Adversarial Water-Filling formulates competitive spectrum sharing as a constrained minimax problem and introduces a permutation-invariant GNN foundation model that approximates its stationary solutions with local linear convergence under regularity conditions.
WirelessLLM: Empowering large language models towards wireless intelligence
5 Pith papers cite this work. Polarity classification is still indexing.
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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.
VLMs and CNNs complement each other on spectrum tasks, with CNNs strong on spatial localization and VLMs on semantic reasoning; a router combining them improves composite performance by 39% over CNN alone.
MM-Telco creates multimodal benchmarks for telecom and demonstrates that fine-tuned LLMs and VLMs achieve significant performance gains on domain-specific tasks.
A survey formalizing responsibility-oriented goals for wireless XAI, developing a taxonomy of explainability approaches, reviewing PHY layer applications, and discussing open challenges including performance tradeoffs and LLM integration.
citing papers explorer
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Adversarial Water-Filling: Theory, Algorithms and Foundation Model
Adversarial Water-Filling formulates competitive spectrum sharing as a constrained minimax problem and introduces a permutation-invariant GNN foundation model that approximates its stationary solutions with local linear convergence under regularity conditions.
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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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When Does Multimodal AI Help? Diagnostic Complementarity of Vision-Language Models and CNNs for Spectrum Management in Satellite-Terrestrial Networks
VLMs and CNNs complement each other on spectrum tasks, with CNNs strong on spatial localization and VLMs on semantic reasoning; a router combining them improves composite performance by 39% over CNN alone.
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MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications
MM-Telco creates multimodal benchmarks for telecom and demonstrates that fine-tuned LLMs and VLMs achieve significant performance gains on domain-specific tasks.
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Explainable AI for Next-Generation Wireless Physical Layer: Basics, State-of-the-Art, and Open Challenges
A survey formalizing responsibility-oriented goals for wireless XAI, developing a taxonomy of explainability approaches, reviewing PHY layer applications, and discussing open challenges including performance tradeoffs and LLM integration.