Optimizing Multi-User Semantic Communication via Transfer Learning and Knowledge Distillation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909476663066624 |
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| author | Nguyen, Loc X. Kim, Kitae Tun, Ye Lin Hassan, Sheikh Salman Tun, Yan Kyaw Han, Zhu Hong, Choong Seon |
| author_facet | Nguyen, Loc X. Kim, Kitae Tun, Ye Lin Hassan, Sheikh Salman Tun, Yan Kyaw Han, Zhu Hong, Choong Seon |
| contents | Semantic communication, notable for ensuring quality of service by jointly optimizing source and channel coding, effectively extracts data semantics, reduces transmission length, and mitigates channel noise. However, most studies overlook multi-user scenarios and resource availability, limiting real-world application. This paper addresses this gap by focusing on downlink communication from a base station to multiple users with varying computing capacities. Users employ variants of Swin transformer models for source decoding and a simple architecture for channel decoding. We propose a novel training regimen, incorporating transfer learning and knowledge distillation to improve low-computing users' performance. Extensive simulations validate the proposed methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03773 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing Multi-User Semantic Communication via Transfer Learning and Knowledge Distillation Nguyen, Loc X. Kim, Kitae Tun, Ye Lin Hassan, Sheikh Salman Tun, Yan Kyaw Han, Zhu Hong, Choong Seon Information Theory Semantic communication, notable for ensuring quality of service by jointly optimizing source and channel coding, effectively extracts data semantics, reduces transmission length, and mitigates channel noise. However, most studies overlook multi-user scenarios and resource availability, limiting real-world application. This paper addresses this gap by focusing on downlink communication from a base station to multiple users with varying computing capacities. Users employ variants of Swin transformer models for source decoding and a simple architecture for channel decoding. We propose a novel training regimen, incorporating transfer learning and knowledge distillation to improve low-computing users' performance. Extensive simulations validate the proposed methods. |
| title | Optimizing Multi-User Semantic Communication via Transfer Learning and Knowledge Distillation |
| topic | Information Theory |
| url | https://arxiv.org/abs/2406.03773 |