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Towards 6G Digital Twin Channel Using Radio Environment Knowledge Pool
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The digital twin channel (DTC) is crucial for 6G wireless autonomous networks as it replicates the wireless channel fading states in 6G air interface transmissions. It is well known that the physical environment influences channels. A key task for accurately twinning channels in complex 6G scenarios is establishing precise relationships between the environment and the channels. In this article, the radio environment knowledge pool (REKP) is proposed, with its core function being to construct and store as much knowledge between the environment and channels as possible. Firstly, the research progress related to DTC is summarized, and a comparative analysis of these achievements on key indicators in digital twin is conducted, proposing the challenges faced in knowledge construction. Secondly, instructions on how to construct and update REKP are given. Then, a typical case is presented to demonstrate the great potential of REKP in enabling DTC. Finally, how to utilize REKP to address open issues in the 6G wireless communication system is discussed, including enhancing performance, reducing costs, and keeping a trustworthy DTC.
Forward citations
Cited by 3 Pith papers
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Digital Twin Online Channel Modeling: Challenges,Principles, and Applications
The authors propose DTOCM, a four-step framework for real-time digital twin channel modeling in 6G, with a prototype demo showing delay spread and spectral efficiency comparisons.
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A proposed 6G paradigm uses sensed environmental information and AI to predict channels and make proactive transmission decisions, outperforming statistical models in simulations.
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Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities
This overview paper structures the convergence of AI and 6G into three stages and proposes a Quality of AI Service framework for measuring AI services in future networks.
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