Optimizing Power-Enabled Fronthaul and Information Freshness for Cyber-Physical Systems
Open Access Depositedthe design of cost-efficient and resilient communication infrastructure and the timely delivery of information. This dissertation studies these challenges through two complementary research directions. The first research direction focuses on fronthaul network design for large-scale distributed CPS, with a particular emphasis on radio access networks (RANs) as a key application setting. In this architecture, the Central Unit (CU), Distributed Unit (DU), and Radio Unit (RU) are responsible for higher-layer processing, lower-layer baseband processing, and radio-frequency transmission, respectively. Within this context, we study the Multi-Distributed Unit Power-Enabled Optical Fronthaul Design (MPOFD) problem, which incorporates Power-over-Fiber (PWoF) technology into optical fronthaul networks to jointly deliver data and electrical power over a shared fiber infrastructure. The problem is formulated as a constrained optimization using integer linear programming and is shown to be NP-hard. To address its computational complexity, the dissertation develops a suite of scalable heuristic algorithms, including clustering-based approaches and novel graph-theoretic methods, which generate cost-efficient deployment strategies under realistic physical and operational constraints. Beyond cost efficiency, the dissertation also studies resilient fronthaul network design under realistic failure assumptions, where fiber links or optical splitters may become unavailable. To ensure continuous service to remote units, the proposed models incorporate backup connectivity and alternative routing options. During the development and evaluation of these algorithms, the need for fair and reproducible performance comparison became evident. As a result, the dissertation introduces a standardized benchmarking framework for Passive Optical Network (PON) based fronthaul design that applies not only to PWoF-enabled architectures but also to general PON deployments. By adopting common cost assumptions, fixed functional split scenarios, and normalized techno-economic models, the framework enables transparent and reproducible comparisons between exact optimization methods and scalable heuristics for 5G and beyond fronthaul architectures. The second research direction addresses the timely availability of information in CPS by minimizing the Age of Information (AoI), a metric that captures the freshness of system state information. The dissertation develops analytical AoI models for multi-sensor systems in which each update may carry correlated information about multiple processes, derives closed-form expressions under queueing constraints, and formulates optimization frameworks that assign sensor correlations to minimize average AoI. It also studies the role of transmission preemption by introducing probabilistic preemption policies that characterize when preemption improves AoI and how correlation structure shapes optimal scheduling decisions. Together, these results provide insight into the interaction between correlation, queue dynamics, and control policies in maintaining timely information delivery. Taken together, the contributions of this dissertation provide a comprehensive approach to the design of large-scale, networked CPS by jointly addressing infrastructure cost, resilience, and information timeliness. The proposed models, algorithms, and benchmarking tools support the development of scalable, cost-efficient, and responsive CPS with distributed sensing and control.
The growing demand for real-time monitoring, control, and data-intensive decision making has made Cyber-Physical Systems (CPS) a central component of next-generation networked and intelligent systems. However, the scalability and real-time responsiveness of large-scale, networked CPS are constrained by two critical challenges
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