In this thesis, we consider a multiuser system with a transmitter equipped with multiple antennas and only one antenna at each receiver user. This system, which is termed MUMISO (Multi User Multiple Input/Single Output), is of use to model the downlink of a wireless communication system, where multiple antennas at the base station transmit to several users with usually only one antenna at each receiving unit. This downlink channel is also called Broadcast Channel (BC). When considering this broadcast channel, the centralized transmitter clearly has more degrees of freedom than each of the receivers. Therefore, it is appropriate to separate the signals by applying precoding at the transmitter. To be able to design precoding, the transmitter needs knowledge about the channel states of the different receivers. In the case of Frequency Division Duplex (FDD) systems, this knowledge can be obtained ...

Due to a variety of potential barriers to sample acquisition, many of the datasets encountered in important classification applications, ranging from tumor identification to facial recognition, are characterized by small samples of high-dimensional data. In such situations, linear classifiers are popular as they have less risk of overfitting while being faster and more interpretable than non-linear classifiers. They are also easier to understand and implement for the inexperienced practitioner. In this dissertation, several gaps in the literature regarding the analysis and design of linear classifiers for high-dimensional data are addressed using tools from the field of asymptotic Random Matrix Theory (RMT) which facilitate the derivation of limits of relevant quantities or distributions, such as the probability of misclassification of a particular classifier or the asymptotic distribution of its discriminant, in the RMT regime where both the sample size and dimensionality ...

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 ...

Scalable representation of a source (e.g., image/video/3D mesh) enables decoding of the encoded bit-stream on a variety of end-user terminals with varying display, storage and processing capabilities. Furthermore, it allows for source communication via channels with different transmission bandwidths, as the source rate can be easily adapted to match the available channel bandwidth. From a different perspective, error-resilience against channel losses is also very important when transmitting scalable source streams over lossy transmission channels. Driven by the aforementioned requirements of scalable representation and error-resilience, this dissertation focuses on the analysis and design of scalable single and multiple description scalar quantizers. In the first part of this dissertation, we consider the design of scalable wavelet-based semi-regular 3D mesh compression systems. In this context, our design methodology thoroughly analyzes different modules of the mesh coding system in order to single-out appropriate design ...

Signal processing techniques for multiple-antenna transmission can exploit the spatial dimension of the wireless channel to serve multiple users simultaneously, achieving high spectral efficiencies. Realizing such gains; however, is strongly dependent on the availability of highly accurate and up-to-date Channel State Information at the Transmitter (CSIT). This stems from the necessity to deal with multiuser interference through preprocessing; as receivers cannot coordinate in general. In wireless systems, CSIT is subject to uncertainty due to estimation and quantization errors, delays and mismatches. This thesis proposes optimized preprocessing techniques for broadcasting scenarios where a multi-antenna transmitter communicates with single-antenna receivers under CSIT uncertainties. First, we consider a scenario where the transmitter communicates an independent message to each receiver. The most popular preprocessing techniques in this setup are based on linear precoding (or beamforming). Despite their near-optimum rate performances when highly accurate CSIT ...

The next generation of wireless communication networks will facilitate the connection of a large number of devices and a broad range of services. Serving such a large amount of user equipment (UEs) can be of very high cost if using active antenna solutions such as increasing the number of base stations (BSs), using massive multiple-input multiple-output (MIMO) antennas, and deploying relays between the BSs and the UEs. In this context, a passive antenna solution, such as reconfigurable intelligent surfaces (RISs), would be more beneficial and attractive. RIS has become an emerging technology with diverse applications in future wireless networks, owing to its ability to dynamically control and optimize the propagation environment. The rapid evolution driven by escalating performance demands of coverage in blocked line-of-sight (LOS) scenarios has prompted the exploration of RIS. Motivated by the potential benefits of RIS in ...

During the last decade, there has been a steady increase in the demand of high data rates that are to be supported by wireless communication applications. Among the different solutions that have been proposed by the research community to cope with this new demand, the utilization of multiple antennas arises as one of the best candidates due to the fact that it provides both an increase in reliability and also in information transmission rate. Although the use of multiple antennas at the receiver side dates back from the sixties, the full potential of multiple antennas at both communication ends has been both theoretically and practically recognized in the last few years. The design of proper multi-antenna communication systems to satisfy the high data rates demand depends not only on the chosen figure of merit or performance metric, but also on ...

