Rateless DeepJSCC for Broadcast Channels: a Rate-Distortion-Complexity Tradeoff
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 le...
| Authors | Zijun Qin, Jingxuan Huang, Zesong Fei et al. |
|---|---|
| Primary category | cs.IT |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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