Professor Theodore Rappaport (New York University) introduces the "Waste Factor" (W) or "Waste Figure," a new figure of merit for power efficiency in circuits and systems. This metric, akin to the

Professor Theodore Rappaport (New York University) introduces the "Waste Factor" (W) or "Waste Figure," a new figure of merit for power efficiency in circuits and systems. This metric, akin to the Noise Factor, addresses a critical gap by quantifying wasted power along any cascade. It's a vital Key Performance Indicator (KPI) for energy-efficient design, applicable from circuits to wireless networks and lossy channels. This webinar is organized by the ITU Journal on Future and Evolving Technologies (ITU J-FET), a free international journal, and moderated by Ian F. Akyildiz and Alessia Magliarditi.

Discover the ITU Journal on Future and Evolving Technologies (ITU J-FET) here. Explore the full ITU Journal webinar series programme here. #ITU #ITUJFET

Telecommunications operators, including China Mobile, face significant transformation due to evolving demands, technologies, and strategies. China Mobile advocates for a future-centric network

Telecommunications operators, including China Mobile, face significant transformation due to evolving demands, technologies, and strategies. China Mobile advocates for a future-centric network integrating "connectivity, computing power, and capability" to meet digital economy needs, innovating from 5G to 6G and establishing a new information service framework.

This strategic transformation was discussed by Wang Xiaoyun of China Mobile during a webinar organized by the ITU Journal on Future and Evolving Technologies (ITU J-FET). This international journal covers all communications and networking paradigms, free for readers and authors, and explores fundamental and applied research. Learn more about the ITU Journal and its webinar series.

Machine Learning (ML) models in mobile applications demand significant computational resources. While distributed ML offers benefits like load distribution and privacy at the network edge, it

Machine Learning (ML) models in mobile applications demand significant computational resources. While distributed ML offers benefits like load distribution and privacy at the network edge, it paradoxically introduces challenges such as slower execution and high bandwidth consumption, hindering low-latency edge services.

This talk addresses this paradox, proposing solutions through dynamic ML model structures and compression techniques. Organized by the ITU Journal on Future and Evolving Technologies (ITU J-FET), this webinar is part of a series covering communications and networking. Explore the ITU J-FET and its webinar series.

Next-generation communication networks, heavily reliant on AI and Machine Learning, confront a critical challenge: balancing transparency, privacy, and performance. These three essential

Next-generation communication networks, heavily reliant on AI and Machine Learning, confront a critical challenge: balancing transparency, privacy, and performance. These three essential objectives often stand in tension; for example, enhancing transparency might reveal sensitive data, while prioritizing privacy could limit data access and impact AI model performance. Achieving a delicate equilibrium is crucial for successful AI system deployment, steering clear of a potential "dark triad."

This webinar, organized by the ITU Journal on Future and Evolving Technologies (ITU J-FET), explores these complex dynamics. The ITU Journal is an international, free-access publication covering fundamental and applied research in communications and networking. Discover the ITU Journal on Future and Evolving Technologies (ITU J-FET) here, and explore the full ITU Journal webinar series programme here.

Challenging the resource-intensive "bigger is better" trend of Large Language Models (LLMs), this webinar introduced the Expressive Neural Network (ENN). This novel architecture rethinks

Challenging the resource-intensive "bigger is better" trend of Large Language Models (LLMs), this webinar introduced the Expressive Neural Network (ENN). This novel architecture rethinks activation functions using classical signal processing (Discrete Cosine Transform), resulting in enhanced flexibility, faster convergence, and significantly smaller, more energy-efficient AI models.

ENNs demonstrate how traditional signal processing can inspire next-generation AI, enabling efficient edge computing in resource-constrained environments. This approach prioritizes expressiveness over sheer size for future neural networks. The webinar featured speaker Ana Pérez-Neira, moderated by Ian F. Akyildiz and Alessia Magliarditi. It was organized by the ITU Journal on Future and Evolving Technologies (ITU-J FET).

6G is rapidly becoming a reality, poised to transform our lives by expanding network capabilities beyond traditional metrics. Unlike previous generations, 6G will prioritize sustainability,

6G is rapidly becoming a reality, poised to transform our lives by expanding network capabilities beyond traditional metrics. Unlike previous generations, 6G will prioritize sustainability, trustworthiness, and digital inclusion, aiming to liberate human potential. Nishant Batra will detail key technologies, including network-as-a-sensor and the AI-native air interface.

This webinar is organized by the ITU Journal on Future and Evolving Technologies (ITU J-FET), a free international journal covering all communications and networking paradigms. It publishes fundamental and applied research, and discusses open topics for future research.

Discover the ITU Journal on Future and Evolving Technologies (ITU J-FET) and explore the full ITU Journal webinar series programme.