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 ...

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 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 ...

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. ...

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 ...

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 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 ...

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 ...

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 ...

Massive multiple input multiple output (MIMO) is a promising 5G and beyond-5G wireless access technology that can provide huge throughput, compared with the current technology, in order to satisfy some requirements for the future generations of wireless networks. The research described in this thesis proposes the design of some applications of the massive MIMO technology that can be implemented in order to increase the spectral efficiency per cell of the future wireless networks through a simple and low complexity signal processing. In particular, massive MIMO is studied in conjunction with two other topics that are currently under investigation for the future wireless systems, both in academia and in industry: the millimeter wave frequencies and the distributed antenna systems. The first part of the thesis gives a brief overview on the requirements of the future wireless networks and it explains some ...

Over the last few decades, transistor miniaturization has enabled a tremendous increase in the processing capability of commercial electronic devices, which, combined with the reduction of production costs, has tremendously fostered the usage of the Information and Communications Technologies (ICTs) both in terms of number of users and required data rates. In turn, this has led to a tremendous increment in the energetic demand of the ICT sector, which is expected to further grow during the upcoming years, reaching unsustainable levels of greenhouse gas emissions as reported by the European Council. Additionally, the autonomy of battery operated devices is getting reduced year after year since battery technology has not evolved fast enough to cope with the increase of energy consumption associated to the growth of the node’s processing capability. Energy harvesting, which is known as the process of collecting energy ...

In the next era of communications, where heterogeneous, asynchronous and ultra-low latency networks are drawn on the horizon, classical cryptography might be inadequate due to the excessive cost of maintaining a public-key infrastructure and the high computational capacity required in the devices. Moreover, it is becoming increasingly difficult to guarantee that the computational capacity of adversaries would not be able to break the cryptograms. Consequently, information-theoretic security, and particularly its application to keyless secrecy communication, might play an important role in the future development of these systems. The notion of secrecy in this case does not rely on any assumption of the computational power of eavesdroppers, and is based instead on guaranteeing statistical independence between the information message and the observed cryptogram. This is possible by constructing channel codes that exploit the noisy behavior of the channels involved in the ...

Multisensor systems are a key enabling technology in, e.g., radar, sonar, medical ultrasound, and wireless communications. Using multiple sensors provides spatial selectivity, improves the signal-to-noise ratio, and enables rejecting unwanted interference. Conventional multisensor systems employ a simple array of uniformly spaced sensors with a linear or rectangular geometry. However, a uniform array spanning a large electrical aperture may become prohibitively expensive, as many sensors and costly RF-IF front ends are needed. In contrast, sparse sensor arrays require drastically fewer resources to achieve comparable performance in terms of spatial resolution and the number of identifiable scatterers or sources. This is facilitated by the co-array: a virtual array structure consisting of the pairwise differences or sums of physical sensor positions. Most recent works on co-array-based sparse array design focus exclusively on passive sensing. Active sensing, where sensors transmit signals and observe their ...

In modern devices which provide hands-free speech capturing functionality, such as hands-free communication kits and voice-controlled devices, the received speech signal at the microphones is corrupted by background noise, interfering speech signals, and room reverberation. In many practical situations, the microphones are not necessarily located near the desired source, and hence, the ratio of the desired speech power to the power of the background noise, the interfering speech, and the reverberation at the microphones can be very low, often around or even below 0 dB. In such situations, the comfort of human-to-human communication, as well as the accuracy of automatic speech recognisers for voice-controlled applications can be signi cantly degraded. Therefore, e ffective speech enhancement algorithms are required to process the microphone signals before transmitting them to the far-end side for communication, or before feeding them into a speech recognition ...