Enabling the Wireless Metaverse via Semantic Multiverse Communication
- URL: http://arxiv.org/abs/2212.06908v1
- Date: Tue, 13 Dec 2022 21:21:07 GMT
- Title: Enabling the Wireless Metaverse via Semantic Multiverse Communication
- Authors: Jihong Park, Jinho Choi, Seong-Lyun Kim, Mehdi Bennis
- Abstract summary: Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems.
We propose a novel semantic communication framework by decomposing the metaverse into human/machine agent-specific semantic multiverses (SMs)
An SM stored at each agent comprises a semantic encoder and a generator, leveraging recent advances in generative artificial intelligence (AI)
- Score: 82.47169682083806
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Metaverse over wireless networks is an emerging use case of the sixth
generation (6G) wireless systems, posing unprecedented challenges in terms of
its multi-modal data transmissions with stringent latency and reliability
requirements. Towards enabling this wireless metaverse, in this article we
propose a novel semantic communication (SC) framework by decomposing the
metaverse into human/machine agent-specific semantic multiverses (SMs). An SM
stored at each agent comprises a semantic encoder and a generator, leveraging
recent advances in generative artificial intelligence (AI). To improve
communication efficiency, the encoder learns the semantic representations (SRs)
of multi-modal data, while the generator learns how to manipulate them for
locally rendering scenes and interactions in the metaverse. Since these learned
SMs are biased towards local environments, their success hinges on
synchronizing heterogeneous SMs in the background while communicating SRs in
the foreground, turning the wireless metaverse problem into the problem of
semantic multiverse communication (SMC). Based on this SMC architecture, we
propose several promising algorithmic and analytic tools for modeling and
designing SMC, ranging from distributed learning and multi-agent reinforcement
learning (MARL) to signaling games and symbolic AI.
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