During the past years wireless communications have been exhibiting an increased growth rendering them the most common way for communication. The continuously increasing demand for wireless services resulted in limited availability of the wireless spectrum. To this end, Cognitive Radio (CR) techniques have been proposed in literature during the past years. The concept of CR approach is to utilize advanced radio and signal-processing technology along with novel spectrum allocation policies to enable new unlicensed wireless users to operate in the existing occupied spectrum areas without degrading the performance of the existing licensed ones. Moreover, the broadcast and fading nature of the wireless channel results in severe degradation on the performance of wireless transmissions. A solution to the problem is the use of multiple-antenna systems so as to achieve spatial diversity. However, in many cases, the communication devices' nature permit the ...

During the last decades, information is being gathered and processed at an explosive rate. This fact gives rise to a very important issue, that is, how to effectively and precisely describe the information content of a given source signal or an ensemble of source signals, such that it can be stored, processed or transmitted by taking into consideration the limitations and capabilities of the several digital devices. One of the fundamental principles of signal processing for decades is the Nyquist-Shannon sampling theorem, which states that the minimum number of samples needed to reconstruct a signal without error is dictated by its bandwidth. However, there are many cases in our everyday life in which sampling at the Nyquist rate results in too many data and thus, demanding an increased processing power, as well as storage requirements. A mathematical theory that emerged ...

Multi-input and multi-output (MIMO) radars achieve high resolution of arrival direction by transmitting orthogonal waveforms, performing matched filtering at the receiver end and then jointly processing the measurements of all receive antennas. This dissertation studies the use of compressive sensing (CS) and matrix completion (MC) techniques as means of reducing the amount of data that need to be collected by a MIMO radar system, without sacrificing the system’s good resolution properties. MIMO radars with sparse sensing are useful in networked radar scenarios, in which the joint processing of the measurements is done at a fusion center, which might be connected to the receive antennas via a wireless link. In such scenarios, reduced amount of data translates into bandwidth and power saving in the receiver-fusion center link. First, we consider previously defined CS-based MIMO radar schemes, and propose optimal transmit antenna ...

Sparse signal recovery consists of algorithms that are able to recover undersampled high dimensional signals accurately. These algorithms require fewer measurements than traditional Shannon/Nyquist sampling theorem demands. Sparse signal recovery has found many applications including magnetic resonance imaging, electromagnetic inverse scattering, radar/sonar imaging, seismic data collection, sensor array processing and channel estimation. The focus of this thesis is on electromagentic inverse scattering problem and joint estimation of the frequency offset and the channel impulse response in OFDM. In the electromagnetic inverse scattering problem, the aim is to find the electromagnetic properties of unknown targets from measured scattered field. The reconstruction of closely placed point-like objects is investigated. The application of the greedy pursuit based sparse recovery methods, OMP and FTB-OMP, is proposed for increasing the reconstruction resolution. The performances of the proposed methods are compared against NESTA and MT-BCS methods. ...

The ever-increasing growth of the wireless application and services affirms the importance of the effective usage of the limited radio spectrum. Existing spectrum management policies have led to significant spectrum under-utilization. Recent measurements showed that large range of the spectrum is sparsely used in both temporal and spatial manner. This conflict between the inefficient usage of the spectrum and the continuous evolution in the wireless communication calls upon the development of more flexible management policies. Cognitive radio (CR) with the dynamic spectrum access (DSA) is considered to be a key technology in making the best solution of this conflict by allowing a group of secondary users (SUs) to share the radio spectrum originally allocated to the primary user (PUs). The operation of CR should not negatively alter the performance of the PUs. Therefore, the interference control along with the highly ...

This dissertation is the combination of three manuscripts –either published in or submitted to journals– on compressive sensing of propeller noise for detection, identification and localization of water crafts. Propeller noise, as a result of rotating blades, is broadband and radiates through water dominating underwater acoustic noise spectrum especially when cavitation develops. Propeller cavitation yields cyclostationary noise which can be modeled by amplitude modulation, i.e., the envelope-carrier product. The envelope consists of the so-called propeller tonals representing propeller characteristics which is used to identify water crafts whereas the carrier is a stationary broadband process. Sampling for propeller noise processing yields large data sizes due to Nyquist rate and multiple sensor deployment. A compressive sensing scheme is proposed for efficient sampling of second-order cyclostationary propeller noise since the spectral correlation function of the amplitude modulation model is sparse as shown in ...

Dynamic spectrum access employing cognitive radios has been proposed, in order to opportunistically use underutilized spectrum portions of a heavily licensed electromagnetic spectrum. Cognitive radios opportunistically share the spectrum, while avoiding any harmful interference to the primary licensed users. One major category of cognitive radios consists of is interweave cognitive radios. In this category, cognitive radios employ spectrum sensing to detect the empty bands of the radio spectrum, also known as spectrum holes. Upon detection of such a spectrum hole, cognitive radios dynamically share this empty band. However, as soon as the primary user appears in the corresponding band, cognitive radios have to vacate the band and look for a new spectrum hole. This way, reliable spectrum sensing becomes a key functionality of a cognitive radio network. The hidden terminal problem and fading effects have been shown to limit the ...

