This Thesis deals with a study of space-time modulation applied to digital metasurfaces and reconfigurable intelligent surfaces (RISs), with a particular focus on their synthesis and diagnostics. The investigations revolve around two fundamental challenges: the efficient design of multi-frequency scatter- ing responses, and the reliable detection of faulty elements in large-scale RISs. On the synthesis side, we explore semi-analytical and machine-learning- based strategies that enable the independent control of harmonic equiva- lent currents in both magnitude and phase. This enables the realization of advanced multi-frequency functionalities, which have been experimentally validated at microwave frequencies. On the diagnostics side, we investigate space-time modulation approaches that allow for accurate and efficient fault identification. Two different strate- gies are analyzed: one based on orthogonal coding, where distinct code chan- nels are assigned to each element, and another based on non-uniform mod- ulation, where a one-to-one mapping is established between harmonic am- plitudes and RIS elements. Both methods are validated through theoretical analysis, simulations, and experiments, demonstrating robustness, scalabil- ity, and practicality for real-world implementations. Overall, the Thesis illustrates how space-time modulation and machine learning can be leveraged to expand both the functional capabilities and the operational reliability of RISs, paving the way toward their deployment in future electromagnetic platforms for wireless communications, radar, and imaging.
Space-Time-Coding Modulation in Digital Metasurfaces: Applications to Multi-Frequency Syntheses and Diagnostics
ROSSI, Marco
2026
Abstract
This Thesis deals with a study of space-time modulation applied to digital metasurfaces and reconfigurable intelligent surfaces (RISs), with a particular focus on their synthesis and diagnostics. The investigations revolve around two fundamental challenges: the efficient design of multi-frequency scatter- ing responses, and the reliable detection of faulty elements in large-scale RISs. On the synthesis side, we explore semi-analytical and machine-learning- based strategies that enable the independent control of harmonic equiva- lent currents in both magnitude and phase. This enables the realization of advanced multi-frequency functionalities, which have been experimentally validated at microwave frequencies. On the diagnostics side, we investigate space-time modulation approaches that allow for accurate and efficient fault identification. Two different strate- gies are analyzed: one based on orthogonal coding, where distinct code chan- nels are assigned to each element, and another based on non-uniform mod- ulation, where a one-to-one mapping is established between harmonic am- plitudes and RIS elements. Both methods are validated through theoretical analysis, simulations, and experiments, demonstrating robustness, scalabil- ity, and practicality for real-world implementations. Overall, the Thesis illustrates how space-time modulation and machine learning can be leveraged to expand both the functional capabilities and the operational reliability of RISs, paving the way toward their deployment in future electromagnetic platforms for wireless communications, radar, and imaging.| File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14242/379651
URN:NBN:IT:UNISANNIO-379651