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논문 링크: Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and OpportunitiesLarge language models (LLMs) have received considerable attention recently due to their outstanding comprehension and reasoning capabilities, leading to great progress in many fields. The advancement of LLM techniques also offers promising opportunities toieeexplore.i..

전체 논문 중 NOMA의 기본 개념에 대해서 정리한다. 출처: Y. Liu, Z. Qin, M. Elkashlan, Z. Ding, A. Nallanathan and L. Hanzo, "Nonorthogonal Multiple Access for 5G and Beyond," in Proceedings of the IEEE, vol. 105, no. 12, pp. 2347-2381, Dec. 2017.논문 링크: Nonorthogonal Multiple Access for 5G and Beyond | IEEE Journals & Magazine | IEEE Xplore Nonorthogonal Multiple Access for 5G and BeyondDriven by the rapid escalation..

논문 링크: Disentangling Learnable and Memorizable Data via Contrastive Learning for Semantic Communications | IEEE Conference Publication | IEEE Xplore 출처: C. Chaccour and W. Saad, "Disentangling Learnable and Memorizable Data via Contrastive Learning for Semantic Communications," 2022 56th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2022.요약 6G 시대엔 메타버스와 같은 차..

논문 링크: A Framework for AI-Native Semantic-Based Dynamic Slicing for 6G NetworksIn the ensuing ultra-dense and diverse environment in future \ac{6G} communication networks, it will be critical to optimize network resources via mechanisms that recognize and cater to the diversity, density, and dynamicity of system changes. However, coparxiv.org 출처: M. R. Chowdhury, E. Hammad, L. Loven, S. Pirttika..

논문 링크: Life-long Learning for Reasoning-based Semantic CommunicationSemantic communication is an emerging paradigm that focuses on understanding and delivering semantics or meaning of messages. Most existing semantic communication solutions define semantic meaning as the labels of objects recognized from a given form of soieeexplore.ieee.org 출처: J. Liang, Y. Xiao, Y. Li, G. Shi and M. Bennis, "L..

논문 링크: Bayesian Inverse Contextual Reasoning for Heterogeneous Semantics- Native CommunicationThis work deals with a heterogeneous semantics-native communication (SNC) problem. When agents do not share the same communication context, the effectiveness of contextual reasoning (CR) is compromised calling for agents to infer other agents’ context beieeexplore.ieee.org 출처: H. Seo, Y. Kang, M. Benni..

논문 링크: Semantics-Native Communication via Contextual ReasoningRecently, machine learning (ML) has shown its effectiveness in improving communication efficiency by reinstating the semantics of bits. To understand its underlying principles, we propose a novel stochastic model of semantic communication, dubbed semanticsieeexplore.ieee.org 출처: H. Seo, J. Park, M. Bennis and M. Debbah, "Semantics-Na..

논문 링크: Less Data, More Knowledge: Building Next-Generation Semantic Communication NetworksSemantic communication is viewed as a revolutionary paradigm that can potentially transform how we design and operate wireless communication systems. However, despite a recent surge of research activities in this area, remarkably, the research landscape isieeexplore.ieee.org 출처: C. Chaccour, W. Saad, M. De..

논문 링크: Telecom’s Artificial General Intelligence (AGI) Vision: Beyond the GenAI FrontierThis paper unveils the groundbreaking impact of Generative AI (GenAI) as the dawn of a transformative era in 5G/6G networks and beyond. Exploring its disruptive potential across the value chain—from network design to agile and robust automation—we showieeexplore.ieee.org 출처: C. Chaccour, A. Karapantelakis, T..