Spectrum Sensing Algorithms for Cognitive Radio Applications

Future wireless communications systems are expected to be extremely dynamic, smart and capable to interact with the surrounding radio environment. To implement such advanced devices, cognitive radio (CR) is a promising paradigm, focusing on strategies for acquiring information and learning. The first task of a cognitive systems is spectrum sensing, that has been mainly studied in the context of opportunistic spectrum access, in which cognitive nodes must implement signal detection techniques to identify unused bands for transmission. In the present work, we study different spectrum sensing algorithms, focusing on their statistical description and evaluation of the detection performance. Moving from traditional sensing approaches we consider the presence of practical impairments, and analyze algorithm design. Far from the ambition of cover the broad spectrum of spectrum sensing, we aim at providing contributions to the main classes of sensing techniques. In particular, in the context of energy detection we studied the practical design of the test, considering the case in which the noise power is estimated at the receiver. This analysis allows to deepen the phenomenon of the SNR wall, providing the conditions for its existence and showing that presence of the SNR wall is determined by the accuracy of the noise power estimation process. In the context of the eigenvalue based detectors, that can be adopted by multiple sensors systems, we studied the practical situation in presence of unbalances in the noise power at the receivers. Then, we shift the focus from single band detectors to wideband sensing, proposing a new approach based on information theoretic criteria. This technique is blind and, requiring no threshold setting, can be adopted even if the statistical distribution of the observed data in not known exactly. In the last part of the thesis we analyze some simple cooperative localization techniques based on weighted centroid strategies.

Wireless Network Analytics for Next Generation Spectrum Awareness

The importance of networks, in their broad sense, is rapidly and massively growing in modern-day society thanks to unprecedented communication capabilities offered by technology. In this context, the radio spectrum will be a primary resource to be preserved and not wasted. Therefore, the need for intelligent and automatic systems for in-depth spectrum analysis and monitoring will pave the way for a new set of opportunities and potential challenges. This thesis proposes a novel framework for automatic spectrum patrolling and the extraction of wireless network analytics. It aims to enhance the physical layer security of next generation wireless networks through the extraction and the analysis of dedicated analytical features. The framework consists of a spectrum sensing phase, carried out by a patrol composed of numerous radio-frequency (RF) sensing devices, followed by the extraction of a set of wireless network analytics. The methodology developed is blind, allowing spectrum sensing and analytics extraction of a network whose key features (i.e., number of nodes, physical layer signals, medium access protocol (MAC) and routing protocols) are unknown. Because of the wireless medium, over-the-air signals captured by the sensors are mixed; therefore, blind source separation (BSS) and measurement association are used to estimate the number of sources and separate the traffic patterns. After the separation, we put together a set of methodologies for extracting useful features of the wireless network, i.e., its logical topology, the application-level traffic patterns generated by the nodes, and their position. The whole framework is validated on an ad-hoc wireless network accounting for MAC protocol, packet collisions, nodes mobility, the spatial density of sensors, and channel impairments, such as path-loss, shadowing, and noise. The numerical results obtained by extensive and exhaustive simulations show that the proposed framework is consistent and can achieve the required performance.

Cognitive radio applications for enhanced spectrum efficiency

The aim of this thesis is the study of techniques for efficient management and use of the spectrum based on cognitive radio technology. The ability of cognitive radio technologies to adapt to the real-time conditions of its operating environment, offers the potential for more flexible use of the available spectrum. In this context, the international interest is particularly focused on the “white spaces” in the UHF band of digital terrestrial television. Spectrum sensing and geo-location database have been considered in order to obtain information on the electromagnetic environment. Different methodologies have been considered in order to investigate spectral resources potentially available for the white space devices in the TV band. The adopted methodologies are based on the geo-location database approach used either in autonomous operation or in combination with sensing techniques. A novel and computationally efficient methodology for the calculation of the maximum permitted white space device EIRP is then proposed. The methodology is suitable for implementation in TV white space databases. Different Italian scenarios are analyzed in order to identify both the available spectrum and the white space device emission limits. Finally two different applications of cognitive radio technology are considered. The first considered application is the emergency management. The attention is focused on the consideration of both cognitive and autonomic networking approaches when deploying an emergency management system. The cognitive technology is then considered in applications related to satellite systems. In particular a hybrid cognitive satellite-terrestrial is introduced and an analysis of coexistence between terrestrial and satellite networks by considering a cognitive approach is performed.

Dynamic power management: from portable devices to high performance computing

Electronic applications are nowadays converging under the umbrella of the cloud computing vision. The future ecosystem of information and communication technology is going to integrate clouds of portable clients and embedded devices exchanging information, through the internet layer, with processing clusters of servers, data-centers and high performance computing systems. Even thus the whole society is waiting to embrace this revolution, there is a backside of the story. Portable devices require battery to work far from the power plugs and their storage capacity does not scale as the increasing power requirement does. At the other end processing clusters, such as data-centers and server farms, are build upon the integration of thousands multiprocessors. For each of them during the last decade the technology scaling has produced a dramatic increase in power density with significant spatial and temporal variability. This leads to power and temperature hot-spots, which may cause non-uniform ageing and accelerated chip failure. Nonetheless all the heat removed from the silicon translates in high cooling costs. Moreover trend in ICT carbon footprint shows that run-time power consumption of the all spectrum of devices accounts for a significant slice of entire world carbon emissions. This thesis work embrace the full ICT ecosystem and dynamic power consumption concerns by describing a set of new and promising system levels resource management techniques to reduce the power consumption and related issues for two corner cases: Mobile Devices and High Performance Computing.

