband time-delay estimation enables precise ranging in multipath environments. However, the aforementioned results also show that the proposed algorithms are sensitive to errors introduced by hardware impairments of RF transceivers and imperfect calibration. In the second part of the thesis, we focus on the problem of joint RF transceiver calibration and high-resolution time-delay estimation. For example, in practical scenarios, the frequency response of RF transceivers might not be known nor calibrated, and performing time-delay estimation without calibrating these effects will lead to biased estimates. We show that the problem of joint RF transceiver calibration and time-delay estimation can be formulated as a particular case of covariance matching, which after reformulation, can be solved using a simple group Lasso algorithm. Likewise, due to imperfections of oscillators used in RF transceivers, the mobile and anchor nodes are usually not frequency synchronized. This frequency offset severely deteriorates the performance of multiband ranging methods. To solve this issue, we design a two-way protocol for collecting multiband channel measurements and a weighted least squares-based algorithm that enable joint clock synchronization and ranging. Finally, in the last part of the thesis, we validate our modeling assumptions and illustrate the performance of the multiband time-delay estimation algorithms by considering practical scenarios of localization in future WiFi-7 networks. For these experiments, we use real indoor multipath channel measurements collected in a hospital and a university building environment. The results of the experiments show that using multiband channel measurements with a total bandwidth of 320 MHz, the absolute ranging error is smaller than 4 cm in 80% of the cases. Likewise, using the same scenario setup and three anchors to localize the mobile node, it is observed that the positioning error is below 24 cm in 95% of the cases. These results show that by using the advanced signal processing techniques to design estimation algorithms and channel measurement protocols that can exploit the properties and degrees of freedom offered by future wireless systems and RF transceivers, decimeter-level accurate positioning is achievable. The signal processing models presented in this thesis are common to the wide area of array signal processing applications, such as radar and ultrasound imaging. Therefore, the results presented in this thesis impact these application areas as well. @en

The presence of rich scattering in indoor and urban radio propagation scenarios may cause a high arrival density of multipath components (MPCs). Often the MPCs arrive in clusters at the receiver, where MPCs within one cluster have similar angles and delays. The MPCs arriving within a single cluster are typically unresolvable in the delay domain. In this paper, we analyze the effects of unresolved MPCs on the bias of the delay estimation with a multiband subspace fitting algorithm. We treat the unresolved MPCs as a model error that results in perturbed subspace estimation. Starting from the first-order approximation of the perturbations, we derive the bias of the delay estimate of the line-of-sight (LOS) component. We show that it depends on the power and relative delay of the unresolved MPCs in the first cluster compared to the LOS component. Numerical experiments are included to show that the derived expression for the bias well describes the effects of unresolved MPCs on the delay estimation.@en

This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY,MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-ofservice (QoS) and quality-of-experience (QoE).We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

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For validation and demonstration of high accuracy ranging and positioning algorithms and systems, a wideband radio signal generation and acquisition testbed, tightly synchronized in time and frequency, is needed. The development of such a testbed requires solutions to several challenges. Tight time and frequency synchronization, derived from a centrally distributed time-frequency reference signal, needs to be maintained in the hardware of the transmitter and receiver nodes, and wideband signal acquisition requires sustainable data throughput between the receiver and host PC as well as data storage at GB level. This article presents a testbed for wideband radio signal acquisition, for validation and demonstration of high accuracy ranging and positioning. It consists of multiple Ettus X310 universal software radio peripherals (USRPs) and supports high accuracy (<100 ps) time-deterministic, sustainable signal transmission and acquisition, with a bandwidth up to 320 MHz (in dual channel mode) and frequencies up to 6 GHz. Generation and processing of wideband arbitrary signal waveforms is done offline. To realize these features, radio frequency on chip (RFNoC) compatible HDL units were developed for integration in the X310 SDR platform. Wideband transmission and signal acquisition at a lower duty cycle is applied to reduce the data offloading throughput to the host's personal computer (PC). Benchmarking of the platform was performed to demonstrate sustainable long duration dual channel acquisition. Indoor range measurements with the synchronous operation of the testbed show a decimeter-level accuracy.

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Global Navigation Satellite Systems (GNSS) are nowadays the most common solutions used to cope with Positioning-Navigation Timing (PNT) applications demands. GNSS are relied on in very diverse contexts and domains, yet the interest in systems such as GPS, GALILEO and Beidou is continuously increasing. However, and in particular for safety critical applications, GNSS are very vulnerable to unintentional interference and to intentional attacks such as spoofing or jamming. GNSS also provide degraded accuracy in dense multipath environments such as in urban canyons. Thus, solutions that could augment, back-up, complement, or surrogate GNSS, are actively sought after. In this paper, we introduce the concept of a hybrid optical wireless positioning system and present the initial experimental positioning results. The system uses optically distributed time and frequency reference signals for synchronization, and wideband radio signals for ranging. Initial results show that decimeter-level accuracy is obtained in urbanlike surroundings.@en

