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Metaverse over wireless networks is an emerging use case of the sixthgeneration (6G) wireless systems, posing unprecedented challenges in terms ofits multi-modal data transmissions with stringent latency and reliabilityrequirements. Towards enabling this wireless metaverse, in this article wepropose a novel semantic communication (SC) framework by decomposing themetaverse into human/machine agent-specific semantic multiverses (SMs). An SMstored at each agent comprises a semantic encoder and a generator, leveragingrecent advances in generative artificial intelligence (AI). To improvecommunication efficiency, the encoder learns the semantic representations (SRs)of multi-modal data, while the generator learns how to manipulate them forlocally rendering scenes and interactions in the metaverse. Since these learnedSMs are biased towards local environments, their success hinges onsynchronizing heterogeneous SMs in the background while communicating SRs inthe foreground, turning the wireless metaverse problem into the problem ofsemantic multiverse communication (SMC). Based on this SMC architecture, wepropose several promising algorithmic and analytic tools for modeling anddesigning SMC, ranging from distributed learning and multi-agent reinforcementlearning (MARL) to signaling games and symbolic AI.

Enabling the Wireless Metaverse via Semantic Multiverse Communication