Task-Oriented Multi-User Semantic Communications

While semantic communications have shown the potential in the case of single-modal single-users, its applications to the multi-user scenario remain limited. In this paper, we investigate deep learning (DL) based multi-user semantic communication systems for transmitting single-modal data and multimo...

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Vydáno v:	IEEE journal on selected areas in communications Ročník 40; číslo 9; s. 2584 - 2597
Hlavní autoři:	Xie, Huiqiang, Qin, Zhijin, Tao, Xiaoming, Letaief, Khaled B.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Coders Communications systems Deep learning Image retrieval Machine translation Modal data multi-user communications multimodal fusion Receivers Robustness (mathematics) semantic communications Semantics Sentences Task analysis transformer Transformers Transmitters
ISSN:	0733-8716, 1558-0008
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Abstract	While semantic communications have shown the potential in the case of single-modal single-users, its applications to the multi-user scenario remain limited. In this paper, we investigate deep learning (DL) based multi-user semantic communication systems for transmitting single-modal data and multimodal data, respectively. We adopt three intelligent tasks, including, image retrieval, machine translation, and visual question answering (VQA) as the transmission goal of semantic communication systems. We propose a Transformer based framework to unify the structure of transmitters for different tasks. For the single-modal multi-user system, we propose two Transformer based models, named, DeepSC-IR and DeepSC-MT, to perform image retrieval and machine translation, respectively. In this case, DeepSC-IR is trained to optimize the distance in embedding space between images and DeepSC-MT is trained to minimize the semantic errors by recovering the semantic meaning of sentences. For the multimodal multi-user system, we develop a Transformer enabled model, named, DeepSC-VQA, for the VQA task by extracting text-image information at the transmitters and fusing it at the receiver. In particular, a novel layer-wise Transformer is designed to help fuse multimodal data by adding connection between each of the encoder and decoder layers. Numerical results show that the proposed models are superior to traditional communications in terms of the robustness to channels, computational complexity, transmission delay, and the task-execution performance at various task-specific metrics.
AbstractList	While semantic communications have shown the potential in the case of single-modal single-users, its applications to the multi-user scenario remain limited. In this paper, we investigate deep learning (DL) based multi-user semantic communication systems for transmitting single-modal data and multimodal data, respectively. We adopt three intelligent tasks, including, image retrieval, machine translation, and visual question answering (VQA) as the transmission goal of semantic communication systems. We propose a Transformer based framework to unify the structure of transmitters for different tasks. For the single-modal multi-user system, we propose two Transformer based models, named, DeepSC-IR and DeepSC-MT, to perform image retrieval and machine translation, respectively. In this case, DeepSC-IR is trained to optimize the distance in embedding space between images and DeepSC-MT is trained to minimize the semantic errors by recovering the semantic meaning of sentences. For the multimodal multi-user system, we develop a Transformer enabled model, named, DeepSC-VQA, for the VQA task by extracting text-image information at the transmitters and fusing it at the receiver. In particular, a novel layer-wise Transformer is designed to help fuse multimodal data by adding connection between each of the encoder and decoder layers. Numerical results show that the proposed models are superior to traditional communications in terms of the robustness to channels, computational complexity, transmission delay, and the task-execution performance at various task-specific metrics.
Author	Qin, Zhijin Letaief, Khaled B. Tao, Xiaoming Xie, Huiqiang
Author_xml	– sequence: 1 givenname: Huiqiang orcidid: 0000-0001-9905-6319 surname: Xie fullname: Xie, Huiqiang email: h.xie@qmul.ac.uk organization: School of Electronic Engineering and Computer Science, Queen Mary University of London, London, U.K – sequence: 2 givenname: Zhijin orcidid: 0000-0002-8507-3975 surname: Qin fullname: Qin, Zhijin email: z.qin@qmul.ac.uk organization: School of Electronic Engineering and Computer Science, Queen Mary University of London, London, U.K – sequence: 3 givenname: Xiaoming orcidid: 0000-0002-8763-9338 surname: Tao fullname: Tao, Xiaoming email: taoxm@tsinghua.edu.cn organization: Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China – sequence: 4 givenname: Khaled B. orcidid: 0000-0003-2519-6401 surname: Letaief fullname: Letaief, Khaled B. email: eekhaled@ust.hk organization: Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
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Snippet	While semantic communications have shown the potential in the case of single-modal single-users, its applications to the multi-user scenario remain limited. In...
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StartPage	2584
SubjectTerms	Coders Communications systems Deep learning Image retrieval Machine translation Modal data multi-user communications multimodal fusion Receivers Robustness (mathematics) semantic communications Semantics Sentences Task analysis transformer Transformers Transmitters
Title	Task-Oriented Multi-User Semantic Communications
URI	https://ieeexplore.ieee.org/document/9830752 https://www.proquest.com/docview/2704097924
Volume	40
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