Joint Communication and Positioning based on Channel Estimation

Mobile wireless communication systems have rapidly and globally become an integral part of everyday life and have brought forth the internet of things. With the evolution of mobile wireless communication systems, joint communication and positioning becomes increasingly important and enables a growing range of new applications. Humanity has already grown used to having access to multimedia data everywhere at every time and thereby employing all sorts of location-based services. Global navigation satellite systems can provide highly accurate positioning results whenever a line-of-sight path is available. Unfortunately, harsh physical environments are known to degrade the performance of existing systems. Therefore, ground-based systems can assist the existing position estimation gained by satellite systems. Determining positioning-relevant information from a unified signal structure designed for a ground-based joint communication and positioning system can either complement existing systems or substitute them. Such a system framework promises to enhance the existing systems by enabling a highly accurate and reliable positioning performance and increased coverage. Furthermore, the unified signal structure yields synergetic effects. In this thesis, I propose a channel estimation-based joint communication and positioning system that employs a virtual training matrix. This matrix consists of a relatively small training percentage, plus the detected communication data itself. Via a core semi- blind estimation approach, this iteratively includes the already detected data to accurately determine the positioning-relevant parameter, by mutually exchanging information between the communication part and the positioning part of the receiver. Synergy is created. I propose a generalized system framework, suitable to be used in conjunction with various communication system techniques. The most critical positioning-relevant parameter, the time-of-arrival, is part of a physical multipath parameter vector. Estimating the time-of-arrival, therefore, means solving a global, non-linear, multi-dimensional optimization problem. More precisely, it means solving the so-called inverse problem. I thoroughly assess various problem formulations and variations thereof, including several different measurements and estimation algorithms. A significant challenge, when it comes to solving the inverse problem to determine the positioning-relevant path parameters, is imposed by realistic multipath channels. Most parameter estimation algorithms have proven to perform well in moderate multipath environments. It is mathematically straightforward to optimize this performance in the sense that the number of observations has to exceed the number of parameters to be estimated. The typical parameter estimation problem, on the other hand, is based on channel estimates, and it assumes that so-called snapshot measurements are available. In the case of realistic channel models, however, the number of observations does not necessarily exceed the number of unknowns. In this thesis, I overcome this problem, proposing a method to reduce the problem dimensionality via joint model order selection and parameter estimation. Employing the approximated and estimated parameter covariance matrix inherently constrains the estimation problem’s model order selection to result in optimal parameter estimation performance and hence optimal positioning performance. To compare these results with the optimally achievable solution, I introduce a focused order-related lower bound in this thesis. Additionally, I use soft information as a weighting matrix to enhance the positioning algorithm positioning performance. For demonstrating the feasibility and the interplay of the proposed system components, I utilize a prototype system, based on multi-layer interleave division multiple access. This proposed system framework and the investigated techniques can be employed for multiple existing systems or build the basis for future joint communication and positioning systems. The assessed estimation algorithms are transferrable to all kinds of joint communication and positioning system designs. This thesis demonstrates their capability to, in principle, successfully cope with challenging estimation problems stemming from harsh physical environments

UNDERWATER COMMUNICATIONS WITH ACOUSTIC STEGANOGRAPHY: RECOVERY ANALYSIS AND MODELING

In the modern warfare environment, communication is a cornerstone of combat competence. However, the increasing threat of communications-denied environments highlights the need for communications systems with low probability of intercept and detection. This is doubly true in the subsurface environment, where communications and sonar systems can reveal the tactical location of platforms and capabilities, subverting their covert mission set. A steganographic communication scheme that leverages existing technologies and unexpected data carriers is a feasible means of increasing assurance of communications, even in denied environments. This research works toward a covert communication system by determining and comparing novel symbol recovery schemes to extract data from a signal transmitted under a steganographic technique and interfered with by a simulated underwater acoustic channel. We apply techniques for reliably extracting imperceptible information from unremarkable acoustic events robust to the variability of the hostile operating environment. The system is evaluated based on performance metrics, such as transmission rate and bit error rate, and we show that our scheme is sufficient to conduct covert communications through acoustic transmissions, though we do not solve the problems of synchronization or equalization.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Gemeinsame Kommunikation und Positionierung basierend auf Interleave-Division Multiplexing

