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Title: On Softwarization of Intelligence in 6G Networks for Ultra-Fast Optimal Policy Selection: Challenges and Opportunities
The emerging Sixth Generation (6G) communication networks promising 100 to 1000 Gb/s rates and ultra-low latency (1 millisecond) are anticipated to have native, embedded Artificial Intelligence (AI) capability to support a myriad of services, such as Holographic Type Communications (HTC), tactile Internet, remote surgery, etc. However, these services require ultra-reliability, which is highly impacted by the dynamically changing environment of 6G heterogeneous tiny cells, whereby static AI solutions fitting all scenarios and devices are impractical. Hence, this article introduces a novel concept called the softwarization of intelligence in 6G networks to select the most ideal, ultra-fast optimal policy based on the highly varying channel conditions, traffic demand, user mobility, and so forth. Our envisioned concept is exemplified in a Multi-Armed Bandit (MAB) framework and evaluated within a use case of two simultaneous scenarios (i.e., Neighbor Discovery and Selection (NDS) in a Device-to-Device (D2D) network and aerial gateway selection in an Unmanned Aerial Vehicle (UAV)-based under-served area network). Furthermore, our concept is evaluated through extensive computer-based simulations that indicate encouraging performance. Finally, related challenges and future directions are highlighted.  more » « less
Award ID(s):
2210252
PAR ID:
10515874
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
IEEE Network
Volume:
37
Issue:
2
ISSN:
0890-8044
Page Range / eLocation ID:
190 to 197
Subject(s) / Keyword(s):
6G mobile communication Artificial intelligence Optimization Computational modeling Vehicle dynamics Device-to-device communication Data models
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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