Spectrum Management and Cognitive Radios

The ever increasing demand for new services from users who want high-quality broadband services while on the move, is straining the efficiency of current spectrum allocation paradigms, leading to an overall feeling of spectrum scarcity. In order to circumvent this problem, two possible solutions are being investigated: (i) implementing new technologies capable of accessing the temporarily/locally unused bands, without interfering with the licensed services, like Cognitive Radios; (ii) release some spectrum bands thanks to new services providing higher spectral efficiency, e.g., DVB-T, and allocate them to new wireless systems. These two approaches are promising, but also pose novel coexistence and interference management challenges to deal with. In particular, the deployment of devices such as Cognitive Radio, characterized by the inherent unplanned, irregular and random locations of the network nodes, require advanced mathematical techniques in order to explicitly model their spatial distribution. In such context, the system performance and optimization are strongly dependent on this spatial configuration. On the other hand, allocating some released spectrum bands to other wireless services poses severe coexistence issues with all the pre-existing services on the same or adjacent spectrum bands. In this thesis, these methodologies for better spectrum usage are investigated. In particular, using Stochastic Geometry theory, a novel mathematical framework is introduced for cognitive networks, providing a closed-form expression for coverage probability and a single-integral form for average downlink rate and Average Symbol Error Probability. Then, focusing on more regulatory aspects, interference challenges between DVB-T and LTE systems are analysed proposing a versatile methodology for their proper coexistence. Moreover, the studies performed inside the CEPT SE43 working group on the amount of spectrum potentially available to Cognitive Radios and an analysis of the Hidden Node problem are provided. Finally, a study on the extension of cognitive technologies to Hybrid Satellite Terrestrial Systems is proposed.

Development and performance assessment of a Plasma Focus electron beam generator for Intra-Operative Radiation Therapy

The Plasma Focus is a device designed to generate a plasma sheet between two coaxial electrodes by means of a high voltage difference. The plasma is then driven to collapse into a “pinch”, where thermonuclear conditions prevail. During the “pinch phase” charged particles are emitted, with two main components: an ion beam peaked forward and an electron beam directed backward. The electron beam emitted backward by Plasma Focus devices is being investigated as a radiation source for medical applications, using it to produce x-rays by interaction with appropriate targets (through bremsstrahlung and characteristic emission). A dedicated Plasma Focus device, named PFMA-3 (Plasma Focus for Medical Applications number 3), has been designed, put in operation and tested by the research groups of the Universities of Bologna and Ferrara. The very high dose rate (several gray per discharge, in less than 1 µs) is a peculiarity of this device that has to be investigated, as it might modify the relative biological effectiveness (RBE). Aim of this Ph.D. project was to investigate the main physical properties of the low-energy x-ray beams produced by a Plasma Focus device and their potential medical applications to IORT treatments. It was necessary to develop the optimal geometrical configuration; to evaluate the x-rays produced and their dose deposited; to estimate the energy electron spectrum produced in the “pinch phase”; to study an optimal target for the conversion of the x-rays; to conduct simulations to study the physics involved; and in order to evaluate the radio-biological features of the beam, cell holders had to be developed for both irradiations and cell growth conditions.

Antipodal random sequences with prescribed second-order statistics: application to Compressive Sensing and UWB system based on DS-CDMA

It is usual to hear a strange short sentence: «Random is better than...». Why is randomness a good solution to a certain engineering problem? There are many possible answers, and all of them are related to the considered topic. In this thesis I will discuss about two crucial topics that take advantage by randomizing some waveforms involved in signals manipulations. In particular, advantages are guaranteed by shaping the second order statistic of antipodal sequences involved in an intermediate signal processing stages. The first topic is in the area of analog-to-digital conversion, and it is named Compressive Sensing (CS). CS is a novel paradigm in signal processing that tries to merge signal acquisition and compression at the same time. Consequently it allows to direct acquire a signal in a compressed form. In this thesis, after an ample description of the CS methodology and its related architectures, I will present a new approach that tries to achieve high compression by design the second order statistics of a set of additional waveforms involved in the signal acquisition/compression stage. The second topic addressed in this thesis is in the area of communication system, in particular I focused the attention on ultra-wideband (UWB) systems. An option to produce and decode UWB signals is direct-sequence spreading with multiple access based on code division (DS-CDMA). Focusing on this methodology, I will address the coexistence of a DS-CDMA system with a narrowband interferer. To do so, I minimize the joint effect of both multiple access (MAI) and narrowband (NBI) interference on a simple matched filter receiver. I will show that, when spreading sequence statistical properties are suitably designed, performance improvements are possible with respect to a system exploiting chaos-based sequences minimizing MAI only.

