A review of the DSP hardware architecture for URLLC scenarios and the collaboration of AI with DSP, terahertz processing, and 6G extreme URLLC architecture is envisioned, providing systematic technical references for researchers.
This survey formally categorizes state-of-the-art DTN architectures into passive monitoring twins and active control twins, and provides an in-depth evaluation of their underlying enabling technologies, specifically ray-tracing, reconfigurable intelligent surfaces, artificial intelligence, and mobile edge computing.
This paper proposes a data-driven solution leveraging Digital Twin (DT) modeling to enhance WTSN management, and demonstrates that delay prediction through WTSN-DT reduces application link latency by up to 96% at the 90th percentile in a real Wi-Fi TSN setting, relative to fixed time slot allocation.
An Artificial Intelligence (AI) centric network management framework that combines 5G radio access and core capabilities, Software-Defined Networking (SDN) for centralized and programmable control and Multi-access Edge Computing (MEC) for localized and low latency processing is introduced.
Overall, AI-native design makes 6G not just faster than 5G but autonomously adaptive and resilient, marking a paradigm shift toward networks that are inherently intelligent, sustainable, and capable of supporting future real-time digital ecosystems.
A practical AI-enabled operating framework to convert streaming measurements to failure probability, time-to-impact, root-cause ranking, optimization recommendations and governed automation actions is proposed.
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