The proliferation of technologies like Internet of Things (IoT) and Industrial IoT (IIoT) has led to rapid growth in the number of connected devices and the volume of data associated with IoT applications. It is expected that more than 125 billion IoT devices will be connected to the Internet by 2030. With the plethora of wireless IoT devices, we are moving towards the connected world which is the guiding principle for the IoT. The next generation of IoT network should be capable of interconnecting heterogeneous IoT sensor or devices for effective Device-to-Device (D2D), Machine-to-Machine (M2M) communications as well as facilitating various IoT services and applications. Therefore, the next generation of IoT networks is expected to meet the capacity demand of such a network of billions of IoT devices. The current underlying wireless network is based on Orthogonal Multiple Access (OMA) ...

Reliable transport systems, defense, public safety and quality assurance in the Industry 4.0 are essential in a modern society. In a mission-critical scenario, a mission failure would jeopardize human lives and put at risk some other assets whose impairment or loss would significantly harm society or business results. Even small degradations of the communications supporting the mission could have large and possibly dire consequences. On the one hand, mission-critical organizations wish to utilize the most modern, disruptive and innovative communication systems and technologies, and yet, on the other hand, need to comply with strict requirements, which are very different to those of non critical scenarios. The aim of this thesis is to assess the feasibility of applying emerging technologies like Internet of Things (IoT), Cyber-Physical Systems (CPS) and 4G broadband communications in mission-critical scenarios along three key critical infrastructure sectors: ...

Distributed signal or information processing using Internet of Things (IoT), facilitates real-time monitoring of signals, for example, environmental pollutants, health indicators, and electric energy consumption in a smart city. Despite the promising capabilities of IoTs, these distributed deployments often face the challenge of data privacy and communication rate constraints. In traditional machine learning, training data is moved to a data center, which requires massive data movement from distributed IoT devices to a third-party location, thus raising concerns over privacy and inefficient use of communication resources. Moreover, the growing network size, model size, and data volume combined lead to unusual complexity in the design of optimization algorithms beyond the compute capability of a single device. This necessitates novel system architectures to ensure stable and secure operations of such networks. Federated learning (FL) architecture, a novel distributed learning paradigm introduced by McMahan ...

The widespread use of mobile internet and smart applications has led to an explosive growth in mobile data traffic. With the rise of smart homes, smart buildings, and smart cities, this demand is ever growing since future communication systems will require the integration of multiple networks serving diverse sectors, domains and applications, such as multimedia, virtual or augmented reality, machine-to-machine (M2M) communication / the Internet of things (IoT), automotive applications, and many more. Therefore, in the future, the communication systems will not only be required to provide Gbps wireless connectivity but also fulfill other requirements such as low latency and massive machine type connectivity while ensuring the quality of service. Without significant technological advances to increase the system capacity, the existing telecommunications infrastructure will be unable to support these multi-dimensional requirements. This poses an important demand for suitable waveforms with ...

Position information is important for various applications, including location-aware communications, autonomous driving, industrial internet of things (IoT). Geometry-based techniques such as time-of-arrival (TOA), time-difference-of-arrival (TDOA), and angle-of-arrival (AOA) are widely used and can be formed as optimization problems. In order to solve these optimization problems efficiently, stochastic optimization methods are discussed in this work in solving target positioning problems and tackling key issues in location-based applications. Firstly, the direction of arrival (DOA) estimation problem is studied in this work. Grid search is useful in the algorithms such as maximum likelihood estimator (MLE), MUltiple SIgnal Classification (MUSIC), etc. However, the computational cost is the main drawback. To speed up the search procedure, we implement random ferns to extract the features from the beampatterns of different DOAs and use these features to identify potential angle candidates. Then, we propose an ultrasonic air-writing ...

Data acquisition is a necessary first step in digital signal processing applications such as radar, wireless communications and array processing. Traditionally, this process is performed by uniformly sampling signals at a frequency above the Nyquist rate and converting the resulting samples into digital numeric values through high-resolution amplitude quantization. While the traditional approach to data acquisition is straightforward and extremely well-proven, it may be either impractical or impossible in many modern applications due to the existing fundamental trade-off between sampling rate, amplitude quantization precision, implementation costs, and usage of physical resources, e.g. bandwidth and power consumption. Motivated by this fact, system designers have recently proposed exploiting sparse and few-bit quantized sampling instead of the traditional way of data acquisition in order to reduce implementation costs and usage of physical resources in such applications. However, before transition from the tradition data ...

Indoor location has experienced a major boost in recent years. location based services (LBS), which until recently were restricted to outdoor scenarios and the use of GPS, have also been extended into buildings. From large public structures such as airports or hospitals to a multitude of industrial scenarios, LBS has become increasingly present in indoor scenarios. Of the various technologies that can be used to achieve this indoor location, the ones based on ultra- wideband (UWB) signals have become ones of the most demanded due primarily to their accuracy in position estimation. Additionally, the appearance in the market of more and more manufacturers and products has lowered the prices of these devices to levels that allow to think about their use for large deployments with a contained budget. By their nature, UWB signals are very resistant to the multi-path phenomenon, ...