Interest in joint communication and positioning is steadily increasing because the combination of both techniques offers a wide range of advantages. On the one hand, synergy effects between communication and positioning like enhanced resource allocation can be exploited. On the other hand, new applications are enabled. Examples comprise a wide area of interest and include the automated localisation of emergency calls, tracking and guiding fire fighters or policemen on a mission, monitoring people with special needs in a hospital or a nursing home, asset tracking, location-based services and so forth. However, it is a challenging task to combine communication and positioning because their prerequisites are quite different. On the one hand, high data rates with little training overhead and low bit error rate are desirable for communication. On the other hand, localisation aims at precise position estimates. Much training is typically spent for that purpose. Given a single transmit signal supporting communication as well as positioning, it is very difficult to fulfil all requirements at the same time. Hence, a flexible configuration is desirable for a joint communication and positioning system with a unified signal structure in order to adjust the tradeoff between both parts to the instantaneous needs. In this thesis, a new system concept for joint communication and positioning with a unified signal structure is proposed and investigated. The system concept is based on interleave-division multiplexing (IDM) in combination with pilot layer aided channel estimation (PLACE) and multilateration via the time of arrival (TOA). On the one hand, IDM seems to be a suitable candidate for a joint communication and positioning system because of its flexible but simple transmitter structure. On the other hand, multilateration via the TOA enables precise localisation. The connection between the communication and the positioning part is accomplished via an enhanced PLACE unit. Through the incorporation of a channel parameter estimator, not only the channel coefficients of the equivalent discrete-time channel model, that are needed for data detection, but also parameters of the physical channel, that are required for positioning, can be estimated. A priori information about pulse shaping and receive filtering is exploited for that purpose. The main aim of this thesis is to show the feasibility of the proposed joint communication and positioning system. Hence, a fundamental system setup is analysed systematically. Since many applications of joint communication and positioning are located in urban or indoor environments, a very high positioning accuracy in the centimetre region is desirable. Unfortunately, positioning is most challenging in these environments due to severe multipath propagation. In order to achieve the required accuracies, the positioning part of the proposed system concept can be complemented by other localisation sources like GPS/Galileo and/or motion sensors via sensor fusion. However, the stand-alone performance of the proposed joint communication and positioning system is evaluated by means of Monte Carlo simulations in this thesis. The achieved results are compared to performance limits in terms of Cramer-Rao lower bounds. In order to improve the overall system performance and to enable sensor fusion, soft information with respect to the parameter as well as the position estimates is taken into account. The accuracy of the soft information is analysed with the help of curvature measures. Altogether, promising results are obtained.Das Interesse an gemeinsamer Kommunikation und Positionierung nimmt aufgrund vieler Vorteile stetig zu: Durch die Kombination beider Techniken können Synergieeffekte wie beispielsweise eine verbesserte Ressourcenverteilung ausgenutzt werden. Des Weiteren werden neue Anwendungen in den unterschiedlichsten Bereichen ermöglicht: Notrufe können automatisch lokalisiert werden, Feuerwehrmänner und Polizisten im Einsatz können durch eine Verfolgung ihrer Position und gegebenenfalls eine Überwachung ihrer Vitalwerte besser angeleitet und koordiniert werden, Patienten mit speziellen Bedürfnissen in Krankenhäusern können durch ein effizientes Monitoring besser versorgt werden, Ein- und Auslagerungsprozesse in Warenhäusern können erleichtert werden, positionsbezogene Dienste können realisiert werden und vieles anderes mehr. Aufgrund der verschiedenen Anforderungen von Kommunikations- und Positionierungsdiensten ist es schwierig, diese beiden Bereiche zu vereinen. Einerseits sollen große Datenraten mit geringem Trainingsaufwand als auch geringen Bitfehlerraten erreicht werden. Andererseits ist eine hohe Positionierungsgenauigkeit erwünscht, die einen großen Trainingsaufwand erfordert. In einem Systementwurf mit einer einheitlichen Signalstruktur ist es schwer, alle Anforderungen gleichzeitig zu erfüllen. Daher ist ein flexibler Systementwurf von Vorteil, um den Abtausch zwischen Kommunikation und Positionierung an die aktuellen Bedürfnisse anpassen zu können. Im Rahmen dieser Arbeit wird ein neues gemeinsames Kommunikations- und Positionierungssystem mit einer einheitlichen Signalstruktur vorgeschlagen und untersucht. Der Systementwurf basiert auf Interleave-Division Multiplexing (IDM) in Kombination mit einer Pilotlayer basierten Kanalschätzung und Multilateration mit Hilfe der Signalankunftszeit, im Folgenden Time of Arrival (TOA) genannt. Einerseits ist IDM aufgrund seiner flexiblen, jedoch einfachen Senderstruktur gut für ein gemeinsames Kommunikations- und Positionierungssystem geeignet. Andererseits ermöglicht eine Multilateration mit Hilfe der TOA hohe Positionierungsgenauigkeiten. Die Verbindung zwischen beiden Komponenten wird durch eine erweiterte Pilotlayer basierte Kanalschätzung erreicht: Durch die Verwendung eines Kanalparameterschätzers können sowohl die Kanalkoeffizienten des äquivalenten zeitdiskreten Ersatzkanalmodells, die für die Datendetektion benötigt werden, als auch Parameter des physikalischen Kanals, die für die Lokalisierung erforderlich sind, geschätzt werden. A priori Information bezüglich des Pulsformungs- und Empfangsfilters werden hierfür ausgenutzt. Das Hauptziel dieser Arbeit ist es, die Realisierbarkeit des vorgeschlagenen gemeinsamen Kommunikations- und Positionierungssystems zu zeigen. Daher wird ein grundlegender Systementwurf systematisch analysiert. Da viele Anwendungen von gemeinsamer Kommunikation und Positionierung innerhalb von Städten oder Gebäuden angesiedelt sind, ist eine sehr hohe Positionierungsgenauigkeit im Zentimeter-Bereich wünschenswert. Unglücklicherweise ist es in diesen Gebieten aufgrund von starker Mehrwegeausbreitung besonders schwer, die Position eines Objektes zu bestimmen. Allerdings kann die Positionierungskomponente durch andere Lokalisierungsquellen wie beispielsweise GPS/Galileo und/oder Bewegungssensoren mittels Sensorfusion ergänzt werden, um die erforderlichen Genauigkeiten zu erreichen. In Rahmen dieser Arbeit wird jedoch nur die eigenständige Leistungsfähigkeit des vorgeschlagenen Systementwurfs mit Hilfe von Monte Carlo Simulationen untersucht. Die Simulationsergebnisse werden mit Leistungsgrenzen in Form von Cramer-Rao Untergrenzen verglichen. Dabei wird Zuverlässigkeitsinformation bezüglich der geschätzten Parameter und der geschätzten Position berücksichtigt, um die gesamte Systemleistung zu verbessern und Sensorfusion zu ermöglichen. Die Genauigkeit der Zuverlässigkeitsinformation wird mit Hilfe von Krümmungsmaßen analysiert. Insgesamt werden vielversprechende Ergebnisse erzielt

