Deep Learning Enabled Semantic Communication Systems

By Huiqiang Xie, Zhijin Qin, Geoffrey Y. Li, Biing-Hwang Fred Juang. IEEE Transactions on Signal Processing, 2020

1,598 citations recorded by the source corpus.

Recently, deep learned enabled end-to-end communication systems have been developed to merge all physical layer blocks in the traditional communication systems, which make joint transceiver optimization possible. Powered by deep learning, natural language processing has achieved great success in analyzing and understanding a large amount of language texts. Inspired by research results in both areas, we aim to provide a new view on communication systems from the semantic level. Particularly, we propose a deep learning based semantic communication system, named DeepSC, for text transmission. Based on the Transformer, the DeepSC aims at maximizing the system capacity and minimizing the semantic errors by recovering the meaning of sentences, rather than bit- or symbol-errors in traditional communications. Moreover, transfer learning is used to ensure the DeepSC applicable to different communication environments and to accelerate the model training process. To justify the performance of semantic communications accurately, we also initialize a new metric, named sentence similarity. Compared with the traditional communication system without considering semantic information exchange, the proposed DeepSC is more robust to channel variation and is able to achieve better performance, especially in the low signal-to-noise (SNR) regime, as demonstrated by the extensive simulation results.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f9314fd99be5f2b1b3efcfab87197d578160d553

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Citations in the corpus

Toward Semantic Communications: Deep Learning-Based Image Semantic Coding (2022, background): “Recent works mainly focus on semantic communications for text/speech modalities [2], [3], [4], [5], and therefore can be categorize as text semantic communications(TSC).”
Resource Allocation for Text Semantic Communications (2022, positive): “Particularly, the DL-enabled semantic communication system (DeepSC) [3] and its several variants [4], [13] can effectively extract the semantic information from text and successfully deliver the meaning to the receiver.”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “For example, the DL based end-to-end semantic communication system was developed in [21] via joint semantic source and wireless channel coding for recovering the meaning of sentences instead of the original transmitted data samples.”
6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications (2020, background): “Interesting examples already incorporating semantic aspects in text transmission are [35] and [42].”
Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges (2022, neutral): “The authors in [138] first develop models with semantic encoder from [37] and Reed Solomon (RS) channel coding [139] with HARQ.”
A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and Challenges (2022, background): “In [142], the authors propose a metric called sentence similarity to measure the semantic error of transmitted sentences in semantic communication systems.”
Semantic Communications: Principles and Challenges (2021, methodology): “which is the loss function designed in [12] to be detailed in Section VI.”
A Lite Distributed Semantic Communication System for Internet of Things (2020, methodology): “In order to understand texts better and apply it in dynamic environments, Xie et al. [17] developed a semantic communication system based on Transformer, named DeepSC, which clariﬁes the concepts of semantic information and semantic error at the sentence-level for the ﬁrst time.”
Task-Oriented Multi-User Semantic Communications (2021, background): “With the depth exploration in the semantic communications, Xie et al. [10] developed more powerful joint semantic-channel coding, named DeepSC, to encode text information into various length over complex channels.”
Semantic Communication Systems for Speech Transmission (2021, background): “Note that transfer learning can be adopted as a promising technique to yield efficient retraining task when coping with dynamic environment [24].”
Federated Learning for 6G: Applications, Challenges, and Opportunities (2021, background): “Semantic communication is similar to the communication that takes place in the human brain, where the difference between the meaning of transmitted symbols and that of recovered ones is correlated [60].”