The multipath radio channel is considered to have a non-bandlimited channel impulse response. Therefore, it is challenging to achieve high resolution time-delay (TD) estimation of multipath components (MPCs) from bandlimited observations of communication signals. It this paper, we consider the problem of multiband channel sampling and TD estimation of MPCs. We assume that the nonideal multi-branch receiver is used for multiband sampling, where the noise is nonuniform across the receiver branches. The resulting data model of Hankel matrices formed from acquired samples has multiple shift-invariance structures, and we propose an algorithm for TD estimation using weighted subspace fitting. The subspace fitting is formulated as a separable nonlinear least squares (NLS) problem, and it is solved using a variable projection method. The proposed algorithm supports high resolution TD estimation from an arbitrary number of bands, and it allows for nonuniform noise across the bands. Numerical simulations show that the algorithm almost attains the Cramér Rao Lower Bound, and it outperforms previously proposed methods such as multiresolution TOA, MI-MUSIC, and ESPRIT.@en

In this paper, we focus on the problem of blind joint calibration of multiband transceivers and time-delay (TD) estimation of multipath channels. We show that this problem can be formulated as a particular case of covariance matching. Although this problem is severely ill-posed, prior information about radio-frequency chain distortions and multipath channel sparsity is used for regularization. This approach leads to a biconvex optimization problem, which is formulated as a rank-constrained linear system and solved by a simple group Lasso algorithm. Numerical experiments show that the proposed algorithm provides better calibration and higher resolution for TD estimation than current state-of-the-art methods.@en

In order to validate and demonstrate newly developed ranging techniques, a flexible test platform for signal acquisition enabling offline signal processing is generally needed. Developing such a platform becomes challenging when working with wideband (> 100MHz) signals due to the critical timing, the very high sampling rates and the huge data throughput involved. In this paper, we introduce an Ettus X310 SDR platform using custom designed logic allowing for dual-channel 400 Msps data transmission and acquisition for centimeter level ranging applications. Furthermore, we present initial measurement results as a benchmark of the platform, which show that the time delay of a 10 m cable can be estimated with high accuracy, in the order of 50 ps.@en

Synchronization and ranging in internet of things (IoT) networks are challenging due to the narrowband nature of signals used for communication between IoT nodes. Recently, several estimators for range estimation using phase difference of arrival (PDoA) measurements of narrowband signals have been proposed. However, these estimators are based on data models which do not consider the impact of clock-skew on the range estimation. In this paper, clock-skew and range estimation are studied under a unified framework. We derive a novel and precise data model for PDoA measurements which incorporates the unknown clock-skew effects. We then formulate joint estimation of the clock-skew and range as a two-dimensional (2-D) frequency estimation problem of a single complex sinusoid. Furthermore, we propose: (i) a two-way communication protocol for collecting PDoA measurements and (ii) a weighted least squares (WLS) algorithm for joint estimation of clock-skew and range leveraging the shift invariance property of the measurement data. Finally, through numerical experiments, the performance of the proposed protocol and estimator is compared against the Cramér Rao lower bound demonstrating that the proposed estimator is asymptotically efficient.

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Achieving high resolution time-of-arrival (TOA) estimation in multipath propagation scenarios from bandlimited observations of communication signals is challenging because the multipath channel impulse response (CIR) is not bandlimited. Modeling the CIR as a sparse sequence of Diracs, TOA estimation becomes a problem of parametric spectral inference from observed bandlimited signals. To increase resolution without arriving at unrealistic sampling rates, we consider multiband sampling approach, and propose a practical multibranch receiver for the acquisition. The resulting data model exhibits multiple shift invariance structures, and we propose a corresponding multiresolution TOA estimation algorithm based on the ESPRIT algorithm. The performance of the algorithm is compared against the derived Cramér Rao Lower Bound, using simulations with standardized ultra-wideband (UWB) channel models. We show that the proposed approach provides high resolution estimates while reducing spectral occupancy and sampling costs compared to traditional UWB approaches.

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This paper presents end-to-end learning from spectrum data-an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural networks. End-to-end learning allows to: 1) automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments and 2) train wireless signal classifiers in one end-to-end step which eliminates the need for complex multi-stage machine learning processing pipelines. The purpose of this paper is to present the conceptual framework of end-to-end learning for spectrum monitoring and systematically introduce a generic methodology to easily design and implement wireless signal classifiers. Furthermore, we investigate the importance of the choice of wireless data representation to various spectrum monitoring tasks. In particular, two case studies are elaborated: 1) modulation recognition and 2) wireless technology interference detection. For each case study three convolutional neural networks are evaluated for the following wireless signal representations: temporal IQ data, the amplitude/phase representation, and the frequency domain representation. From our analysis, we prove that the wireless data representation impacts the accuracy depending on the specifics and similarities of the wireless signals that need to be differentiated, with different data representations resulting in accuracy variations of up to 29%. Experimental results show that using the amplitude/phase representation for recognizing modulation formats can lead to performance improvements up to 2% and 12% for medium to high SNR compared to IQ and frequency domain data, respectively. For the task of detecting interference, frequency domain representation outperformed amplitude/phase and IQ data representation up to 20%.@en