This work considers behavior modeling of analog to digital converters with applications in the radio frequency range, including the field of telecommunication as well as test and measurement instrumentation, where the conversion from analog to digital signals often is a bottleneck in performance. The models are intended to post-process output data from the converter and thereby improve the performance of the digital signal. By building a model of practical converters and the way in which they deviate from ideal, imperfections can be corrected using post-correction methods. Behavior modeling implies generation of a suitable stimulus, capturing the output data, and characterizing a model. The demands on the test setup are high for converters in the radio frequency range. The test-bed used in this thesis is composed of commercial state-of-the-art instruments and components designed for signal conditioning and signal capture. Further, in ...

Advanced nuclear medical imaging systems collect multiple attributes of a large number of photon events, resulting in extremely large datasets which present challenges to image reconstruction and assessment. This dissertation addresses several of these challenges. The image formation process in nuclear medical imaging can be posed as a parametric estimation problem where the image pixels are the parameters of interest. Since nuclear medical imaging applications are often ill-posed inverse problems, unbiased estimators result in very noisy, high-variance images. Typically, smoothness constraints and a priori information are used to reduce variance in medical imaging applications at the cost of biasing the estimator. For such problems, there exists an inherent tradeoff between the recovered spatial resolution of an estimator, overall bias, and its statistical variance; lower variance can only be bought at the price of decreased spatial resolution and/or increased overall bias. ...

In the last years, research has focused on multiple-input multiple-output (MIMO) wireless technology due to the capacity and performance improvement it provides, offering a higher spectral efficiency. In addition, when multiple users take part in the network, the scenario becomes much more complex, since resources like bandwidth, time or transmission power must be shared. Furthermore, the performance of the system is degraded as a consequence of the noise and multiuser interference (MUI). When the transmission is conducted from a base station (BS) to multiple users, a pre-equalization stage called precoding is applied. By means of this, each user will be able to interpret the signal independently, without the knowledge of the channel. Precoding techniques are classified into linear and non-linear. In fact, the non-linear Tomlinson-Harashima precoding (THP) and vector precoding (VP) techniques have been shown to achieve very good results ...

System designers across all disciplines of technology face the need to develop machines capable of independently processing and analyzing data and predicting future data. This is the fundamental problem of interest in “estimation theory,” wherein probabilistic analyses are used to isolate relationships between variables, and in “statistical inference,” wherein those variables are used to make inferences about real-world quantities. In practice, all estimators are designed based on limited statistical generalizations about the behavior of the observed and latent variables of interest; however, these models are rarely fully representative of reality. In such cases, there exists a “model misspecification,” and the resulting estimators will produce results that differ from those of the properly specified estimators. Evaluating the performance of a given estimator may sometimes be done by direct comparison of estimator outputs to known ground truth. However, in many cases, there ...

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 ...

The term "spike" is used to describe a short-time event that is the result of the activity of its source. Spikes can be seen in different signal modalities. In these modalities, often more than one source generates spikes. Classification algorithms can be used to group similar spikes, ideally spikes from the same source. This work examines the classification of spikes generated from neurons and muscles. When each detected spike is assigned to its source, the spike trains of these sources can provide information on complex brain network functioning, muscle disorders, and other applications. During the past several decades, there were many attempts to create and improve spike classification algorithms. No matter how advanced these methods are today, errors in classification cannot be avoided. Therefore, methods that would determine and improve reliability of classification are very desirable. In this work, it ...

The large number of practical applications involving digital images has motivated a significant interest towards restoration solutions that improve the visual quality of the data under the presence of various acquisition and compression artifacts. Digital images are the results of an acquisition process based on the measurement of a physical quantity of interest incident upon an imaging sensor over a specified period of time. The quantity of interest depends on the targeted imaging application. Common imaging sensors measure the number of photons impinging over a dense grid of photodetectors in order to produce an image similar to what is perceived by the human visual system. Different applications focus on the part of the electromagnetic spectrum not visible by the human visual system, and thus require different sensing technologies to form the image. In all cases, even with the advance of ...

The aim of this thesis is to investigate key issues encountered in the design of transmission schemes and receiving techniques for Ultra Wideband (UWB) communication systems. Based on different data rate applications, this work is divided into two parts, where energy efficient and robust physical layer solutions are proposed, respectively. Due to a huge bandwidth of UWB signals, a considerable amount of multipath arrivals with various path gains is resolvable at the receiver. For low data rate impulse radio UWB systems, suboptimal non-coherent detection is a simple way to effectively capture the multipath energy. Feasible techniques that increase the power efficiency and the interference robustness of non-coherent detection need to be investigated. For high data rate direct sequence UWB systems, a large number of multipath arrivals results in severe inter-/intra-symbol interference. Additionally, the system performance may also be deteriorated by ...