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출처: K. Dev, S. A. Khowaja, E. Zeydan, K. Singh and M. Debbah, "Advanced Architectures Integrated With Agentic AI for Next-Generation Wireless Networks," in IEEE Communications Standards Magazine논문 링크: Advanced Architectures Integrated With Agentic AI for Next-Generation Wireless NetworksThis paper investigates a range of cutting-edge technologies and architectural innovations aimed at simplifyi..

논문 링크: Toward Edge General Intelligence With Agentic AI and Agentification: Concepts, Technologies, and Future DirectionsThe rapid expansion of sixth-generation (6G) wireless networks and the Internet of Things (IoT) has catalyzed the evolution from centralized cloud intelligence towards decentralized edge general intelligence. However, traditional edge intelligence methods,ieeexplore.ieee.org ..

논문 링크: Fundamental Limits to Exploiting Side Information for CSI Feedback in Wireless SystemsIn modern wireless systems, the feedback of DownLink (DL) Channel State Information (CSI) from User Equipment (UE) to Base Stations (BS) may require substantial computational and feedback bandwidth overheads. A promising approach to improve feedback efficiieeexplore.ieee.org 출처: H. Kim, G. de Veciana an..

깃허브 링크: GitHub - eunc812/Semantics-Native-Communication-via-Contextual-Reasoning: Implementation of Semantics-Native Communication via CImplementation of Semantics-Native Communication via Contextual Reasoning - eunc812/Semantics-Native-Communication-via-Contextual-Reasoninggithub.com 출처: H. Seo, J. Park, M. Bennis and M. Debbah, "Semantics-Native Communication via Contextual Reasoning," in IEEE..

논문 링크: (PDF) Stigmergy as a Universal Coordination Mechanism: components, varieties and applicationsPDF | The concept of stigmergy has been used to analyze self-organizing activities in an ever-widening range of domains, including social insects,... | Find, read and cite all the research you need on ResearchGatewww.researchgate.net 출처: F. Heylighen, “Stigmergy as a universal coordination mecha..

논문 링크: Multi-agent Reinforcement Learning with Emergent Communication using Discrete and Indifferentiable MessageThis paper proposes an integrated model of multi-agent reinforcement learning with emergent communication based on probabilistic generative models called MASAC- ECo that enables two agents to learn cooperative actions. In this model, agents receive messageieeexplore.ieee.org 출처: H. E..

논문 링크: Efficient Prompting for LLM-Based Generative Internet of ThingsLarge language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently. Due to security concerns, many institutieeexplore.ieee.org 출처: B. Xiao, B. Kantarci, J. Kang, D. Niyato and M. ..

논문 링크: Agentic AI-Enhanced Semantic Communications: Foundations, Architecture, and ApplicationsSemantic communications (SemCom), as one of the key technologies for 6G, is shifting networks from bit transmission to semantic information exchange. On this basis, introducing agentic artificial intelligence (AI) with perception, memory, reasoning, and acarxiv.org 출처: H. Gao, M. Sun, R. Zhang, Y. Wang..

논문 링크: Large Language Model Enhanced Multi-Agent Systems for 6G CommunicationsThe rapid development of the Large Language Model (LLM) presents huge opportunities for 6G communications, e.g., network optimization and management by allowing users to input task requirements to LLMs by nature language. However, directly applying nativearxiv.org 출처: F. Jiang, L. Dong, Y. Peng, K. Wang, K. Yang, C. P..

논문 링크: Representation Learning via Invariant Causal MechanismsSelf-supervised learning has emerged as a strategy to reduce the reliance on costly supervised signal by pretraining representations only using unlabeled data. These methods combine heuristic proxy classification tasks with data augmentations and have achiarxiv.org 출처: J. Mitrovic, B. McWilliams, J. Walker, L. Buesing, and C. Blundel..