Knowledge Enhanced Semantic Communication Receiver

Wang, Bing-Yan; Li, Rongpeng; Zhu, Jianhang; Zhao, Zhifeng; Zhang, Honggang

doi:10.1109/lcomm.2023.3274562

Cited by 10 publications

(2 citation statements)

References 14 publications

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“…Often, what these meanings are or what they represent is not apparent, due to the black-box nature of deep networks. Many of these approaches were inspired by the initial success of DeepSC [23] and its variants [24]- [26], which implement transformer-based architectures and achieve promising results [27]- [29]. However, many works taking this approach (including DeepSC) train the system with the goal of exactly reproducing the initial data [25], [30], i.e., they are still operating at the technical level of communication.…”

Section: A Related Workmentioning

confidence: 99%

Engineering Semantic Communication: A Survey

Wheeler

Natarajan

2023

IEEE Access

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As the global demand for data has continued to rise exponentially, some have begun turning to the idea of semantic communication as a means of efficiently meeting this demand. Pushing beyond the boundaries of conventional communication systems, semantic communication focuses on the accurate recovery of the meaning conveyed from source to receiver, as opposed to the accurate recovery of transmitted symbols. In this survey, we aim to provide a comprehensive view of the history and current state of semantic communication and the techniques for engineering this higher level of communication. A survey of the current literature reveals four broad approaches to engineering semantic communication. We term the earliest of these approaches classical semantic information, which seeks to extend informationtheoretic results to include semantic information. A second approach makes use of knowledge graphs to achieve semantic communication, and a third utilizes the power of modern deep learning techniques to facilitate this communication. The fourth approach focuses on the significance of information, rather than its meaning, to achieve efficient, goal-oriented communication. We discuss each of these four approaches and their corresponding studies in detail, and provide some challenges and opportunities that pertain to each approach. Finally, we introduce a novel approach to semantic communication, which we term contextbased semantic communication. Inspired by the way in which humans naturally communicate with one another, this context-based approach provides a general, optimization-based design framework for semantic communication systems. Together, this survey provides a useful guide for the design and implementation of semantic communication systems.

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Section: A Related Workmentioning

confidence: 99%

Engineering Semantic Communication: A Survey

Wheeler

Natarajan

2023

IEEE Access

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“…It has been adapted to numerous other problems, e.g., speech transmission [ 22 , 23 ] and multi-user transmission with multi-modal data [ 24 ]. Even knowledge graphs, i.e., a prior knowledge base, were incorporated into the transformer-based AE design to improve inference at the receiver side and, thus, text recovery [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

Semantic Information Recovery in Wireless Networks

Beck

Bockelmann

Dekorsy

2023

Sensors

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Motivated by the recent success of Machine Learning (ML) tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shannon’s classic design paradigm by aiming to transmit the meaning of a message, i.e., semantics, rather than its exact version and, thus, enables savings in information rate. In this work, we extend the fundamental approach from Basu et al. for modeling semantics to the complete communications Markov chain. Thus, we model semantics by means of hidden random variables and define the semantic communication task as the data-reduced and reliable transmission of messages over a communication channel such that semantics is best preserved. We consider this task as an end-to-end Information Bottleneck problem, enabling compression while preserving relevant information. As a solution approach, we propose the ML-based semantic communication system SINFONY and use it for a distributed multipoint scenario; SINFONY communicates the meaning behind multiple messages that are observed at different senders to a single receiver for semantic recovery. We analyze SINFONY by processing images as message examples. Numerical results reveal a tremendous rate-normalized SNR shift up to 20 dB compared to classically designed communication systems.

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Interplay of Semantic Communication and Knowledge Learning

Ni,

Wang,

et al. 2024

Wireless Semantic Communications

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Knowledge Enhanced Semantic Communication Receiver

Cited by 10 publications

References 14 publications

Engineering Semantic Communication: A Survey

Engineering Semantic Communication: A Survey

Semantic Information Recovery in Wireless Networks

Interplay of Semantic Communication and Knowledge Learning

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