AI Strategy and Concepts Bibliography

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Liu, Z., Lee, H., Khyam, M. O., He, J., Pesch, D., & Moessner, K., et al. (2020). 6g for vehicle-to-everything (v2x) communications: Enabling technologies, challenges, and opportunities. arXiv preprint arXiv:2012.07753, 
Added by: SijanLibrarian (2021-09-27 13:40:36)   Last edited by: SijanLibrarian (2021-09-27 13:43:02)
Resource type: Journal Article
BibTeX citation key: Liu2020a
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Categories: Artificial Intelligence, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Decision Theory, Engineering, General, Innovation, Military Science
Subcategories: 5G, Augmented cognition, Autonomous systems, Big data, Blockchain, Cloud computing, Command and control, Cross-domain deterrence, Decision making, Drones, Edge AI, Fog computing, Internet of things, JADC2, Machine intelligence, Machine learning, Mosaic warfare, Networked forces, Quantum computing, Robotics, Space Force
Creators: He, Khyam, Lee, Liu, Moessner, others, Pesch, Poor, Saad
Collection: arXiv preprint arXiv:2012.07753
Views: 49/51
Views index: 23%
Popularity index: 5.75%
We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, as well as a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network which should be extremely intelligent and capable of concurrently supporting hyper-fast, ultra-reliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning will play an instrumental role for advanced vehicular communication and networking. To this end, we provide an overview on the recent advances of machine learning in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.
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