This thesis examines the critical relationship between data, complex models, and other methods to measure and analyze them. As models grow larger and more intricate, they require more data, making it vital to use that data effectively. The document starts with a deep dive into nonconvex functions, a fundamental element of modern complex systems, identifying key conditions that ensure these systems can be analyzed efficiently—a crucial consideration in an era of vast amounts of variables. Loss functions, traditionally seen as mere optimization tools, are analyzed and recast as measures of how accurately a model reflects reality. This redefined perspective permits the refinement of data-sourcing strategies for a better data economy. The aim of the investigation is the model itself, which is used to understand and harness the underlying patterns of complex systems. By incorporating structure both implicitly (through periodic patterns) and explicitly (using graphs), the model's ability to make sense of the data is enhanced. Moreover, online learning principles are applied to a crucial practical scenario: robotic resource monitoring. The results established in this thesis, backed by simulations and theoretical proofs, highlight the advantages of online learning methods over traditional ones commonly used in robotics. In sum, this thesis presents an integrated approach to measuring complex systems, providing new insights and methods that push forward the capabilities of machine learning.

With the significant advancements of wireless communication systems that aim to meet exponentially increasing data rate demands, two promising concepts have appeared: (i) Cell-free massive MIMO, which entails the joint transmission and processing of the signals allowing the removal of classical cell boundaries, and (ii) integrated sensing and communication (ISAC), unifying communication and sensing in a single framework. This dissertation aims to take steps toward overcoming the key challenges in each concept and eventually merge them for efficient future communication and sensing networks.Cell-free massive MIMO is a distributed MIMO concept that eliminates classical cell boundaries and provides a robust performance. A significant challenge in realizing the cell-free massive MIMO in practice is its deployment complexity. In particular, connecting its many distributed access points with the central processing unit through wired fronthaul is an expensive and time-consuming approach. To eliminate this problem and enhance scalability, in this dissertation, a cell-free massive MIMO architecture adopting a wireless fronthaul is proposed, and the optimization of achievable rates for the end-to-end system is carried out. The evaluation has shown the strong potential of employing wireless fronthaul in cell-free massive MIMO systems. ISAC merges radar and communication systems, allowing effective sharing of resources, including bandwidth and hardware. The ISAC framework also enables sensing to aid communications, which shows a significant potential in mobile communication applications. Specifically, radar sensing data can address challenges like beamforming overhead and blockages associated with higher frequency, large antenna arrays, and narrow beams. To that end, this dissertation develops radar-aided beamforming and blockage prediction approaches using low-cost radar devices and evaluates them in real-world systems to verify their potential. At the intersection of these two paradigms, the integration of sensing into cell-free massive MIMO systems emerges as an intriguing prospect for future technologies. This integration, however, presents the challenge of considering both sensing and communication objectives within a distributed system. With the motivation of overcoming this challenge, this dissertation investigates diverse beamforming and power allocation solutions. Comprehensive evaluations have shown that the incorporation of sensing objectives into joint beamforming designs offers substantial capabilities for next-generation wireless communication and sensing systems.

Millimeter-wave (mmWave) and sub-terahertz (sub-THz) systems aim to utilize the large bandwidth available at these frequencies. This has the potential to enable several future applications that require high data rates, such as autonomous vehicles and digital twins. These systems, however, have several challenges that need to be addressed to realize their gains in practice. First, they need to deploy large antenna arrays and use narrow beams to guarantee sufficient receive power. Adjusting the narrow beams of the large antenna arrays incurs massive beam training overhead. Second, the sensitivity to blockages is a key challenge for mmWave and THz networks. Since these networks mainly rely on line-of-sight (LOS) links, sudden link blockages highly threaten the reliability of the networks. Further, when the LOS link is blocked, the network typically needs to hand off the user to another LOS basestation, which may incur critical time latency, especially if a search over a large codebook of narrow beams is needed. A promising way to tackle both these challenges lies in leveraging additional side information such as visual, LiDAR, radar, and position data. These sensors provide rich information about the wireless environment, which can be utilized for fast beam and blockage prediction. This dissertation presents a machine-learning framework for sensing-aided beam and blockage prediction. In particular, for beam prediction, this work proposes to utilize visual and positional data to predict the optimal beam indices. For the first time, this work investigates the sensing-aided beam prediction task in a real-world vehicle-to-infrastructure and drone communication scenario. Similarly, for blockage prediction, this dissertation proposes a multi-modal wireless communication solution that utilizes bimodal machine learning to perform proactive blockage prediction and user hand-off. Evaluations on both real-world and synthetic datasets illustrate the promising performance of the proposed solutions and highlight their potential for next-generation communication and sensing systems.

