SCSC: A Novel Standards-Compatible Semantic Communication Framework for Image Transmission

TL;DR

This paper introduces a standards-compatible semantic communication framework for image transmission that combines learnable modules with conventional coding, improving efficiency and deployment on legacy systems.

Contribution

It proposes a novel JSCC framework integrating learnable modules with traditional codes, enabling semantic preservation and efficient MIMO transmission compatible with existing standards.

Findings

Saves over 29% of channel bandwidth.

Requires lower complexity than baseline schemes.

Demonstrates strong generalization to unseen datasets.

Abstract

Joint source-channel coding (JSCC) is a promising paradigm for next-generation communication systems, particularly in challenging transmission environments. In this paper, we propose a novel standard-compatible JSCC framework for the transmission of images over multiple-input multiple-output (MIMO) channels. Different from the existing end-to-end AI-based DeepJSCC schemes, our framework consists of learnable modules that enable communication using conventional separate source and channel codes (SSCC), which makes it amenable for easy deployment on legacy systems. Specifically, the learnable modules involve a preprocessing-empowered network (PPEN) for preserving essential semantic information, and a precoder \& combiner-enhanced network (PCEN) for efficient transmission over a resource-constrained MIMO channel. We treat existing compression and channel coding modules as non-trainable blocks. Since the parameters of these modules are non-differentiable, we employ a proxy network that mimics their operations when training the learnable modules. Numerical results demonstrate that our scheme can save more than 29\% of the channel bandwidth, and requires lower complexity compared to the constrained baselines. We also show its generalization capability to unseen datasets and tasks through extensive experiments.