SCSC: A Novel Standards-Compatible Semantic Communication Framework for Image Transmission
Journal:
arXiv
Published Date:
Jan 6, 2025
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.
