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6G MailBox Theory: Cognitive Information Carrier to Enable Distributed Algorithm Embedding for Intelligence Networking

Published

Author(s)

Yixue Hao, Yiming Miao, Min Chen, Hamid Gharavi, Victor Leung

Abstract

With the rapid development of 5G communications, enhanced mobile broadband, massive machine type communications and ultra-reliable low latency communications are widely supported. However, a 5G communication system is still based on Shannon's information theory, while the meaning and value of information itself are not taken into account in the process of transmission. Therefore, it is difficult to meet the requirements of intelligence, customization and value transmission of 6G networks. In order to solve the above challenges, we propose a 6G mailbox theory, namely a cognitive information carrier to enable distributed algorithm embedding for intelligence networking. Based on Mailbox, 6G network will form an intelligent agent with self-organization, self-learning, self-adaptation and continuous evolution capabilities. With the intelligent agent, redundant transmission of data can be reduced while the value transmission of information can be improved. Then, the features of mailbox principle are introduced, including polarity, traceability, dynamics, convergence, figurability and dependence. Furthermore, key technologies with which value transmission of information can be realized are introduced, including knowledge graph, distributed learning and blockchain. Finally, we establish a cognitive communication system assisted by deep learning. The experimental results show that, compared with a traditional communication system, our communication system performs less data transmission quantity and error.
Citation
Big Data and Cognitive Computing
Volume
5
Issue
4

Keywords

Cognitive Computing, Distribute Algorithm, 6G Network

Citation

Hao, Y. , Miao, Y. , Chen, M. , Gharavi, H. and Leung, V. (2021), 6G MailBox Theory: Cognitive Information Carrier to Enable Distributed Algorithm Embedding for Intelligence Networking, Big Data and Cognitive Computing, [online], https://doi.org/10.3390/bdcc5040056, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933512 (Accessed June 20, 2024)

Issues

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Created October 16, 2021, Updated November 29, 2022