This dissertation explores the intersection of data fusion, federated learning, and Bayesian methods, with a focus on their applications in indoor localization, GNSS, and image processing. Data fusion involves integrating data and knowledge from multiple sources. It becomes essential when data is only available in a distributed fashion or when different sensors are used to infer a quantity of interest. Data fusion typically includes raw data fusion, feature fusion, and decision fusion. In this thesis, we will concentrate on feature fusion. Distributed data fusion involves merging sensor data from different sources to estimate an unknown process. Bayesian framework is often used because it can provide an optimal and explainable feature by preserving the full distribution of the unknown given the data, called posterior, over the estimated process at each agent. This allows for easy and recursive merging of sensor data ...

While 5G wireless systems offer significant enhancements to their 4G counterparts in terms of bandwidth, connectivity, latency, etc. they are unable to meet the requirements of the applications envisioned for the next decade. The demands of applications such as super-smart city, autonomous vehicles, smart health-care, etc. are much greater than what 5G systems can afford. This means that we cannot yet expect the widespread realization of IoT/IoE and have to wait for 6G to finally fulfill this long-awaited promise. As an enabler and a key player for the success of IoT/IoE, WPCN has been the center of attention in the past decade and attracted a large number of journal and conference publications. Despite the extensive efforts in this area, WPCN still lacks the required performance for being seamlessly fitted into the next generation IoT/IoE environments. The main objective of this ...

Future wireless networks will evolve to integrate communication, localization, and sensing capabilities. This evolution is driven by emerging application platforms such as digital twins, on the one hand, and advancements in wireless technologies, on the other, characterized by increased bandwidths, more antennas, and enhanced computational power. Crucial to this development is the application of artificial intelligence (AI), which is set to harness the vast amounts of available data in the sixth-generation (6G) of mobile networks and beyond. Integrating AI and machine learning (ML) algorithms, in particular, with wireless localization offers substantial opportunities to refine communication systems, improve the ability of wireless networks to locate the users precisely, enable context-aware transmission, and utilize processing and energy resources more efficiently. In this dissertation, advanced ML algorithms for enhanced wireless localization are proposed. Motivated by the capabilities of deep neural networks (DNNs) and ...

Global Navigation Satellite Systems (GNSS) signals are broadly used for positioning, navigation and timing (PNT) in many different applications and use cases. Although different PNT technologies are available, GNSS is expected to be a key player in the derivation of positioning and timing for many future applications, including those in the context of the Internet of Things (IoT) or autonomous vehicles, since it has the important advantage of being open access and worldwide available. Indeed, GNSS is performing very well in mild propagation conditions, achieving position and time synchronization accuracies down to the cm and ns levels, respectively. Nevertheless, the exploitation of GNSS in harsh propagation conditions typical of urban and indoor scenarios is very challenging, resulting in position errors of up to tens or even hundreds of meters, and timing accuracies of hundreds of ns. This thesis deals with ...

The level of automation in our society is ever increasing. Technologies like self-driving cars, virtual reality, and fully autonomous robots, which all were unimaginable a few decades ago, are realizable today, and will become standard consumer products in the future. These technologies depend upon autonomous localization and situation awareness where careful processing of sensory data is required. To increase efficiency, robustness and reliability, appropriate models for these data are needed. In this thesis, such models are analyzed within three different application areas, namely (1) magnetic localization, (2) extended target tracking, and (3) autonomous learning from raw pixel information. Magnetic localization is based on one or more magnetometers measuring the induced magnetic field from magnetic objects. In this thesis we present a model for determining the position and the orientation of small magnets with an accuracy of a few millimeters. This ...

In multiple-input multiple-output (MIMO) communications systems, the channel state information (CSI) is typically estimated at the receiver side by sending a few known pilots and then used for decoding at the receiver and/or for precoding at the transmitter. These are known as coherent schemes. However, in scenarios dominated by fast fading or massive MIMO systems dedicated to ultra-reliable low-latency communications (URLLC), getting an accurate channel estimate would require pilots to occupy a disproportionate fraction of communication resources. This becomes also a problem in machine-to-machine (M2M) communications that arise in the so-called Internet of Things (IoT). The advent of 5G and beyond (B5G) systems has introduced these novel scenarios that underscore the need for noncoherent communications schemes in which neither the transmitter nor the receiver has any knowledge about the instantaneous CSI. The Grassmannian and Stiefel manifolds play a significant role ...

In a future perspective, the need of mapping an unknown indoor environment, of localizing and retrieving information from objects with zero costs and efforts could be satisfied by the adoption of next 5G technologies. Thanks to the mix of mmW and massive arrays technologies, it will be possible to achieve a higher indoor localization accuracy without relying on a dedicated infrastructure for localization but exploiting that designed for communication purposes. Besides users’ localization and navigation objectives, mapping and thus, the capability of reconstructing indoor scenarios, will be an important field of research with the possibility of sharing environmental information via crowd-sourcing mechanisms between users. Finally, in the Internet of Things vision, it is expected that people, objects and devices will be interconnected to each other with the possibility of exchanging the acquired and estimated data including those regarding objects identification, ...

Over the last few years multimedia and data-based services experienced a non-stopping growth. Unlike before, people do not use the services only from a static location, but they are continuously on the move between different scenarios, using their mobile devices to access data-based services. In parallel, commuter traffic from rural areas is also rising, since most of work places are in and around cities. During transportation, people intensively employ mobile devices to work, access to social networks, or as an entertainment means. Internet access is required for most of these services. Currently, GSM for Railways (GSM-R), which is based on the Global System for Mobile Communications (GSM), is the most widely used communication system between trains and the elements involved in operation, control, and intercommunication within the railway infrastructure. However, GSM-R is not well suited for supporting advanced services such ...