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Industry 5.0 is Coming: A Survey on Intelligent NextG Wireless Networks as Technological Enablers

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arxiv 2205.09084 v1 pith:BWTTJXZG submitted 2022-05-18 cs.NI eess.SP

classification cs.NIeess.SP
keywords industryvisionemergingenablersintelligencenetworkstechnologicalcobots
verification ladder T0 review T1 audit T2 compute T3 formal
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Industry 5.0 vision, a step toward the next industrial revolution and enhancement to Industry 4.0, envisioned the new goals of resilient, sustainable, and human-centric approaches in diverse emerging applications, e.g., factories-of-the-future, digital society. The vision seeks to leverage human intelligence and creativity in nexus with intelligent, efficient, and reliable cognitive collaborating robots (cobots) to achieve zero waste, zerodefect, and mass customization-based manufacturing solutions. However, the vision requires the merging of cyber-physical worlds through utilizing Industry 5.0 technological enablers, e.g., cognitive cobots, person-centric artificial intelligence (AI), cyberphysical systems, digital twins, hyperconverged data storage and computing, communication infrastructure, and others. In this regard, the convergence of the emerging computational intelligence (CI) paradigm and next-generation wireless networks (NGWNs) can fulfill the stringent communication and computation requirements of the technological enablers in the Industry 5.0 vision, which is the aim of this survey-based tutorial. In this article, we address this issue by reviewing and analyzing current emerging concepts and technologies, e.g., CI tools and frameworks, network-in-box architecture, open radio access networks, softwarized service architectures, potential enabling services, and others, essential for designing the objectives of CINGWNs to fulfill the Industry 5.0 vision requirements. Finally, we provide a list of lessons learned from our detailed review, research challenges, and open issues that should be addressed in CI-NGWNs to realize Industry 5.0.

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  1. BERT4MIMO: A Foundation Model using BERT Architecture for Massive MIMO Channel State Information Prediction

    cs.IT 2025-01 reject novelty 3.0 of 10

    A BERT-inspired transformer is trained to reconstruct masked synthetic massive MIMO channel state information, with reported MSE far below simple linear and MLP baselines.

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