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) ...

The fifth-generation (5G) is in its business version, and researchers have started to look at the potential technologies to be employed in the next generation. In this context, intelligent reflecting surface (IRS) is a promising technology for the sixth-generation (6G) of wireless systems by introducing the smart radio environment concept. The promised gains of IRS-assisted communications depend on the accuracy of the channel state information. Using a tensor framework, particularly tensor decomposition, we propose different solutions to solve the channel estimation problem for different scenarios. We firstly address the receiver design for an IRS-assisted multiple-input multiple-output (MIMO) communication system via a tensor modeling approach to solve the channel estimation problem using supervised (pilot-assisted) methods. Considering a structured time-domain pattern of pilots and IRS phase shifts, we present two channel estimation methods that rely on a parallel factors (PARAFAC) tensor modeling ...

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: ...

Wireless Terabits per second (Tbps) link is needed for the new emerging data-hungry applications in Beyond 5G (B5G) (e.g., high capacity broadband, enhanced hotspot, 3D extended reality, etc.). The sub-GHz bands are scarce and overused, while the considered millimeter Wave bands in 5G are insufficient to reach the desired ultra-high throughput. Thus, the sub-THz/THz bands are envisaged as the next frontier for B5G wireless communication. Even though a wider bandwidth and large-scale MIMO are envisioned at sub-THz bands, but the system and waveform design should consider the channel characteristics, technological limitations, and high RF impairments. Based on these challenges, we proposed to use an energy-efficient low order single carrier modulation accompanied by spectral-efficient Index Modulation (IM) with advanced MIMO techniques In the first part of this thesis, the spectral-efficient MIMO Spatial Multiplexing (SMX) and Generalized Spatial Modulation (GSM), that generalizes ...

With the rapid development of the internet of things (IoT) and the popularization of mobile internet applications, the location-based service (LBS) has attracted much attention due to its commercial, military, and social applications. The global positioning system (GPS) is the prominent and most widely used technology that provides localization and navigation services for outdoor location information. However, the GPS cannot be used well in indoor environments due to weak signal reception, radio multi-path effect, signal scattering, and attenuation. Therefore, localization-based systems for indoor environments have been designed using various wireless communication technologies such as Wi-Fi, ZigBee, Bluetooth, UWB, etc., depending on the context and application scenarios. Received signal strength indicator (RSSI) technology has been extensively used in indoor localization technology due to it provides accuracy, high feasibility, simplicity, and deployment practicability features. Various machine learning algorithms have been employed to ...

The wireless communication industry has seen a tremendous growth in the last few decades. The ever increasing demand to stay connected at home, work, and on the move, with voice and data applications, has continued the need for more sophisticated end-user devices. A typical smart communication device these days consists of a radio system that can access a mixture of mobile cellular services (GSM, UMTS, etc), indoor wireless broadband services (WLAN-802.11b/g/n), short range and low energy personal communications (Bluetooth), positioning and navigation systems (GPS), etc. A smart device capable of meeting all these requirements has to be highly flexible and should be able to reconfigure radio transmitters and receivers as and when required. Further, the radio modules used in these devices should be extremely small so that the device itself is portable. In addition, the device should also be economical ...

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 ...

During the past two decades, image compression has developed from a mostly academic Rate-Distortion (R-D) field, into a highly commercial business. Various lossless and lossy image coding techniques have been developed. This thesis represents an interdisciplinary work between the field of astronomy and digital image processing and brings new aspects into both of the fields. In fact, image compression had its beginning in an American space program for efficient data storage. The goal of this research work is to recognize and develop new methods for space observatories and software tools to incorporate compression in space astronomy standards. While the astronomers benefit from new objective processing and analysis methods and improved efficiency and quality, for technicians a new field of application and research is opened. For validation of the processing results, the case of InfraRed (IR) astronomy has been specifically analyzed. ...

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 ...

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 ...

Facing the diversity of communication needs of 5G networks and the future 6G, resource allocation is considered as a key enabler to increase the number of devices, the data rate or the reliability of the communication links. In machine-type communications networks, recent work has proposed to adapt the temporal resource allocation as a function of the underlying process driving the activity of the devices. This thesis firstly focuses on the impact of having only limited knowledge of the underlying process, and proposes methods to mitigate the bias induced by the lack of knowledge. Secondly, an algorithm for the joint optimization of the temporal resource allocation and the transmit power of the devices is proposed. The algorithm ensures that devices that are likely to transmit on the same resources do so with a sufficient power diversity to ensure their decodability by ...

We are in the middle of the digital era, with more and more amazing features becoming available even in entry-level consumer devices (smartphones, tablets, wearable devices such as smart watches, etc.). This pervasive almost ubiquitous availability of interconnected devices, unconceivable until only a decade ago, is opening the doors to unprecedented applications, for which location awareness is an essential need. Unfortunately, none of the current positioning technologies alone is able to provide anywhere and anytime location capabilities, that is, to ensure service coverage in heterogeneous environments (e.g., outdoor, indoor) while offering adequate positioning accuracy. In response to such demand, this thesis investigates novel localization algorithms that can offer ubiquitous positioning capabilities. For this purpose, a novel holistic framework is proposed, based on the combined use of four dimensions of design, each focusing on a specific aspect of the whole localization ...

The thriving development of wireless communications calls for innovative and advanced signal processing techniques targeting at an enhanced performance in terms of reliability, throughput, robustness, efficiency, flexibility, etc.. This thesis addresses such a compelling demand and presents new and intriguing progress towards fulfilling it. We mainly concentrate on two advanced multi-dimensional signal processing challenges for wireless systems that have attracted tremendous research attention in recent years, multi-carrier Multiple-Input Multiple-Output (MIMO) systems and multi-dimensional harmonic retrieval. As the key technologies of wireless communications, the numerous benefits of MIMO and multi-carrier modulation, e.g., boosting the data rate and improving the link reliability, have long been identified and have ignited great research interest. In particular, the Orthogonal Frequency Division Multiplexing (OFDM)-based multi-user MIMO downlink with Space-Division Multiple Access (SDMA) combines the twofold advantages of MIMO and multi-carrier modulation. It is the essential element ...

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 ...

This dissertation deals with the distributed processing techniques for parameter estimation and efficient data-gathering in wireless communication and sensor networks. The estimation problem consists in inferring a set of parameters from temporal and spatial noisy observations collected by different nodes that monitor an area or field. The objective is to derive an estimate that is as accurate as the one that would be obtained if each node had access to the information across the entire network. With the aim of enabling an energy aware and low-complexity distributed implementation of the estimation task, several useful optimization techniques that generally yield linear estimators were derived in the literature. Up to now, most of the works considered that the nodes are interested in estimating the same vector of global parameters. This scenario can be viewed as a special case of a more general ...