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.
翻译:以无线网络代替无线网络是一个新兴的第六代(6G)无线系统使用案例,在使用多模式数据传输方面提出了前所未有的挑战,因为多模式数据传输具有严格的延迟性和可靠性要求。为了使这种无线逆向能够促成无线逆向,我们在本篇文章中提出一个新的语义通信(SC)框架,将该元分解成人文/机械代理特定语义多变量(SMS)。每个代理器储存的SM包括一个语义编码器和一个生成器,利用基因化人工智能(AI)的最新进步。为了提高通信效率,编码器学习多模式数据的语义表达(SRs),而生成器则学会如何操纵它们在当地展示场景和互动。由于这些学习的语义通信(SC)框架偏向于当地环境,其成功取决于在背景中同步兼容的SMM(SM),同时将无线反向反向问题转化为精通性人工智能通信(SMC)问题。基于这一SMC结构,我们提议从一些有希望的算法和信号强化工具,从模型和模拟和模拟模型学习到多式的多式强化游戏。