Angelo Vera-Rivera

Alfredo Nunez-Unda, Angelo Vera-Rivera, Nuwan Balasuriya et al.

Sixth-generation (6G) networks are expected to provide ubiquitous connectivity across terrestrial and non-terrestrial domains. This will be possible by integrating non-terrestrial networks (NTNs) to extend coverage to underserved areas. Antennas are central to this vision, with multiple-input multiple-output (MIMO) technologies receiving the most attention due to their ability to exploit spatial multiplexing to improve link capacity and reliability. However, conventional MIMO can consume significant energy, as each antenna element typically requires an independent RF chain. This limitation is particularly critical in non-terrestrial systems, where onboard energy resources are limited. Holographic MIMO (HMIMO) has emerged as a promising alternative in this context. These systems are based on theoretically continuous apertures, where radiation is generated through controlled modulation of surface impedance. This enables beamforming mechanisms with significantly fewer RF chains, reducing power consumption. In this work, we make the case for HMIMO as a suitable candidate for NTN integration within IMT-2030 systems. We discuss its advantages over conventional MIMO and present a case study of HMIMO integration in LEO-based multi-user communication.

Atefeh Termehchi, Ekram Hossain, Angelo Vera-Rivera et al.

Recent advances in computational infrastructure and large-scale data processing have accelerated the adoption of data-driven inference methods, particularly deep learning (DL), to solve problems in many scientific and engineering domains. In wireless systems, DL has been applied to problems where analytical modeling or optimization is difficult to formulate, relies on oversimplified assumptions, or becomes computationally intractable. However, conventional DL models are often regarded as non-transparent, as their internal reasoning mechanisms are difficult to interpret even when model parameters are fully accessible. This lack of transparency undermines trust and leads to three interrelated challenges: limited interpretability, weak generalization, and the absence of a principled framework for parameter tuning. Science-informed deep learning (ScIDL) has emerged as a promising paradigm to address these limitations by integrating scientific knowledge into deep learning pipelines. This integration enables more precise characterization of model behavior and provides clearer explanations of how and why DL models succeed or fail. Despite growing interest, the existing literature remains fragmented and lacks a unifying taxonomy. This tutorial presents a structured overview of ScIDL methods and their applications in wireless systems. We introduce a structured taxonomy that organizes the ScIDL landscape, present two representative case studies illustrating its use in challenging wireless problems, and discuss key challenges and open research directions. The pedagogical structure guides readers from foundational concepts to advanced applications, making the tutorial accessible to researchers in wireless communications without requiring prior expertise in AI.