提出可自适应调整的率无关联合编码框架，提升边缘广播画质与效率。
Rateless DeepJSCC for Broadcast Channels: a Rate-Distortion-Complexity Tradeoff
- 融合学习型变换与物理层LT码，构建可变长度编码框架
- 在异构设备上实现失真、速率与复杂度的可控权衡
- 适合资源差异大的边缘广播场景，支持端到端优化
近年来，大量数据密集型无线边缘广播应用涌现，亟需在失真、传输速率和处理复杂度之间实现灵活权衡。尽管基于深度学习的联合源信道编码（DeepJSCC）被视为解决数据密集通信的潜在方案，但多数方法局限于最坏情况下的解法，缺乏自适应复杂度，且在广播场景中效率低下。为此，本文提出非线性变换率无关源信道编码（NTRSCC），一种基于率无关码的广播信道可变长度JSCC框架。具体而言，将学习型源变换与物理层LT码结合，设计利用解码器侧信息的不等保护机制，并引入近似方法以实现率无关参数的端到端优化。该框架使异构接收端可自适应调整接收到的率无关符号数量及置信传播中的解码迭代次数，从而实现失真、速率与解码复杂度之间的可控权衡。仿真结果表明，在严苛通信与处理预算下，所提方法显著提升了异构边缘设备上的图像广播质量。
原文摘要 · Abstract (English)
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.
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