6G Integrated Sensing and Communication System for the Factory of the Future
- Autores: A.M. Ramos
- Directores de la Tesis: José Francisco Monserrat del Río (dir. tes.)
- Lectura: En la Universitat Politècnica de València ( España ) en 2024
- Idioma: inglés
- Tribunal Calificador de la Tesis: Narciso Cardona Marcet (presid.), Salvador Luna Ramírez (secret.), Xavier Gelabert Doran (voc.)
- Programa de doctorado: Programa de Doctorado en Telecomunicación por la Universitat Politècnica de València
- Materias:
- Enlaces
- Tesis en acceso abierto en: RiuNet
- Resumen
Leveraging Fifth Generation (5G) technology to advance Industry 4.0 has marked a significant milestone in the historical evolution of cellular networks. This development aims to support smart factories with stringent communication requirements, as their operation is focused on meeting Quality of Service (QoS) standards, making the Industrial Internet of Things (IIoT) applications susceptible to unstable network performance. Moreover, these applications frequently occur indoors, where high-density clutter poses additional challenges. Large metal structures, robots, and moving vehicles obstruct signal propagation and can significantly degrade communication performance. The first standardized channel model for Indoor Factory (InF) was introduced by the Third Generation Partnership Project (3GPP) in Release 16 to study and address these environmental particularities. This Thesis builds on this foundation and examines the modeling procedure, identifying limitations such as imprecise parameter characterization and a limited ability to capture the full geometric complexity of such environments. Concerned about these limitations, this work takes a significant step forward by proposing a new technology to address challenges in industrial modeling. This approach opens the door to exploring one of the key emerging trends in Sixth Generation (6G) for IIoT applications: Integrated Sensing and Communications (ISAC) systems. ISAC systems hold the promising potential to overcome not only existing challenges but also introduce additional, valuable enhancements. As ISAC is a novel technology, no channel model has been specifically designed for it so far. To fill this need, this Thesis presents the development of an ISAC channel model as a foundational step in advancing this technology. During this progress, fundamental features for building an ISAC channel model have been identified, which are often overlooked in the literature. In response, this work motivates the development of technical guidelines for ISAC modeling, forming an evaluation methodology. An evaluation methodology is important for ISAC or any system, as it is essential for assessing performance and guiding future upgrades. Such a methodology does not exist for ISAC. This Thesis tackles these challenges by emphasizing the importance of considering the main features to construct an ISAC channel: Correlation between the sensing and communication channel and spatial consistency. Building on the initial development of the ISAC framework, the next step involves testing ISAC in quasi-realistic environments. This Thesis presents an industrial use case that applies sensing-assisted beam training, demonstrating how ISAC can deal with the issue of multiple obstructions in such environments. Specifically, it explores the background subtraction technique in a predictive beamforming algorithm, which leverages target-related information obtained through sensing. Under these considerations, the findings indicate a substantial improvement in communication performance, particularly regarding signal-to-noise ratio (SNR) and effective data rate. In other words, the results highlight ISAC's potential to tackle the geometrical complexities of the environment of interest effectively. This Thesis not only pioneers the background subtraction technique but also showcases its impact, paving the way for future applications to other sensing algorithms within the framework of ISAC and the factory of the future.
