Multi-level Reliability Interface for Semantic Communications over Wireless Networks

TL;DR

This paper introduces a multi-level reliability interface for semantic communications over wireless networks, enabling semi-JSCC that improves end-to-end distortion and robustness without requiring fully joint source-channel coding.

Contribution

It proposes a novel multi-level reliability interface that allows separate, sequential design of source and channel mappings, bridging the gap between deep learning JSCC and practical network deployment.

Findings

Achieves lower end-to-end distortion compared to traditional methods.

Provides graceful degradation of distortion with channel quality.

Enables semi-JSCC in practical wireless networks.

Abstract

Semantic communication, when examined through the lens of joint source-channel coding (JSCC), maps source messages directly into channel input symbols, where the measure of success is defined by end-to-end distortion rather than traditional metrics such as block error rate. Previous studies have shown significant improvements achieved through deep learning (DL)-driven JSCC compared to traditional separate source and channel coding. However, JSCC is impractical in existing communication networks, where application and network providers are typically different entities connected over general-purpose TCP/IP links. In this paper, we propose designing the source and channel mappings separately and sequentially via a novel multi-level reliability interface. This conceptual interface enables semi-JSCC at both the learned source and channel mappers and achieves many of the gains observed in existing DL-based JSCC work (which would require a fully joint design between the application and the network), such as lower end-to-end distortion and graceful degradation of distortion with channel quality. We believe this work represents an important step towards realizing semantic communications in wireless networks.