Rateless DeepJSCC for Broadcast Channels: a Rate-Distortion-Complexity Tradeoff

TL;DR

This paper introduces a flexible rateless joint source-channel coding framework for broadcast channels that adapts to heterogeneous receivers, balancing distortion, rate, and complexity for improved image broadcast quality.

Contribution

It proposes a novel nonlinear transform rateless source-channel coding scheme that integrates learned transformations with LT codes and enables adaptive complexity control.

Findings

Enhances image broadcast quality under strict communication constraints.

Allows receivers to adjust received symbols and decoding iterations adaptively.

Achieves a controllable tradeoff between distortion, rate, and decoding complexity.

Abstract

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.