Spectrum Sharing, Latency, and Security in 5G Networks with Application to IoT and Smart Grid

The surge of mobile devices, such as smartphones, and tables, demands additional capacity. On the other hand, Internet-of-Things (IoT) and smart grid, which connects numerous sensors, devices, and machines require ubiquitous connectivity and data security. Additionally, some use cases, such as automated manufacturing process, automated transportation, and smart grid, require latency as low as 1 ms, and reliability as high as 99.99\%. To enhance throughput and support massive connectivity, sharing of the unlicensed spectrum (3.5 GHz, 5GHz, and mmWave) is a potential solution. On the other hand, to address the latency, drastic changes in the network architecture is required. The fifth generation (5G) cellular networks will embrace the spectrum sharing and network architecture modifications to address the throughput enhancement, massive connectivity, and low latency. To utilize the unlicensed spectrum, we propose a fixed duty cycle based coexistence of LTE and WiFi, in which the duty cycle of LTE transmission can be adjusted based on the amount of data. In the second approach, a multi-arm bandit learning based coexistence of LTE and WiFi has been developed. The duty cycle of transmission and downlink power are adapted through the exploration and exploitation. This approach improves the aggregated capacity by 33\%, along with cell edge and energy efficiency enhancement. We also investigate the performance of LTE and ZigBee coexistence using smart grid as a scenario. In case of low latency, we summarize the existing works into three domains in the context of 5G networks: core, radio and caching networks. Along with this, fundamental constraints for achieving low latency are identified followed by a general overview of exemplary 5G networks. Besides that, a loop-free, low latency and local-decision based routing protocol is derived in the context of smart grid. This approach ensures low latency and reliable data communication for stationary devices. To address data security in wireless communication, we introduce a geo-location based data encryption, along with node authentication by k-nearest neighbor algorithm. In the second approach, node authentication by the support vector machine, along with public-private key management, is proposed. Both approaches ensure data security without increasing the packet overhead compared to the existing approaches

