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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.08978 |
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| _version_ | 1866929627172175872 |
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| author | Pan, Hongzhi Wu, Shengliang Wang, Lingyun Zhu, Yujun Jiang, Weiwei He, Xin |
| author_facet | Pan, Hongzhi Wu, Shengliang Wang, Lingyun Zhu, Yujun Jiang, Weiwei He, Xin |
| contents | To address the challenges of robust data transmission over complex time-varying channels, this paper introduces channel learning and enhanced adaptive reconstruction (CLEAR) strategy for semantic communications. CLEAR integrates deep joint source-channel coding (DeepJSCC) with an adaptive diffusion denoising model (ADDM) to form a unique framework. It leverages a trainable encoder-decoder architecture to encode data into complex semantic codes, which are then transmitted and reconstructed while minimizing distortion, ensuring high semantic fidelity. By addressing multipath effects, frequency-selective fading, phase noise, and Doppler shifts, CLEAR achieves high semantic fidelity and reliable transmission across diverse signal-to-noise ratios (SNRs) and channel conditions. Extensive experiments demonstrate that CLEAR achieves a 2.3 dB gain on peak signal-to-noise ratio (PSNR) over the existing state-of-the-art method, DeepJSCC-V. Furthermore, the results verify that CLEAR is robust against varying channel conditions, particularly in scenarios characterized by high Doppler shifts and strong phase noise. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08978 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CLEAR: Channel Learning and Enhanced Adaptive Reconstruction for Semantic Communication in Complex Time-Varying Environments Pan, Hongzhi Wu, Shengliang Wang, Lingyun Zhu, Yujun Jiang, Weiwei He, Xin Networking and Internet Architecture To address the challenges of robust data transmission over complex time-varying channels, this paper introduces channel learning and enhanced adaptive reconstruction (CLEAR) strategy for semantic communications. CLEAR integrates deep joint source-channel coding (DeepJSCC) with an adaptive diffusion denoising model (ADDM) to form a unique framework. It leverages a trainable encoder-decoder architecture to encode data into complex semantic codes, which are then transmitted and reconstructed while minimizing distortion, ensuring high semantic fidelity. By addressing multipath effects, frequency-selective fading, phase noise, and Doppler shifts, CLEAR achieves high semantic fidelity and reliable transmission across diverse signal-to-noise ratios (SNRs) and channel conditions. Extensive experiments demonstrate that CLEAR achieves a 2.3 dB gain on peak signal-to-noise ratio (PSNR) over the existing state-of-the-art method, DeepJSCC-V. Furthermore, the results verify that CLEAR is robust against varying channel conditions, particularly in scenarios characterized by high Doppler shifts and strong phase noise. |
| title | CLEAR: Channel Learning and Enhanced Adaptive Reconstruction for Semantic Communication in Complex Time-Varying Environments |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2412.08978 |