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- W4387415360 abstract "The vision for 6G networks is to offer pervasive intelligence and internet of intelligence, in which the networks natively support artificial intelligence (AI), empower smart applications and scenarios in various fields, and create a ubiquitous-intelligence world. In this vision, the traditional session-oriented architecture cannot achieve flexible per-user customization, ultimate performance, security and reliability required by future AI services. In addition, users’ requirements for personalized AI services may become a key feature in the near future. However, the traditional AI deployment based on cloud/mobile edge computing (MEC) has limitations such as low throughput, long delay, poor privacy, and high carbon emissions, resulting in the inability to provide personalized quality of experience (QoE) assurance. By integrating AI in the network, the network AI has more advantages than cloud/MEC AI, such as better QoS assurance, lower latency, less transmission and computing overhead, and stronger security and privacy. Therefore, this article proposes the task-oriented native-AI network architecture (TONA), to natively support the network AI. By introducing task control and quality of AI services (QoAIS) assurance mechanisms at the control layer of 6G, the TONA can achieve the finest service granularity at the task level for guaranteeing every user’s personalized QoE." @default.
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- W4387415360 date "2023-01-01" @default.
- W4387415360 modified "2023-10-08" @default.
- W4387415360 title "Task-Oriented 6G Native-AI Network Architecture" @default.
- W4387415360 doi "https://doi.org/10.1109/mnet.2023.3321464" @default.
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