  From the perspective of joint source-channel coding (JSCC), there has been
significant research on utilizing semantic communication, which inherently
possesses analog characteristics, within digital device environments. However,
a single-model approach that operates modulation-agnostically across various
digital modulation orders has not yet been established. This article presents
the first attempt at such an approach by proposing a universal joint
source-channel coding (uJSCC) system that utilizes a single-model
encoder-decoder pair and trained vector quantization (VQ) codebooks. To support
various modulation orders within a single model, the operation of every neural
network (NN)-based module in the uJSCC system requires the selection of
modulation orders according to signal-to-noise ratio (SNR) boundaries. To
address the challenge of unequal output statistics from shared parameters
across NN layers, we integrate multiple batch normalization (BN) layers,
selected based on modulation order, after each NN layer. This integration
occurs with minimal impact on the overall model size. Through a comprehensive
series of experiments, we validate that this modulation-agnostic semantic
communication framework demonstrates superiority over existing digital semantic
communication approaches in terms of model complexity, communication
efficiency, and task effectiveness.
