Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Prediction

Girgis, Abanoub M.; Seo, Hyowoon; Park, Jihong; Bennis, Mehdi; Choi, Jin‐Ho

doi:10.48550/arxiv.2104.08109

Cited by 1 publication

(3 citation statements)

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“…In addition to distributed learning, the authors in [70] focus on the remote monitoring scenario with wireless connectivity. Since the system dynamics are always non-linear and highdimensional, the correction of distortions caused by wireless transmission is hard to achieve without a full understanding of the system dynamics.…”

Section: A Accurate Sementioning

confidence: 99%

“…In Koopman operator theory [203], a finite-dimensional non-linear system can be transformed into an infinite-dimensional linear system based on a Koopman operator (usually represented as a matrix) and its associated eigenfunctions. In particular, the Koopman operator helps to shift the viewpoint from the system state space to the observable space [70], [204], which can be regarded as a semantic representation and determined by the eigenfunctions of the Koopman operator. However, deriving the Koopman operator and corresponding eigenfunctions mathematically is also unattainable.…”

Section: A Accurate Sementioning

confidence: 99%

“…Besides the methods mentioned in [59], [68]- [70], there are other scenarios with straightforward semantic information, wherein the extraction of semantic information can be achieved during the optimization of system performance. For instance, in [63], the authors propose a video semantics based resource allocation for object detection tasks, wherein the SE is performed by determining the quantitative parameters, which characterizes the compression ratio of the video clips in high efficiency video coding [62].…”

Section: A Accurate Sementioning

confidence: 99%

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Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Yang¹,

Du²,

Liew³

et al. 2022

Preprint

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With the increasing demand for intelligent services, the sixth-generation (6G) wireless networks will shift from a traditional architecture that focuses solely on high transmission rate to a new architecture that is based on the intelligent connection of everything. Semantic communication (SemCom), a revolutionary architecture that integrates user as well as application requirements and meaning of information into the data processing and transmission, is predicted to become a new core paradigm in 6G. While SemCom is expected to progress beyond the classical Shannon paradigm, several obstacles need to be overcome on the way to a SemCom-enabled smart wireless Internet. In this paper, we first highlight the motivations and compelling reasons of SemCom in 6G. Then, we outline the major 6G visions and key enabler techniques which lay the foundation of SemCom. Meanwhile, we highlight some benefits of SemComempowered 6G and present a SemCom-native 6G network architecture. Next, we show the evolution of SemCom from its introduction to classical SemCom related theory and modern AIenabled SemCom. Following that, focusing on modern SemCom, we classify SemCom into three categories, i.e., semantic-oriented communication, goal-oriented communication, and semanticaware communication, and introduce three types of semantic metrics. We then discuss the applications, the challenges and technologies related to semantics and communication. Finally, we introduce future research opportunities. In a nutshell, this paper investigates the fundamentals of SemCom, its applications in 6G networks, and the existing challenges and open issues for further direction.

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Section: A Accurate Sementioning

confidence: 99%