Telecom World Models introduce a three-layer architecture for learned, action-conditioned, uncertainty-aware modeling of 6G network dynamics, combining digital twins and foundation models, with a network slicing proof-of-concept showing improved KPI prediction over baselines.
Wireless large AI model: shaping the AI-empowered future of 6G and beyond
4 Pith papers cite this work. Polarity classification is still indexing.
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abstract
The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is a wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, explaining its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges and discuss pivotal future research directions.
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ComHymba introduces a domain-informed wireless foundation model with Hymba blocks for linear-complexity CSI modeling, reporting accuracy gains on eight downstream tasks and up to 3.3x inference speedup over Transformers.
A hybrid beamforming framework combining liquid crystal antennas and liquid neural networks delivers 88.6% spectral efficiency gain and improved robustness in 108 GHz urban ray-tracing simulations compared to baselines and 3GPP models.
AI and terahertz networks form a mutual symbiosis where each addresses the limitations of the other across hardware, physical layer, protocols, and services.
citing papers explorer
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Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G
Telecom World Models introduce a three-layer architecture for learned, action-conditioned, uncertainty-aware modeling of 6G network dynamics, combining digital twins and foundation models, with a network slicing proof-of-concept showing improved KPI prediction over baselines.
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ComHymba: Low-Complexity Domain-Informed Foundation Model for Wireless Communications
ComHymba introduces a domain-informed wireless foundation model with Hymba blocks for linear-complexity CSI modeling, reporting accuracy gains on eight downstream tasks and up to 3.3x inference speedup over Transformers.
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Robust Hybrid Beamforming with Liquid Crystal Antennas and Liquid Neural Networks
A hybrid beamforming framework combining liquid crystal antennas and liquid neural networks delivers 88.6% spectral efficiency gain and improved robustness in 108 GHz urban ray-tracing simulations compared to baselines and 3GPP models.
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When AI Meets Terahertz: A Survey on the Symbiosis of Artificial Intelligence and Terahertz Networks
AI and terahertz networks form a mutual symbiosis where each addresses the limitations of the other across hardware, physical layer, protocols, and services.