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6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications

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arxiv 2011.14844 v3 pith:YGG65BG4 submitted 2020-11-04 cs.NI cs.ITcs.LGmath.IT

classification cs.NIcs.ITcs.LGmath.IT
keywords semanticlearninggoalnetworksgoal-orientedmeaningpacketaccomplish
verification ladder T0 review T1 audit T2 compute T3 formal
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The goal of this paper is to promote the idea that including semantic and goal-oriented aspects in future 6G networks can produce a significant leap forward in terms of system effectiveness and sustainability. Semantic communication goes beyond the common Shannon paradigm of guaranteeing the correct reception of each single transmitted packet, irrespective of the meaning conveyed by the packet. The idea is that, whenever communication occurs to convey meaning or to accomplish a goal, what really matters is the impact that the correct reception/interpretation of a packet is going to have on the goal accomplishment. Focusing on semantic and goal-oriented aspects, and possibly combining them, helps to identify the relevant information, i.e. the information strictly necessary to recover the meaning intended by the transmitter or to accomplish a goal. Combining knowledge representation and reasoning tools with machine learning algorithms paves the way to build semantic learning strategies enabling current machine learning algorithms to achieve better interpretation capabilities and contrast adversarial attacks. 6G semantic networks can bring semantic learning mechanisms at the edge of the network and, at the same time, semantic learning can help 6G networks to improve their efficiency and sustainability.

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  1. UAV Cognitive Semantic Communications Enabled by Knowledge Graph for Robust Object Detection

    eess.SP 2025-02 conditional novelty 4.0 of 10

    A knowledge-graph-enhanced semantic communication system for UAV object detection, transmitting compressed features with SNR-adaptive coding, outperforms conventional and deep-learning benchmarks at low SNR on DOTA.

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