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The emergence of sixth-generation (6G) wireless networks imposes physical-layer requirements, particularly ultra-reliable low-latency communication (URLLC), demanding sub-millisecond latency and reliability exceeding 99.999%. Conventional long-blocklength coding schemes are incompatible with these constraints due to inherent decoding delays and interleaving latency. This dissertation develops low-complexity, high-reliability signal processing and coding techniques tailored for short-blocklength wireless machine-type communications…

The rapid growth of mobile traffic and emerging service requirements have accelerated the evolution of wireless networks, particularly through the integration of satellite communication (SatCom) systems and terrestrial networks (TNs). This convergence has led to Integrated Satellite-Terrestrial Networks (ISTNs), envisioned as a promising enabler of seamless, ubiquitous, and high-capacity connectivity in future 6G networks. By…

Ultrasound imaging is a cornerstone of modern clinical diagnostics due to its real-time capability, safety, and portability. Despite its broad utility, from obstetric to cardiovascular imaging and beyond, challenges remain, particularly in maintaining high image quality under physical and anatomical constraints. The modality relies on transmitting acoustic pulses into tissue and reconstructing images from returning…

This work contributes to the field by addressing the problem of Classification and Anomaly Detection using Sparse Representations and Dictionary Learning algorithms. These techniques offer unique advantages for classification problems and anomaly detection, as they efficiently capture the underlying structure of data. By leveraging the inherent sparsity in data, these methods aim to represent data…

The explosive growth of distributed data generation — spanning data centers, sensornetworks, massive IoT, and edge learning places an unsustainable burden on modern infrastructure, where the energy and latency costs of moving raw data often outstrip thoseof processing it. While analog over-the-air computation (OAC) promises a solution byexploiting the natural superposition of wireless waveforms to…

Ultrasound Nondestructive Evaluation (UNDE) systems enjoy widespread usage thanks to their comparative ease of deployment, safety, and ability to characterize critical structures. However, this inspection modality is governed by a complex physical phenomenon and requires extensive post-processing before yielding useful results. Furthermore, when employed for localization and imaging, UNDE necessitates multichannel data, and benefits from…

The increasing complexity of modern decision-making systems has propelled reinforcement learning (RL) into the spotlight, offering a framework for adaptive control and optimization in high-dimensional environments. Despite its successes, RL faces significant challenges, including the curse of dimensionality and high sample complexity, which hinder scalability and efficiency. Traditional non-parametric methods often fall short in addressing…

The field of wireless communications has traditionally relied on signal processing techniques, based on mathematical models, to describe and optimize wireless transmission of information. However, when the underlying assumptions of these models are not fully satisfied, conventional methods often exhibit suboptimal performance. In recent years, machine learning techniques have emerged as a promising alternative to…

The deployment of 5G and 6G wireless networks enforces a paradigm shift in communication system design, driven by the move towards sub-THz frequencies and extremely large antenna arrays. These advancements leave traditional channel modeling—which often relies on far-field assumptions and neglects electromagnetic interactions between antenna elements—physically inconsistent. Furthermore, the overhead associated with acquiring instantaneous channel…

This dissertation focuses on Kronecker compressed sensing, recovering multidimensional sparse signals from their linear projections on Kronecker product measurement matrices. Multidimensional signals are functions of different dimensions, each conveying a specific physical quantity and they arise in applications such as wireless communications and image processing. Kronecker product matrices naturally capture the multidimensional nature, making Kronecker…