This PhD dissertation analyzes the behavior of multi-antenna diversity techniques in broadcasting scenarios of TDT (terrestrial digital television) systems and proposes a low-complexity detection and decoding design for their practical implementation. For that purpose, the transmission-reception chains of the European DVB-T (Digital Video Broadcasting - Terrestrial) and DVB-T2 standards have been implemented over which diversity and MIMO (multiple-input multiple-output) techniques have been assessed through Monte Carlo simulations. On one hand, the most important multi-antenna diversity techniques such as CDD (cyclic delay diversity), Alamouti code-based SFBC (space-frequency block coding) and MRC (maximum ratio combining), have been evaluated in a DVB-T system over both fixed and mobile Rayleigh and Ricean channels. With the DVB-T2 standard release, multi-antenna processing has actually been introduced in digital television systems. The distributed SFBC configuration proposed in DVB-T2 is analyzed from a performance point of view considering ...

THE task of modeling data traffic in networks is as old as the first commercial telephony systems. In the recent past in mobile telephone networks the focus has moved from voice to packetswitched services. The new cellular mobile networks of the third generation (UMTS) and the evolved second generation (GPRS) offer the subscriber the possibility of staying online everywhere and at any time. The design and dimensioning is well known for circuit switched voice systems, but not for mobile packet-switched systems. The terms user expectation, grade of service and so on need to be defined. To find these parameters it is important to have an accurate traffic model that delivers good traffic estimates. In this thesis we carried out measurements in a live 3G core network of an Austrian operator, in order to find appropriate models that can serve as ...

Designing energy-efficient multiprocessing hardware for applications such as video decoding or MIMO-OFDM baseband processing is challenging because these applications require high throughput, as well as flexibility for efficient use of the processing resources. Application specific hardwired accelerator circuits are the most energy-efficient processing resources, but are inflexible by nature. Furthermore, designing an application specific circuit is expensive and time-consuming. A solution that maintains the energy-efficiency of accelerator circuits, but makes them flexible as well, is to make the accelerator circuits fine-grained. Fine-grained application specific processing elements can be designed to implement general purpose functions that can be used in several applications and their small size makes the design and verification times reasonable. This thesis proposes an efficient method for orchestrating the use of heterogeneous fine-grained processing elements in dynamic applications without introducing tremendous orchestration overheads. Furthermore, the thesis presents a ...

The term extended reality refers to all possible interactions between real and virtual (computed generated) elements and environments. The extended reality field is rapidly growing, primarily through augmented and virtual reality applications. The former allows users to bring digital elements into the real world, while the latter lets us experience and interact with an entirely virtual environment. While currently extended reality implementations primarily focus on the visual domain, we cannot underestimate the impact of auditory perception in order to provide a fully immersive experience. As a matter of fact, effective handling of the acoustic content is able to enrich the engagement of users. We refer to Extended Audio Reality (EAR) as the subset of extended reality operations related to the audio domain. In this thesis, we propose a parametric approach to EAR conceived in order to provide an effective and ...

This thesis contributes to motion characterization through inertial and physiological signals captured by wearable devices and analyzed using signal processing and deep learning techniques. This research leverages the possibilities of motion analysis for three main applications: to know what physical activity a person is performing (Human Activity Recognition), to identify who is performing that motion (user identification) or know how the movement is being performed (motor anomaly detection). Most previous research has addressed human motion modeling using invasive sensors in contact with the user or intrusive sensors that modify the user’s behavior while performing an action (cameras or microphones). In this sense, wearable devices such as smartphones and smartwatches can collect motion signals from users during their daily lives in a less invasive or intrusive way. Recently, there has been an exponential increase in research focused on inertial-signal processing to ...

In the last two decades, multicarrier modulations have emerged as a low complexity solution to combat the effects of the multipath in wireless communications. Among them, Orthogonal Frequency Division Multiplexing (OFDM) is possibly the most studied modulation scheme, and has also been widely adopted as the foundation of industry standards such as WiMAX or LTE. However, OFDM is sensitive to time-selective channels, which are featured in mobility scenarios, due to the appearance of Inter-Carrier Interference (ICI). Implementation of hardware equipment for the end user is usually implemented in dedicated chips, but in research environments, more flexible solutions are preferred. One popular approach is the so-called Software Defined Radio (SDR), where the signal processing algorithms are implemented in reconfigurable hardware such as Digital Signal Processors (DSPs) and Field Programmable Gate Arrays (FPGAs). The aim of this work is two-fold. On the ...

The recent progress in artificial neural networks (rebranded as “deep learning”) has significantly boosted the state-of-the-art in numerous domains of computer vision offering an opportunity to approach the problems which were hardly solvable with conventional machine learning. Thus, in the frame of this PhD study, we explore how deep learning techniques can help in the analysis of one the most basic and essential semantic traits revealed by a human face, namely, gender and age. In particular, two complementary problem settings are considered: (1) gender/age prediction from given face images, and (2) synthesis and editing of human faces with the required gender/age attributes. Convolutional Neural Network (CNN) has currently become a standard model for image-based object recognition in general, and therefore, is a natural choice for addressing the first of these two problems. However, our preliminary studies have shown that the ...

