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Semantic communication is a new paradigm that exploits deep learning modelsto enable end-to-end communications processes, and recent studies have shownthat it can achieve better noise resiliency compared with traditionalcommunication schemes in a low signal-to-noise (SNR) regime. To achievemultiple access in semantic communication, we propose a deep learning-basedmultiple access (DeepMA) method by training semantic communication models withthe abilities of joint source-channel coding (JSCC) and orthogonal signalmodulation. DeepMA is achieved by a DeepMA network (DMANet), which is comprisedof several independent encoder-decoder pairs (EDPs), and the DeepMA encoderscan encode the input data as mutually orthogonal semantic symbol vectors (SSVs)such that the DeepMA decoders can detect and recover their own target data froma received mixed SSV (MSSV) superposed by multiple SSV components transmittedfrom different encoders. We describe frameworks of DeepMA in wirelessdevice-to-device (D2D), downlink, and uplink channel multiplexing scenarios,along with the training algorithm. We evaluate the performance of the proposedDeepMA in wireless image transmission tasks and compare its performance withthe attention module-based deep JSCC (ADJSCC) method and conventionalcommunication schemes using better portable graphics (BPG) and Low-densityparity-check code (LDPC). The results obtained show that the proposed DeepMAcan achieve effective, flexible, and privacy-preserving channel multiplexingprocess, and demonstrate that our proposed DeepMA approach can yield comparablebandwidth efficiency compared with conventional multiple access schemes.

DeepMA: End-to-end Deep Multiple Access for Wireless Image Transmission in Semantic Communication