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Found 7 papers, 0 papers with code
Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems
Diverging from NLP-based foundation models, the proposed framework promotes the design of large multi-modal models (LMMs) fostered by three key capabilities: 1) processing of multi-modal sensing data, 2) grounding of physical symbol representations in real-world wireless systems using causal reasoning and retrieval-augmented generation (RAG), and 3) enabling instructibility from the wireless environment feedback to facilitate dynamic network adaptation thanks to logical and mathematical reasoning facilitated by neuro-symbolic AI.
Reasoning with the Theory of Mind for Pragmatic Semantic Communication
In this paper, a pragmatic semantic communication framework that enables effective goal-oriented information sharing between two-intelligent agents is proposed.
Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks
We showcase how incorporating causal discovery can assist in achieving dynamic adaptability, resilience, and cognition in addressing these challenges.
Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach
The causal structure in the source data is extracted using novel approaches from the framework of deep end-to-end causal inference, thereby enabling the creation of a semantic representation that is causally invariant, which in turn helps generalize the learned knowledge of the system to unseen scenarios.
Reliable Beamforming at Terahertz Bands: Are Causal Representations the Way Forward?
Future wireless services, such as the metaverse require high information rate, reliability, and low latency.
Neuro-Symbolic Causal Reasoning Meets Signaling Game for Emergent Semantic Communications
In this paper, a novel emergent SC (ESC) system framework is proposed and is composed of a signaling game for emergent language design and a neuro-symbolic (NeSy) artificial intelligence (AI) approach for causal reasoning.
Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication
Novel analytical formulations are developed to define key metrics for semantic message transmission, including semantic distortion, semantic similarity, and semantic reliability.
