Direction-of-Arrival (DOA) estimation from data collected at a sensor array in the presence of noise has been a fundamental and long-established research topic of interest in sensor array processing. The application of DOA estimation does not only restrict to radar but also spans multiple additional fields of research, including radio astronomy, biomedical imaging, seismic exploration, wireless communication, among others. Due to the wide applications of DOA estimation, various methods have been developed in the literature to increase the resolution capability, computational efficiency, and robustness of the algorithms. However, a trade-off between the estimation performance and the computational complexity is generally inevitable. This thesis addresses the challenge of developing low-complexity DOA estimators with the ability to resolve closely spaced source signals in the threshold region, i.e., low sample size or low Signal-to-Noise ratio. Motivated by various interpretations of the conventional DOA ...

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 ...

Since the late 1990s, massive multiple-input multiple-output (MIMO) has been suggested to improve the achievable data rate in wireless communication systems. To overcome the high path losses in the high frequency bands, the use of massive MIMO will be a must rather than an option in future wireless communication systems. At the same time, due to the high cost and high energy consumption of the traditional fully digital beamforming architecture, a new beamforming architecture is required. Among the proposed solutions, the hybrid analog digital (HAD) beamforming architecture has received considerable attention. The promising massive MIMO gains heavily rely on the availability of accurate channel state information (CSI). This thesis considers a wideband massive MIMO orthogonal frequency division multiplexing (OFDM) system. We propose a channel estimation method called sequential alternating least squares approximation (SALSA) by exploiting a hidden tensor structure in ...

The problem of segregating a sound source of interest from an acoustic background has been extensively studied due to applications in hearing prostheses, robust speech/speaker recognition and audio information retrieval. Computational auditory scene analysis (CASA) approaches the segregation problem by utilizing grouping cues involved in the perceptual organization of sound by human listeners. Binaural processing, where input signals resemble those that enter the two ears, is of particular interest in the CASA field. The dominant approach to binaural segregation has been to derive spatially selective filters in order to enhance the signal in a direction of interest. As such, the problems of sound localization and sound segregation are closely tied. While spatial filtering has been widely utilized, substantial performance degradation is incurred in reverberant environments and more fundamentally, segregation cannot be performed without sufficient spatial separation between sources. This dissertation ...

This dissertation details three approaches for direction-of-arrival (DOA) estimation or beamforming in array signal processing from the perspective of sparsity. In the first part of this dissertation, we consider sparse array beamformer design based on the alternating direction method of multipliers (ADMM); in the second part of this dissertation, the problem of joint DOA estimation and distorted sensor detection is investigated; and off-grid DOA estimation is studied in the last part of this dissertation. In the first part of this thesis, we devise a sparse array design algorithm for adaptive beamforming. Our strategy is based on finding a sparse beamformer weight to maximize the output signal-to-interference-plus-noise ratio (SINR). The proposed method utilizes ADMM, and admits closed-form solutions at each ADMM iteration. The algorithm convergence properties are analyzed by showing the monotonicity and boundedness of the augmented Lagrangian function. In addition, ...

Position information is important for various applications, including location-aware communications, autonomous driving, industrial internet of things (IoT). Geometry-based techniques such as time-of-arrival (TOA), time-difference-of-arrival (TDOA), and angle-of-arrival (AOA) are widely used and can be formed as optimization problems. In order to solve these optimization problems efficiently, stochastic optimization methods are discussed in this work in solving target positioning problems and tackling key issues in location-based applications. Firstly, the direction of arrival (DOA) estimation problem is studied in this work. Grid search is useful in the algorithms such as maximum likelihood estimator (MLE), MUltiple SIgnal Classification (MUSIC), etc. However, the computational cost is the main drawback. To speed up the search procedure, we implement random ferns to extract the features from the beampatterns of different DOAs and use these features to identify potential angle candidates. Then, we propose an ultrasonic air-writing ...

Multiple-input multiple output (MIMO) systems deploy multiple antennas at either end of a communication link and can provide significant benefits compared to traditional single antenna systems, such as increased data rates through spatial multiplexing gain, and/or improved link reliability through diversity techniques. Recently, the natural extension of utilising multiple antennas in relay networks, known as MIMO relaying, has attracted significant research attention due to the fact that the benefits of MIMO can be coupled with extended network coverage through the use of relaying devices. This thesis concentrates on the design and analysis of different aspects of MIMO relay systems communicating over frequency selective channels with the use of orthogonal frequency division multiplexing (OFDM). The first focus of this thesis is on the development of training based channel estimation algorithms for two-hop MIMO OFDM relaying. In the first phase of channel ...

