Artificial intelligence (AI) and machine learning (ML) are nowadays mature
technologies considered essential for driving the evolution of future
communications systems. Simultaneously, Wi-Fi technology has constantly evolved
over the past three decades and incorporated new features generation after
generation, thus gaining in complexity. As such, researchers have observed that
AI/ML functionalities may be required to address the upcoming Wi-Fi challenges
that will be otherwise difficult to solve with traditional approaches. This
paper discusses the role of AI/ML in current and future Wi-Fi networks and
depicts the ways forward. A roadmap towards AI/ML-native Wi-Fi, key challenges,
standardization efforts, and major enablers are also discussed. An exemplary
use case is provided to showcase the potential of AI/ML in Wi-Fi at different
adoption stages.

人工智能和机器学习是驱动未来通信系统演进的必备技术，而 Wi-Fi 网络不断演化且变得越来越复杂。本文讨论了人工智能和机器学习在当前和未来 Wi-Fi 网络中的作用，并提出了人工智能和机器学习原生 Wi-Fi 的发展方向、关键挑战、标准化努力和主要推动因素。通过提供一个示例用例展示了人工智能和机器学习在不同采用阶段的 Wi-Fi 中的潜力。

基于机器学习和 Wi-Fi 的 AI/ML 本地化 IEEE 802.11 网络揭示之路

Machine Learning & Wi-Fi: Unveiling the Path Towards AI/ML-Native IEEE  802.11 Networks

Generative Face Video Coding (GFVC) techniques can exploit the compact
representation of facial priors and the strong inference capability of deep
generative models, achieving high-quality face video communication in ultra-low
bandwidth scenarios. This paper conducts a comprehensive survey on the recent
advances of the GFVC techniques and standardization efforts, which could be
applicable to ultra low bitrate communication, user-specified
animation/filtering and metaverse-related functionalities. In particular, we
generalize GFVC systems within one coding framework and summarize different
GFVC algorithms with their corresponding visual representations. Moreover, we
review the GFVC standardization activities that are specified with supplemental
enhancement information messages. Finally, we discuss fundamental challenges
and broad applications on GFVC techniques and their standardization potentials,
as well as envision their future trends. The project page can be found at
this https URL

Generative Face Video Coding (GFVC) techniques, which exploit facial priors and deep generative models, are comprehensively surveyed in this paper, including standardization efforts and their potential applications.