6G Affection-Centric Metaverse: Learn from In-Network Human-Like Sentiment

The forthcoming Metaverse will have to be based on future 6G networks, as 5G/5G-Advanced networks cannot cope with the assumed requirements of the Metaverse, especially regarding latency, robustness of connections, and wireless access throughput. Another essential aspect missing in current networks is understanding the role of human affections in interactions between humans and interfaces accessing the Metaverse. Such affections are hidden in large amounts of human-centric content, semantics, and sentiment-related data. Moreover, existing methods and schemes cannot intelligently analyse human sentiments. Therefore, there is a need to research sentimental analysis to thoroughly comprehend human demands in 6G-driven Metaverse scenarios. With that enhancement, 6G will be able to fulfil the expectation of providing a revolution in interactions between humans and virtual realities.

Therefore, in this talk, I will discuss for the first time the learning-based affection-centric networking (ACN) initiative and architecture for the 6G-enabled Metaverse, which uses artificial intelligence to enable human-like affection analysis at the network layer. First, an affection-centric in-networking sentimental analysis framework is discussed, where the ACN architecture and the heterogeneous affection transformation mechanism are designed. Second, an initiative learning-based in-network human-like sentimental analysis mechanism is developed. This mechanism maps data of devices and humans into hierarchical forms, analyses the emotions of humans in the network layer, and makes affection-centric decisions and predictions. Third, Meta-verse standardisation activities are studied. Finally, the case study and simulation results demonstrate the efficiency of the proposed ACN framework.