Multiple-input multiple-output (MIMO) systems have recently emerged as one of the most significant wireless techniques, as they can greatly improve the channel capacity and link reliability of wireless communications. These benefits have encouraged extensive research on a virtual MIMO system where the transmitter has multiple antennas and each of the receivers has a single antenna. Single-antenna receivers can work together to form a virtual antenna array and reap some performance benefits of MIMO systems. The idea of receiver-side local cooperation is attractive for wireless networks since a wireless receiver may not have multiple antennas due to size and cost limitations. In this thesis we investigate a virtual-MIMO wireless system using the receiver-side cooperation with the compress-and-forward (CF) protocol. Firstly, to perform CF at the relay, we propose to use standard source coding techniques, based on the analysis of its expected ...

Independent component analysis (ICA) and blind source separation (BSS) deal with extracting a number of mutually independent elements from a set of observed linear mixtures. Motivated by various applications, this work considers a more general and more flexible model: the sources can be partitioned into groups exhibiting dependence within a given group but independence between two different groups. We argue that this is tantamount to considering multidimensional components, as opposed to the standard ICA case which is restricted to one-dimensional components. In this work, we focus on second-order methods to separate statistically-independent multidimensional components from their linear instantaneous mixture. The purpose of this work is to provide theoretical answers to questions which so far have been discussed mainly in the empirical domain. Namely, we provide a closed-form expression for the figure of merit, the mean square error (MSE), for multidimensional ...

This thesis addresses a number of problems all related to parameter estimation in sensor array processing. The unifying theme is that some of these parameters are known before the measurements are acquired. We thus study how to improve the estimation of the unknown parameters by incorporating the knowledge of the known parameters; exploiting this knowledge successfully has the potential to dramatically improve the accuracy of the estimates. For covariance matrix estimation, we exploit that the true covariance matrix is Kronecker and Toeplitz structured. We then devise a method to ascertain that the estimates possess this structure. Additionally, we can show that our proposed estimator has better performance than the state-of-art when the number of samples is low, and that it is also efficient in the sense that the estimates have Cramér-Rao lower Bound (CRB) equivalent variance. In the direction of ...

This thesis focuses on different nonlinear distortion aspects in radio transmitter and receivers. Such nonlinear distortion aspects are generally becoming more and more important as the communication waveforms themselves get more complex and thus more sensitive to any distortion. Also balancing between the implementation costs, size, power consumption and radio performance, especially in multiradio devices, creates tendency towards using lower cost, and thus lower quality, radio electronics. Furthermore, increasing requirements on radio flexibility, especially on receiver side, reduces receiver radio frequency (RF) selectivity and thus increases the dynamic range and linearity requirements. Thus overall, proper understanding of nonlinear distortion in radio devices is essential, and also opens the door for clever use of digital signal processing (DSP) in mitigating and suppressing such distortion effects. On the receiver side, the emphasis in this thesis is mainly on the analysis and DSP ...

Base station cooperation is recognized as a key technology for future wireless cellular communication networks. Considering antennas of distributed base stations and those of multiple terminals within those cells as a distributed multiple-input multiple-output (MIMO) system, this technique has the potential to eliminate inter-cell interference by joint signal processing and to enhance spectral efficiency in this way. Although the theoretical gains are meanwhile well-understood, it still remains challenging to realize the full potential of such cooperative schemes in real-world systems. Among other factors, such as the limited overhead for pilot symbols and for the feedback and backhaul, these performance limitations are related to channel and synchronization impairments, such as channel estimation, feedback quantization and channel aging, as well as imperfect carrier and sampling synchronization among the base stations. Because of these impairments, joint data precoding results to be mismatched with ...

In the last decades the world has experienced a massive growth in the demand for wireless services. The recent popularity of hand-held devices with data exchange capabilities over wireless networks, such as smartphones and tablets, increased the wireless data traffic even further. This trend is not expected to cease in the foreseeable future. In fact, it is expected to accelerate as everyday apparatus unrelated with data communications, such as vehicles or household devices, are foreseen to be equipped with wireless communication capabilities. Further, the next generation wireless networks should be designed such that they have increased spectral and energy efficiency, provide uniformly good service to all of the accommodated users and handle many more devices simultaneously. Massive multiple-input multiple-output (Massive MIMO) systems, also termed as large-scale MIMO, very large MIMO or full-dimension MIMO, have recently been proposed as a candidate ...

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 ...

Post-selection estimation refers to the scenario where a preliminary data-based selection stage determines the specific estimation problem. In this work, we are researching two fundamental problems of this framework: estimation after model selection and estimation after parameter selection. In estimation after model selection, the observation model is unknown. Therefore, prior to estimation, a model selection procedure is used to chose a model from a set of candidate models. Then, the parameters of the selected model are estimated. In estimation after parameter selection, the observations model is known. In this case, the selection refers to choosing the ``parameters of interest'' based on the data, while the rest of the unknown parameters are considered as nuisance parameters. In both problems, the selection stage impacts the subsequent estimation, for example, by introducing a selection bias. This research establishes a post-selection estimation theory for ...