arXiv subjects

Angelo Vera-Rivera

Publications and source records attributed to Angelo Vera-Rivera.

5 recordsLinked to original sources

arXiv2026

Advanced Holographic Multi-Antenna Solutions for Global Non-Terrestrial Network Integration in IMT-2030 Systems

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.

cs.NI↗

arXiv2025

Toward Hyper-Dimensional Connectivity in Beyond 6G: A Conceptual Framework

Cellular wireless networks enable mobile broadband connectivity for Internet-based applications through their radio access and core network infrastructure. While Fifth-Generation (5G) cellular systems are currently being deployed, ongoing research on cellular technologies primarily focuses on Sixth-Generation (6G) networks to set the stage for developing standards for these systems. Therefore, the time has come to articulate the visions for beyond 6G (B6G) systems. In this article, we present a visionary framework toward hyper-dimensional connectivity in B6G that enables wireless access to hyper-immersive Internet technologies. Our contributions include a conceptual framework for B6G cellular systems with jointly integrated communication, cognition, computing, and cyber-physical capabilities as core connectivity dimensions, a set of technical definitions outlining potential use cases and system-level requirements, a mapping of prospective technology enablers, and a forward-looking research agenda for B6G systems. The conceptual discussions in this article would be helpful for identifying innovation drivers, shaping long-term technical goals, and defining research agendas for the future of mobile broadband technologies.

cs.NI↗

arXiv2025

Decentralizing Trust: Consortium Blockchains and Hyperledger Fabric Explained

Trust models are essential components of networks of any nature, as they refer to confidence frameworks to evaluate and verify if their participants act reliably and fairly. They are necessary to any social, organizational, or computer network model to ensure truthful interactions, data integrity, and overall system resilience. Trust models can be centralized or distributed, each providing a good fair of benefits and challenges. Blockchain is a special case of distributed trust models that utilize advanced cryptographic techniques and decentralized consensus mechanisms to enforce confidence among participants within a network. In this piece, we provide an overview of blockchain networks from the trust model perspective, with a special focus on the Hyperledger Fabric framework, a widespread blockchain implementation with a consortium architecture. We explore Fabric in detail, including its trust model, components, overall architecture, and a general implementation blueprint for the platform. We intend to offer readers with technical backgrounds but not necessarily experts in the blockchain field a friendly review of these topics to spark their curiosity to continue expanding their knowledge on these increasingly popular technologies.

cs.DC↗

arXiv2025

6G Cellular Networks: Mapping the Landscape for the IMT-2030 Framework

The IMT-2030 framework provides the vision and conceptual foundation for the next-generation of mobile broadband systems, colloquially known as Sixth-Generation (6G) cellular networks. Academic circles, industry players, and Standard Developing Organizations (SDOs) are already engaged in early standardization discussions for the system, providing key insights for future technical specifications. In this context, a structured thematic synthesis aligned with IMT-2030 is essential to inform the discussions and assist collaboration among 6G stakeholders -- including scholars, professionals, regulators, and SDO officials. This review intends to offer a concise yet informative synthesis of well-established 6G literature, viewed through the IMT-2030 lens, for both specialists and generalists engaged in shaping future standards and advancing 6G research.

cs.NI↗

arXiv2024

Science-Informed Design of Deep Learning With Applications to Wireless Systems: A Tutorial

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.

cs.IT↗