In this dissertation, I implement and demonstrate a distributed coherent mesh beamforming system, for wireless communications, that provides increased range, data rate, and robustness to interference. By using one or multiple distributed, locally-coherent meshes as antenna arrays, I develop an approach that realizes a performance improvement, related to the number of mesh elements, in signal-to-noise ratio over a traditional single-antenna to single-antenna link without interference. I further demonstrate that in the presence of interference, the signal-to-interference-plus-noise ratio improvement is significantly greater for a wide range of environments. I also discuss key performance bounds that drive system design decisions as well as techniques for robust distributed adaptive beamformer construction. I develop and implement an over-the-air distributed time and frequency synchronization algorithm to enable distributed coherence on software-defined radios. Finally, I implement the distributed coherent mesh beamforming system over-the-air on a network of software-defined radios and demonstrate both simulated and experimental results both with and without interference that achieve performance approaching the theoretical bounds.

A distributed framework is proposed for addressing resource sharing problems in communications, micro-economics, and various other network systems. The approach uses a hierarchical multi-layer decomposition for network utility maximization. This methodology uses central management and distributed computations to allocate resources, and in dynamic environments, it aims to efficiently respond to network changes. The main contributions include a comprehensive description of an exemplary unifying optimization framework to share resources across different operators and platforms, and a detailed analysis of the generalized methods under the assumption that the network changes are on the same time-scale as the convergence time of the algorithms employed for local computations.Assuming strong concavity and smoothness of the objective functions, and under some stability conditions for each layer, convergence rates and optimality bounds are presented. The effectiveness of the framework is demonstrated through numerical examples. Furthermore, a novel Federated Edge Network Utility Maximization (FEdg-NUM) architecture is proposed for solving large-scale distributed network utility maximization problems in a fully decentralized way. In FEdg-NUM, clients with private utilities communicate with a peer-to-peer network of edge servers. Convergence properties are examined both through analysis and numerical simulations, and potential applications are highlighted. Finally, problems in a complex stochastic dynamic environment, specifically motivated by resource sharing during disasters occurring in multiple areas, are studied. In a hierarchical management scenario, a method of applying a primal-dual algorithm in higher-layer along with deep reinforcement learning algorithms in localities is presented. Analytical details as well as case studies such as pandemic and wildfire response are provided.

With the rapid development of reflect-arrays and software-defined meta-surfaces, reconfigurable intelligent surfaces (RISs) have been envisioned as promising technologies for next-generation wireless communication and sensing systems. These surfaces comprise massive numbers of nearly-passive elements that interact with the incident signals in a smart way to improve the performance of such systems. In RIS-aided communication systems, designing this smart interaction, however, requires acquiring large-dimensional channel knowledge between the RIS and the transmitter/receiver. Acquiring this knowledge is one of the most crucial challenges in RISs as it is associated with large computational and hardware complexity. For RIS-aided sensing systems, it is interesting to first investigate scene depth perception based on millimeter wave (mmWave) multiple-input multiple-output (MIMO) sensing. While mmWave MIMO sensing systems address some critical limitations suffered by optical sensors, realizing these systems possess several key challenges: communication-constrained sensing framework design, beam codebook design, and scene depth estimation challenges. Given the high spatial resolution provided by the RISs, RIS-aided mmWave sensing systems have the potential to improve the scene depth perception, while imposing some key challenges too. In this dissertation, for RIS-aided communication systems, efficient RIS interaction design solutions are proposed by leveraging tools from compressive sensing and deep learning. The achievable rates of these solutions approach the upper bound, which assumes perfect channel knowledge, with negligible training overhead. For RIS-aided sensing systems, a mmWave MIMO based sensing framework is first developed for building accurate depth maps under the constraints imposed by the communication transceivers. Then, a scene depth estimation framework based on RIS-aided sensing is developed for building high-resolution accurate depth maps. Numerical simulations illustrate the promising performance of the proposed solutions, highlighting their potential for next-generation communication and sensing systems.