Development of A Fully Data-Driven Artificial Intelligence and Deep Learning for URLLC Application in 6G Wireless Systems: A Survey
The full future of the sixth generation will develop a fully data-driven that provide terabit rate per second, and adopt an average of 1000+ massive number of connections per person in 10 years 2030 virtually instantaneously. Data-driven for ultra-reliable and low latency communication is a new serv...
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Zusammenfassung: | The full future of the sixth generation will develop a fully data-driven that
provide terabit rate per second, and adopt an average of 1000+ massive number
of connections per person in 10 years 2030 virtually instantaneously.
Data-driven for ultra-reliable and low latency communication is a new service
paradigm provided by a new application of future sixth-generation wireless
communication and network architecture, involving 100+ Gbps data rates with one
millisecond latency. The key constraint is the amount of computing power
available to spread massive data and well-designed artificial neural networks.
Artificial Intelligence provides a new technique to design wireless networks by
apply learning, predicting, and make decisions to manage the stream of big data
training individuals, which provides more the capacity to transform that expert
learning to develop the performance of wireless networks. We study the
developing technologies that will be the driving force are artificial
intelligence, communication systems to guarantee low latency. This paper aims
to discuss the efficiency of the developing network and alleviate the great
challenge for application scenarios and study Holographic radio, enhanced
wireless channel coding, enormous Internet of Things integration, and haptic
communication for virtual and augmented reality provide new services on the 6G
network. Furthermore, improving a multi-level architecture for ultra-reliable
and low latency in deep Learning allows for data-driven AI and 6G networks for
device intelligence, as well as allowing innovations based on effective
learning capabilities. These difficulties must be solved in order to meet the
needs of future smart networks. Furthermore, this research categorizes various
unexplored research gaps between machine learning and the sixth generation. |
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DOI: | 10.48550/arxiv.2108.10076 |