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201900 https://hdl.handle.net/2286/R.2.N.201900 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2025 126 pages Doctoral Dissertation Academic theses en Doly, Shammi A Bliss, Daniel W. Chiriyath, Alex R. Papandreou-Suppappola, Antonia Dasarathy, Gautam Arizona State University Partial requirement for: Ph.D., Arizona State University, 2025 Field of study: Electrical Engineering Initially motivated by the issues of "spectral congestion" , this dissertation explores the potential of advanced adaptive waveform co-design strategies for Integrated Sensing and Communication (ISAC) systems, targeting the future demands of 6G and next-generation wireless networks. The goal of this research is to develop ISAC algorithms that co-design traditional radar waveforms to enable accurate sensing and high-quality communications simultaneously, through the seamless integration of machine learning (ML) within constrained hardware and spectral resources. Furthermore, the feasibility of the developed ISAC algorithms is evaluated through various experimental testbeds, including hardware-in-the-loop (HWIL) and over-the-air (OTA) platforms, such as WISCANet and GNU Radio. The waveform co-design problem is formulated in different multi-access scenarios and environmental conditions, involving single and multiple ISAC nodes operating under the “Network as Sensors (NaS)” paradigm, enabling simultaneous communication and detection through an optimized radar waveform. A decision-theoretic framework is employed to jointly optimize performance in spectrally shared environments, with the initial formulation based on a Partially Observable Markov Decision Process (POMDP) for single-node configurations. To address the resulting optimization problem, two approximate dynamic programming methods—Near-Black Optimality (NBO) and Recursive Soft Matching Heuristic Policy (RS-MHP)—are extended and systematically evaluated. In addition to traditional Linear Frequency Modulated (LFM) chirps, we explore the opportunities of using a novel hardware-friendly Nonlinear Frequency Modulated (NLFM) chirp co-design methodology based on the Principle of Stationary Phase (PSP) concept. This approach yields a closed-form solution for the spectrum of the NLFM chirp with a parametric polynomial phase in terms of the signum function, highlighting its performance characteristics with respect to the Cramér-Rao Lower Bound (CRLB) in the ISAC scenario. The proposed chirp co-design approach simplifies waveform optimization, enables real-time adaptability, balances radar and communication objectives, and supports spectrum-agile environments. The scope is further extended to distributed ISAC scenarios using the Decentralized POMDP (Dec-POMDP) framework, facilitating scalable decision-making across multiple agents with reduced computational overhead. Together, these contributions establish a robust and practical foundation for ISAC waveform design. This adaptive framework is designed for real-time implementation and is especially well suited for emerging ISAC applications, including autonomous vehicles and secure communications in dynamic next-generation network environments. Electrical Engineering Decision Theoretic Approach Dynamic Programming Next-Gen Wireless Radar Signal Processing Waveform Optimization Wireless Communications Adaptive Waveform Co-design for Integrated Sensing and Communications (ISAC)