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&

The Interplay between Computation and Communication

In this thesis, a comprehensive exploration into the integration of communication and learning within the massive Internet of Things (mIoT) is undertaken. Addressing one of the fundamental challenges of mIoT, where traditional channel estimation methods prove inefficient due to high device density and short packets; initially, a novel approach leveraging unsupervised machine learning for joint channel estimation and signal detection is proposed. This technique utilizes the Gaussian mixture model (GMM) clustering of received signals, thereby reducing the necessity for exhaustive channel estimation, decreasing the number of required pilot symbols, and enhancing symbol error rate (SER) performance. Building on this foundation, an innovative method is proposed that eliminates the need for pilot symbols entirely. By coupling GMM clustering with rotational invariant (RI) coding, the model maintains robust performance against the effects of channel rotation, thereby improving the efficiency of mIoT systems. This research delves further into integrating communication and learning in mIoT, specifically focusing on federated learning (FL) convergence under error-prone conditions. It carefully analyzes the impact of factors like block length, coding rate, and signal-to-noise ratio on FL's accuracy and convergence. A novel approach is proposed to address communication error challenges, where the base station (BS) uses memory to cache key parameters. Closing the thesis, an extensive simulation of a real-world mIoT system, integrating previously developed techniques, such as the innovative channel estimation method, RI coding, and the introduced FL model. It notably demonstrates that optimal learning outcomes can be achieved even without stringent communication reliability. Thus, this work not only achieves comparable or superior performance to traditional methods with fewer pilot symbols but also provides valuable insights for optimizing mIoT systems within the FL framework

Indoor Visible Light Communication:A Tutorial and Survey

Abstract With the advancement of solid-state devices for lighting, illumination is on the verge of being completely restructured. This revolution comes with numerous advantages and viable opportunities that can transform the world of wireless communications for the better. Solid-state LEDs are rapidly replacing the contemporary incandescent and fluorescent lamps. In addition to their high energy efficiency, LEDs are desirable for their low heat generation, long lifespan, and their capability to switch on and off at an extremely high rate. The ability of switching between different levels of luminous intensity at such a rate has enabled the inception of a new communication technology referred to as visible light communication (VLC). With this technology, the LED lamps are additionally being used for data transmission. This paper provides a tutorial and a survey of VLC in terms of the design, development, and evaluation techniques as well as current challenges and their envisioned solutions. The focus of this paper is mainly directed towards an indoor setup. An overview of VLC, theory of illumination, system receivers, system architecture, and ongoing developments are provided. We further provide some baseline simulation results to give a technical background on the performance of VLC systems. Moreover, we provide the potential of incorporating VLC techniques in the current and upcoming technologies such as fifth-generation (5G), beyond fifth-generation (B5G) wireless communication trends including sixth-generation (6G), and intelligent reflective surfaces (IRSs) among others

Advanced receivers for distributed cooperation in mobile ad hoc networks

Mobile ad hoc networks (MANETs) are rapidly deployable wireless communications systems, operating with minimal coordination in order to avoid spectral efficiency losses caused by overhead. Cooperative transmission schemes are attractive for MANETs, but the distributed nature of such protocols comes with an increased level of interference, whose impact is further amplified by the need to push the limits of energy and spectral efficiency. Hence, the impact of interference has to be mitigated through with the use PHY layer signal processing algorithms with reasonable computational complexity. Recent advances in iterative digital receiver design techniques exploit approximate Bayesian inference and derivative message passing techniques to improve the capabilities of well-established turbo detectors. In particular, expectation propagation (EP) is a flexible technique which offers attractive complexity-performance trade-offs in situations where conventional belief propagation is limited by computational complexity. Moreover, thanks to emerging techniques in deep learning, such iterative structures are cast into deep detection networks, where learning the algorithmic hyper-parameters further improves receiver performance. In this thesis, EP-based finite-impulse response decision feedback equalizers are designed, and they achieve significant improvements, especially in high spectral efficiency applications, over more conventional turbo-equalization techniques, while having the advantage of being asymptotically predictable. A framework for designing frequency-domain EP-based receivers is proposed, in order to obtain detection architectures with low computational complexity. This framework is theoretically and numerically analysed with a focus on channel equalization, and then it is also extended to handle detection for time-varying channels and multiple-antenna systems. The design of multiple-user detectors and the impact of channel estimation are also explored to understand the capabilities and limits of this framework. Finally, a finite-length performance prediction method is presented for carrying out link abstraction for the EP-based frequency domain equalizer. The impact of accurate physical layer modelling is evaluated in the context of cooperative broadcasting in tactical MANETs, thanks to a flexible MAC-level simulato