The demand for precious radio spectrum is continuously increasing while the available radio frequency resource has become scarce due to spectrum segmentation and the dedicated frequency allocation of standardized wireless systems. This scarcity has led to the concept of cognitive radio communication which comprises a variety of techniques capable of allowing the coexistence of licensed and unlicensed systems over the same spectrum. In this context, this thesis focuses on interweave and underlay cognitive radio paradigms which are widely considered as important enablers for realising cognitive radio technology. In the interweave paradigm, an unlicensed user explores the spectral holes by means of some spectrum awareness methods and utilizes the available spectral availabilities opportunistically while in the underlay paradigm, an unlicensed user is allowed to coexist with the licensed user only if sufficient protection to the licensed user can be guaranteed. Starting ...

Competition for limited bandwidth, power, and time resources is an intrinsic aspect of multi-user wireless networks. There has been a recent move towards optimizing coexistence and confidentiality at the physical layer of multi-user wireless networks, mainly by exploiting the advanced capabilities of multiple-input multiple-out (MIMO) signal processing methods. Coexistence of disparate networks is made possible via interference mitigation and suppression, and is exemplified by the current interest in cognitive radio (CR) systems. On the other hand, MIMO communications that are secure at the physical layer without depending upon network-layer encryption are achieved by redirecting jamming or multi-user interference to unauthorized receivers, while minimizing that to legitimate receivers. In all cases, the accuracy of the channel state information (CSI) available at the transmitters plays a crucial role in determining the degree of interference mitigation and confidentiality that is achieved. This dissertation ...

Compressed sensing (CS) is a recently introduced signal acquisition framework that goes against the traditional Nyquist sampling paradigm. CS demonstrates that a sparse, or compressible, signal can be acquired using a low rate acquisition process. Since noise is always present in practical data acquisition systems, sensing and reconstruction methods are developed assuming a Gaussian (light-tailed) model for the corrupting noise. However, when the underlying signal and/or the measurements are corrupted by impulsive noise, commonly employed linear sampling operators, coupled with Gaussian-derived reconstruction algorithms, fail to recover a close approximation of the signal. This dissertation develops robust sampling and reconstruction methods for sparse signals in the presence of impulsive noise. To achieve this objective, we make use of robust statistics theory to develop appropriate methods addressing the problem of impulsive noise in CS systems. We develop a generalized Cauchy distribution (GCD) ...

Compressed Sensing (CS) is a widely used technique for efficient signal acquisition, in which a very small number of (possibly noisy) linear measurements of an unknown signal vector are taken via multiplication with a designed ‘sensing matrix’ in an application-specific manner, and later recovered by exploiting the sparsity of the signal vector in some known orthonormal basis and some special properties of the sensing matrix which allow for such recovery. We study three new applications of CS, each of which poses a unique challenge in a different aspect of it, and propose novel techniques to solve them, advancing the field of CS. Each application involves a unique combination of realistic assumptions on the measurement noise model and the signal, and a unique set of algorithmic challenges. We frame Pooled RT-PCR Testing for COVID-19 – wherein RT-PCR (Reverse Transcription Polymerase Chain ...

Wireless networks and power distribution grids are experiencing increasing demands on their efficiency and reliability. Judicious methods for allocating scarce resources such as power and bandwidth are of paramount importance. As a result, nonlinear optimization and signal processing tools have been incorporated into the design of contemporary networks. This thesis develops schemes for efficient resource allocation (RA) in such dynamic networks, with an emphasis in stochasticity, which is accounted for in the problem formulation as well as in the algorithms and schemes to solve those problems. Stochastic optimization and decomposition techniques are investigated to develop low-complexity algorithms with specific applications in cross-layer design of wireless communications, cognitive radio (CR) networks and smart power distribution systems. The costs and constraints on the availability of network resources, together with diverse quality of service (QoS) requirements, render network design, management, and operation challenging ...

Over the last decade there has been an increasing interest in solutions for the continuous monitoring of health status with wireless, and in particular, wearable devices that provide remote analysis of physiological data. The use of wireless technologies have introduced new problems such as the transmission of a huge amount of data within the constraint of limited battery life devices. The design of an accurate and energy efficient telemonitoring system can be achieved by reducing the amount of data that should be transmitted, which is still a challenging task on devices with both computational and energy constraints. Furthermore, it is not sufficient merely to collect and transmit data, and algorithms that provide real-time analysis are needed. In this thesis, we address the problems of compression and analysis of physiological data using the emerging frameworks of Compressive Sensing (CS) and sparse ...

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 the last few years, wireless devices have evolved to unimaginable heights. Current forecasts suggest that, in the near future, every device that may take advantage of a wireless connection will have one. In addition, there is a gradual migration to smart devices and high-speed connections, and, as a consequence, the overall mobile traffic is expected to experience a tremendous growth in the next years. The multiuser interference will hence become the main limiting factor and the most critical point to address. As instrumental to efficiently manage interference between different systems, this thesis provides a thorough study on cooperative techniques. That is, users share information and exploit it to improve the overall performance. Since multiuser cooperation represents a very broad term, we will focus on algorithm design and transceiver optimization for three cooperative scenarios that capture some of the main ...