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Semantic Data Sourcing for 6G Edge Intelligence | | Kaibin Huang
; Qiao Lan
; Zhiyan Liu
; Lin Yang
; | | Date: |
1 Jan 2023 | | Abstract: | As a new function of 6G networks, edge intelligence refers to the ubiquitous
deployment of machine learning and artificial intelligence (AI) algorithms at
the network edge to empower many emerging applications ranging from sensing to
auto-pilot. To support relevant use cases, including sensing, edge learning,
and edge inference, all require transmission of high-dimensional data or AI
models over the air. To overcome the bottleneck, we propose a novel framework
of SEMantic DAta Sourcing (SEMDAS) for locating semantically matched data
sources to efficiently enable edge-intelligence operations. The comprehensive
framework comprises new architecture, protocol, semantic matching techniques,
and design principles for task-oriented wireless techniques. As the key
component of SEMDAS, we discuss a set of machine learning based semantic
matching techniques targeting different edge-intelligence use cases. Moreover,
for designing task-oriented wireless techniques, we discuss different tradeoffs
in SEMDAS systems, propose the new concept of joint semantics-and-channel
matching, and point to a number of research opportunities. The SEMDAS framework
not only overcomes the said communication bottleneck but also addresses other
networking issues including long-distance transmission, sparse connectivity,
high-speed mobility, link disruptions, and security. In addition, experimental
results using a real dataset are presented to demonstrate the performance gain
of SEMDAS. | | Source: | arXiv, 2301.00403 | | Services: | Forum | Review | PDF | Favorites | |
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