Rateless DeepJSCC for Broadcast Channels: a Rate-Distortion-Complexity Tradeoff
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910065110286336 |
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| author | Qin, Zijun Huang, Jingxuan Fei, Zesong Ding, Haichuan Shao, Yulin Chen, Xianhao |
| author_facet | Qin, Zijun Huang, Jingxuan Fei, Zesong Ding, Haichuan Shao, Yulin Chen, Xianhao |
| contents | In recent years, numerous data-intensive broadcasting applications have emerged at the wireless edge, calling for a flexible tradeoff between distortion, transmission rate, and processing complexity. While deep learning-based joint source-channel coding (DeepJSCC) has been identified as a potential solution to data-intensive communications, most of these schemes are confined to worst-case solutions, lack adaptive complexity, and are inefficient in broadcast settings. To overcome these limitations, this paper introduces nonlinear transform rateless source-channel coding (NTRSCC), a variable-length JSCC framework for broadcast channels based on rateless codes. In particular, we integrate learned source transformations with physical-layer LT codes, develop unequal protection schemes that exploit decoder side information, and devise approximations to enable end-to-end optimization of rateless parameters. Our framework enables heterogeneous receivers to adaptively adjust their received number of rateless symbols and decoding iterations in belief propagation, thereby achieving a controllable tradeoff between distortion, rate, and decoding complexity. Simulation results demonstrate that the proposed method enhances image broadcast quality under stringent communication and processing budgets over heterogeneous edge devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21616 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Rateless DeepJSCC for Broadcast Channels: a Rate-Distortion-Complexity Tradeoff Qin, Zijun Huang, Jingxuan Fei, Zesong Ding, Haichuan Shao, Yulin Chen, Xianhao Information Theory Machine Learning Signal Processing In recent years, numerous data-intensive broadcasting applications have emerged at the wireless edge, calling for a flexible tradeoff between distortion, transmission rate, and processing complexity. While deep learning-based joint source-channel coding (DeepJSCC) has been identified as a potential solution to data-intensive communications, most of these schemes are confined to worst-case solutions, lack adaptive complexity, and are inefficient in broadcast settings. To overcome these limitations, this paper introduces nonlinear transform rateless source-channel coding (NTRSCC), a variable-length JSCC framework for broadcast channels based on rateless codes. In particular, we integrate learned source transformations with physical-layer LT codes, develop unequal protection schemes that exploit decoder side information, and devise approximations to enable end-to-end optimization of rateless parameters. Our framework enables heterogeneous receivers to adaptively adjust their received number of rateless symbols and decoding iterations in belief propagation, thereby achieving a controllable tradeoff between distortion, rate, and decoding complexity. Simulation results demonstrate that the proposed method enhances image broadcast quality under stringent communication and processing budgets over heterogeneous edge devices. |
| title | Rateless DeepJSCC for Broadcast Channels: a Rate-Distortion-Complexity Tradeoff |
| topic | Information Theory Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2603.21616 |