Submarine networks have evolved alongside terrestrial ones over the past several decades. Although there are similarities between these two network categories (e.g., the need to cover ultra-long-haul distances and transport huge amounts of data), there are also important differences that have dictated their different evolutionary paths. Space division multiplexing (SDM) promises to be the ultimate solution to cover future capacity needs and overcome problems of both networks. In this work, we review recent and future submarine technologies, focusing on all critical sectors: cable systems, amplifiers’ technology, submarine network architectures, electrical power- feeding issues, economics, and security. Such an analysis, with the level of detail provided in this manuscript, is not available in the literature so far. We first overview all recently announced SDM-based submarine cable systems, compare their performance (capacity-distance product), and analyze the reasons that led to the first SDM submarine deployment. Also, we report up-to-date experimental results of submarine transmission demonstrations and perform a qualitative categorization that relies on their features. Moreover, based on all latest advances and our study findings, we try to predict the future of SDM submarine optical networks mainly in the fields of fiber types, fiber counts per cable, fiber-coating variants, modulation formats, as well as the type and layout structure of optical amplifiers. More specifically, results show that SDM can offer higher capacities (in order of Pb/s) compared to its counterparts, supported by novel network technologies: pump-farming amplification schemes, high counts up to 50 parallel fiber pairs, thinner fiber coating variants (200 μm), and optimum spectral efficiency (2–3 b/s/Hz). Finally, we conclude that tradeoffs between capacity and implementation complexity and cost will have to be carefully considered for future deployments of submarine cable systems.

Humankind has entered a new era wherein a main characteristic is the convergence of various technologies providing services and exerting a major impact upon all aspects of human activity, be it social interactions with the natural environment. Fixed networks are about to play a major role in this convergence, since they form, along with mobile networks, the backbone that provides access to a broad gamut of services, accessible from any point of the globe. It is for this reason that we introduce a forward-looking approach for fixed networks, particularly focused on Fixed 6th Generation (F6G) networks. First, we adopt a novel classification scheme for the main F6G services, comprising six categories. This classification is based on the key service requirements, namely latency, capacity, and connectivity. F6G networks differ from those of previous generations (F1G–F5G) in that they concurrently support multiple key requirements. We then propose concrete steps towards transforming the main elements of fixed networks, such as optical transceivers, optical switches, etc., such that they satisfy the new F6G service requirements. Our study categorizes the main networking paradigm of optical switching into two categories, namely ultra-fast and ultra-high capacity switching, tailored to different service categories. With regard to the transceiver physical layer, we propose (a) the use of all-optical processing to mitigate performance barriers of analog-to-digital and digital-to-analog converters (ADC/DAC) and (b) the exploitation of optical multi-band transmission, space division-multiplexing, and the adoption of more efficient modulation formats.

The evolution from 4G to 5G—and eventually to the forthcoming 6G networks—has revolutionized wireless communications by enabling high-speed, low-latency services that support a wide range of applications, including the Internet of Things (IoT), smart cities, and critical infrastructures. However, the unique characteristics of these networks—extensive connectivity, device heterogeneity, and architectural flexibility—impose significant security challenges. This paper introduces a comprehensive framework for enhancing the security of current and emerging wireless networks by integrating state-of-the-art machine learning (ML) techniques into intrusion detection and prevention systems. It also thoroughly explores the key aspects of wireless network security, including architectural vulnerabilities in both 5G and future 6G networks, novel ML algorithms tailored to address evolving threats, privacy-preserving mechanisms, and regulatory compliance with the EU AI Act. Finally, a Wireless Intrusion Detection Algorithm (WIDA) is proposed, demonstrating promising results in improving wireless network security.

The sixth generation (6G) of mobile networks will radically change the way that we interact, as the artificial intelligence (AI)-enabled fixed and wireless communications and networks infrastructure will offer improved performance and an unprecedented variety of applications. 6G builds upon the evolution of 5G, which already introduces various innovations in network architecture, technologies, and operation capabilities. We expect further progress across many areas, including the evolving fixed Internet Protocol/optical transport network (called x-haul) connecting the radio access network (RAN), mobile core network (CN) and Internet. It is the one on which 6G will rely upon, as it carries the aggregated traffic of all users and circulates it across the world. In this article, we address possible 6G innovations, such as the implementation of flexible functional splits and end-to-end network slicing, from the viewpoint of evolved x-haul networks that are based on novel and energy-efficient optical communications systems and network technologies.

Integrating artificial intelligence into border control systems may help to strengthen security and make operations more efficient. For example, the emerging application of artificial intelligence for lie detection when inspecting passengers presents significant opportunities for future implementation. However, as it makes use of technology that is associated with artificial intelligence, the system is classified as high risk, in accordance with the EU AI Act and, therefore, must adhere to rigorous regulatory requirements to mitigate potential risks. This manuscript distinctly amalgamates the technical, ethical, and legal aspects, thereby offering an extensive examination of the AI-based lie detection systems utilized in border security. This academic paper is uniquely set apart from others because it undertakes a thorough investigation into the categorization of these emerging technologies in terms of the regulatory framework established by the EU AI Act, which classifies them as high risk. It further makes an assessment of practical case studies, including notable examples such as iBorderCtrl and AVATAR. This in-depth analysis seeks to emphasize not only the enormous challenges ahead for practitioners but also the progress made in this emerging field of study. Furthermore, it seeks to investigate threats, vulnerabilities, and privacy concerns associated with AI, while providing security controls to address difficulties related to lie detection. Finally, we propose a framework that encompasses the EU AI Act’s principles and serves as a foundation for future approaches and research projects. By analyzing current methodologies and considering future directions, the paper aims to provide a comprehensive understanding of the viability and consequences of deploying AI lie detection capabilities in border control.

Artificial Intelligence (AI) is increasingly incorporated into medical devices, revolutionizing diagnostics, treatment planning, and patient monitoring. To ensure AI’s safe and ethical use, the European Commission published the AI Act in 2024, which places stringent obligations on AI systems, especially those classified as high-risk, such as medical devices. This paper evaluates the impact of the EU AI Act on existing regulations such as the Medical Device Regulation (MDR) and the In Vitro Diagnostic Regulation (IVDR). It explores challenges related to compliance, certification processes, and potential conflicts between the AI Act and existing medical device frameworks while providing recommendations for harmonization.