Pervasive Sensing in Future Networks

Pervasive Sensing is a recent research trend that aims at providing widespread computing and sensing capabilities to enable the creation of smart environments that can sense, process, and act by considering input coming from both people and devices. The capabilities necessary for Pervasive Sensing are nowadays available on a plethora of devices, from embedded devices to PCs and smartphones. The wide availability of new devices and the large amount of data they can access enable a wide range of novel services in different areas, spanning from simple data collection systems to socially-aware collaborative filtering. However, the strong heterogeneity and unreliability of devices and sensors poses significant challenges. So far, existing works on Pervasive Sensing have focused only on limited portions of the whole stack of available devices and data that they can use, to propose and develop mainly vertical solutions. The push from academia and industry for this kind of services shows that time is mature for a more general support framework for Pervasive Sensing solutions able to enhance frail architectures, promote a well balanced usage of resources on different devices, and enable the widest possible access to sensed data, while ensuring a minimal energy consumption on battery-operated devices. This thesis focuses on pervasive sensing systems to extract design guidelines as foundation of a comprehensive reference model for multi-tier Pervasive Sensing applications. The validity of the proposed model is tested in five different scenarios that present peculiar and different requirements, and different hardware and sensors. The ease of mapping from the proposed logical model to the real implementations and the positive performance result campaigns prove the quality of the proposed approach and offer a reliable reference model, together with a direction for the design and deployment of future Pervasive Sensing applications.

Transport Properties and Novel Sensing Applications of Organic Semiconducting Crystals

The present thesis is focused on the study of Organic Semiconducting Single Crystals (OSSCs) and crystalline thin films. In particular solution-grown OSSC, e.g. 4-hdroxycyanobenzene (4HCB) have been characterized in view of their applications as novel sensors of X-rays, gamma-rays, alpha particles radiations and chemical sensors. In the field of ionizing radiation detection, organic semiconductors have been proposed so far mainly as indirect detectors, i.e. as scintillators or as photodiodes. I first study the performance of 4HCB single crystals as direct X-ray detector i.e. the direct photon conversion into an electrical signal, assessing that they can operate at room temperature and in atmosphere, showing a stable and linear response with increasing dose rate. A dedicated study of the collecting electrodes geometry, crystal thickness and interaction volume allowed us to maximize the charge collection efficiency and sensitivity, thus assessing how OSSCs perform at low operating voltages and offer a great potential in the development of novel ionizing radiation sensors. To better understand the processes generating the observed X-ray signal, a comparative study is presented on OSSCs based on several small-molecules: 1,5-dinitronaphthalene (DNN), 1,8-naphthaleneimide (NTI), Rubrene and TIPS-pentacene. In addition, the proof of principle of gamma-rays and alpha particles has been assessed for 4HCB single crystals. I have also carried out an investigation of the electrical response of OSSCs exposed to vapour of volatile molecules, polar and non-polar. The last chapter deals with rubrene, the highest performing molecular crystals for electronic applications. We present an investigation on high quality, millimeter-sized, crystalline thin films (10 – 100 nm thick) realized by exploiting organic molecular beam epitaxy on water-soluble substrates. Space-Charge-Limited Current (SCLC) and photocurrent spectroscopy measurements have been carried out. A thin film transistor was fabricated onto a Cytop® dielectric layer. The FET mobility exceeding 2 cm2/Vs, definitely assess the quality of RUB films.

Study of the calorimetric performance of a liquid Argon detector based on a novel optical imaging concept

DUNE is a next-generation long-baseline neutrino oscillation experiment. It aims to measure the still unknown $ \delta_{CP} $ violation phase and the sign of $ \Delta m_{13}^2 $, which defines the neutrino mass ordering. DUNE will exploit a Far Detector composed of four multi-kiloton LArTPCs, and a Near Detector (ND) complex located close to the neutrino source at Fermilab. The SAND detector at the ND complex is designed to perform on-axis beam monitoring, constrain uncertainties in the oscillation analysis and perform precision neutrino physics measurements. SAND includes a 0.6 T super-conductive magnet, an electromagnetic calorimeter, a 1-ton liquid Argon detector - GRAIN - and a modular, low-density straw tube target tracker system. GRAIN is an innovative LAr detector where neutrino interactions can be reconstructed using only the LAr scintillation light imaged by an optical system based on Coded Aperture masks and lenses - a novel approach never used before in particle physics applications. In this thesis, a first evaluation of GRAIN track reconstruction and calorimetric capabilities was obtained with an optical system based on Coded Aperture cameras. A simulation of $\nu_\mu + Ar$ interactions with the energy spectrum expected at the future Fermilab Long Baseline Neutrino Facility (LBNF) was performed. The performance of SAND was evaluated, combining the information provided by all its sub-detectors, on the selection of $ \nu_\mu + Ar \to \mu^- + p + X $ sample and on the neutrino energy reconstruction.