On scalable, reconfigurable, and intelligent metasurfaces
- Autores: Hamidreza Taghvaee
- Directores de la Tesis: Albert Cabellos Aparicio (dir. tes.), Sergi Abadal Cavallé (codir. tes.)
- Lectura: En la Universitat Politècnica de Catalunya (UPC) ( España ) en 2021
- Idioma: español
- Materias:
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- Resumen
Sixth Generation (6G) of wireless networks will be even more heterogeneous and dense as compared to Fifth Generation (5G) and other legacy networks. Thus, the 6G architecture will need to be adapted to serve the ever-evolving capacity and quality of service requirements. To satisfy these ever-increasing demands, multiple enablers such as visible light communication, light fidelity, Reconfigurable Intelligent Surfaces (RISs), THz communications, etc., have been proposed. Specifically, RISs, through their programmable characteristics, can perform the fine-grained manipulation of the radio signals being generated by the myriad transmitter devices/access points for their corresponding receivers. Such manipulations include absorption of certain components of the impinging radio signals, as well as fine-grained control of these signals in terms of direction, polarization, phase, and power in a frequency-selective manner. An RIS consists of a device that controls the behavior of the Electromagnetic (EM) waves, alongside circuits that provide the tuning mechanism and the intelligence to control it. This device that controls the EM wave behavior can be realized using Metasurfaces (MSs), which are electromagnetically thin-film and planar artificial structures that enable the control of EM fields in engineered and even atypical ways. Hence, the MS is a component of the RIS. On a more granular level, an MS is composed of subwavelength building blocks known as unit cells or meta-atoms. The design of these unit cells depends on the required EM functionality, reconfigurability, or accuracy. The promises of the RIS paradigm, therefore, come at the expense of a non-trivial complexity in the MS. On the one hand, the performance of an RIS depends on the size of the unit cells, the number of unit cell states, or the size of the whole MS. On the other hand, there are costs and energy overheads associated with the fabrication and operation of RISs that also scale with the aforementioned factors. This thesis aims to bridge this gap by providing a method to dimension the RIS through a design-oriented scalability analysis of programmable MSs. Besides the challenge of design complexity, MSs will become prone to failure as they continue integrating sophisticated tuning, control and sensing circuits. However, the impact of faults on the performance of individual MSs is not well understood yet. This thesis proposes a framework to evaluate the impact of failures in programmable MSs, distinguishing between the type of faults and their spatial distribution. While RIS generally hinge on the design of rather complex tunable MSs, such a complexity can be amortized if the functionality provided by the RIS can be shared among multiple users. This thesis introduces a coding (i.e. digital programming of unit cells) technique based on the momentum conservation law and superposition of waves for MS reconfiguration to engineer multiple beams independently. Then, the wireless channel of such framework among multiple users is evaluated. The capacity is increased at least one order of magnitude compared to a scenario without RIS. Machine learning techniques, and particularly Neural Networks (NNs), owing to their ability to learn complex relationships between input and output data, are capable of solving differential equations, thereby circumventing the need for numerical calculations. This thesis provisions a data-driven NN-based approach for determining an accurate estimation of the radiation pattern or several measures of interest that enable the full characterization of the radiation pattern. In summary, contributions of this thesis fall under the umbrella of paving the way to realizing 5G and beyond wireless communications empowered with RIS technology.