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 ...

Voice-over-Internet Protocol (VoIP) technology provides the transmission of speech over packet-based networks. The transition from circuit-switched to packet-switched networks introduces two major quality impairments: packet loss and end-to-end delay. This thesis shows that the incorporation of packets that were damaged by bit errors reduces the effective packet loss rate, and thus improves the speech quality as perceived by the user. Moreover, this thesis addresses the impact of transmission delay on conversational interactivity and on the perceived speech quality. In order to study the structure and interactivity of conversations, the framework of Parametric Conversation Analysis (P-CA) is introduced and three metrics for conversational interactivity are defined. The investigation of five conversation scenarios based on subjective quality tests has shown that only highly structured scenarios result in high conversational interactivity. The speaker alternation rate has turned out to represent a simple and ...

This work is focused on the synthesis of Digital Signal Processing (DSP) circuits usingc specific hardware architectures. Due to its complexity, the design process has been subdivided into separate tasks, thus hindering the global optimization of the resulting systems. The author proposes the study of the combination of two major design tasks, Word-Length Allocation (WLA) and High-Level Synthesis (HLS), aiming at the optimization of DSP implementations using modern Field Programmable Gate Array devices (FPGAs). A multiple word-length approach (MWL) is adopted since it leads to highly optimized implementations. MWL implies the customization of the word-lengths of the signals of an algorithm. This complicates the design, since the number possible assignations between algorithm operations and hardware resources becomes very high. Moreover, this work also considers the use of heterogeneous FPGAs where there are several types of resources: configurable logic-based blocks (LUT-based) ...

Feedback in wireless communications is tied to a long-standing and successful history, facilitating robust and spectrally efficient transmission over the uncertain wireless medium. Since the application of multiple antennas at both ends of the communication link, enabling multiple-input multiple-output (MIMO) transmission, the importance of feedback information to achieve the highest performance is even more pronounced. Especially when multiple antennas are employed by the transmitter to handle the interference between multiple users, channel state information (CSI) is a fundamental prerequisite. The corresponding multi-user MIMO, interference alignment and coordination techniques are considered as a central part of future cellular networks to cope with the growing inter-cell-interference, caused by the unavoidable densification of base stations to support the exponentially increasing demand on network capacities. However, this vision can only be implemented with efficient feedback algorithms that provide accurate CSI at the transmitter without ...

In recent years, inertial sensors have undergone major developments. The quality of their measurements has improved while their cost has decreased, leading to an increase in availability. They can be found in stand-alone sensor units, so-called inertial measurement units, but are nowadays also present in for instance any modern smartphone, in Wii controllers and in virtual reality headsets. The term inertial sensor refers to the combination of accelerometers and gyroscopes. These measure the external specific force and the angular velocity, respectively. Integration of their measurements provides information about the sensor’s position and orientation. However, the position and orientation estimates obtained by simple integration suffer from drift and are therefore only accurate on a short time scale. In order to improve these estimates, we combine the inertial sensors with additional sensors and models. To combine these different sources of information, also ...

Ill-posed inverse problems appear in many signal and image processing applications, such as deblurring, super-resolution and compressed sensing. The common approach to address them is to design a specific algorithm, or recently, a specific deep neural network, for each problem. Both signal processing and machine learning tactics have drawbacks: traditional reconstruction strategies exhibit limited performance for complex signals, such as natural images, due to the hardness of their mathematical modeling; while modern works that circumvent signal modeling by training deep convolutional neural networks (CNNs) suffer from a huge performance drop when the observation model used in training is inexact. In this work, we develop and analyze reconstruction algorithms that are not restricted to a specific signal model and are able to handle different observation models. Our main contributions include: (a) We generalize the popular sparsity-based CoSaMP algorithm to any signal ...

Interaction between the Medium Access Control (MAC)-layer and the physical-layer routines is one of the basic concepts of modern wireless networks. Physical-layer dependent resource allocation and scheduling guarantee efficient network utilization. Accordingly, classical link-level analyses, focusing only on the physical-layer are not sufficient anymore for optimum transceiver structure and algorithm development. This thesis presents the development and application of a system-level description suitable for the downlink of Multiple-Input Multiple-Output (MIMO) enhanced High-Speed Downlink Packet Access (HSDPA), with particular focus on the Double Transmit Antenna Array (D-TxAA) transmission mode. The system-level model allows for investigating and evaluating transmission systems and algorithms in the context of cellular networks. Two separate models are proposed to obtain a complete system-level description: (i) a link-quality model, analytically describing the MIMO HSDPA link quality in a so-called equivalent fading parameter structure, and (ii) a link-performance model, ...

In recent years, a huge body of data generated by various applications in domains like social networks and healthcare have paved the way for the development of high performance models. Deep learning has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks. Combined with advancements in electromyography it has given rise to new hand gesture recognition applications, such as human computer interfaces, sign language recognition, robotics control and rehabilitation games. The purpose of this thesis is to develop novel methods for electromyography signal analysis based on deep learning for the problem of hand gesture recognition. Specifically, we focus on methods for data preparation and developing accurate models even when few data are available. Electromyography signals are in general one-dimensional time-series with a rich frequency content. Various feature sets have ...