Swin Transformer-Based Dynamic Semantic Communication for Multi-User with Different Computing Capacity Article Swipe
Loc X. Nguyen
,
Ye Lin Tun
,
Yan Kyaw Tun
,
Minh N. H. Nguyen
,
Chaoning Zhang
,
Zhu Han
,
Choong Seon Hong
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.03402
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.03402
Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication systems, primarily due to its ability to reduce communication costs. However, little literature has studied its effectiveness in multi-user scenarios, particularly when there are variations in the model architectures used by users and their computing capacities. To address this issue, we explore a semantic communication system that caters to multiple users with different model architectures by using a multi-purpose transmitter at the base station (BS). Specifically, the BS in the proposed framework employs semantic and channel encoders to encode the image for transmission, while the receiver utilizes its local channel and semantic decoder to reconstruct the original image. Our joint source-channel encoder at the BS can effectively extract and compress semantic features for specific users by considering the signal-to-noise ratio (SNR) and computing capacity of the user. Based on the network status, the joint source-channel encoder at the BS can adaptively adjust the length of the transmitted signal. A longer signal ensures more information for high-quality image reconstruction for the user, while a shorter signal helps avoid network congestion. In addition, we propose a hybrid loss function for training, which enhances the perceptual details of reconstructed images. Finally, we conduct a series of extensive evaluations and ablation studies to validate the effectiveness of the proposed system.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.03402
- https://arxiv.org/pdf/2307.03402
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4383860418
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4383860418Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2307.03402Digital Object Identifier
- Title
-
Swin Transformer-Based Dynamic Semantic Communication for Multi-User with Different Computing CapacityWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-07Full publication date if available
- Authors
-
Loc X. Nguyen, Ye Lin Tun, Yan Kyaw Tun, Minh N. H. Nguyen, Chaoning Zhang, Zhu Han, Choong Seon HongList of authors in order
- Landing page
-
https://arxiv.org/abs/2307.03402Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2307.03402Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2307.03402Direct OA link when available
- Concepts
-
Computer science, Encoder, Transmitter, Channel (broadcasting), Computer network, Real-time computing, Computer engineering, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.effectiveness | 38, 223 |
| abstract_inverted_index.multi-purpose | 80 |
| abstract_inverted_index.reconstructed | 207 |
| abstract_inverted_index.transmission, | 104 |
| abstract_inverted_index.reconstruction | 179 |
| abstract_inverted_index.source-channel | 122, 156 |
| abstract_inverted_index.signal-to-noise | 140 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.66641172 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |