  Semantic communication serves as a novel paradigm and attracts the broad
interest of researchers. One critical aspect of it is the multi-user semantic
communication theory, which can further promote its application to the
practical network environment. While most existing works focused on the design
of end-to-end single-user semantic transmission, a novel non-orthogonal
multiple access (NOMA)-based multi-user semantic communication system named
NOMASC is proposed in this paper. The proposed system can support semantic
tranmission of multiple users with diverse modalities of source information. To
avoid high demand for hardware, an asymmetric quantizer is employed at the end
of the semantic encoder for discretizing the continuous full-resolution
semantic feature. In addition, a neural network model is proposed for mapping
the discrete feature into self-learned symbols and accomplishing intelligent
multi-user detection (MUD) at the receiver. Simulation results demonstrate that
the proposed system holds good performance in non-orthogonal transmission of
multiple user signals and outperforms the other methods, especially at
low-to-medium SNRs. Moreover, it has high robustness under various simulation
settings and mismatched test scenarios.
