Semantics-Empowered Non-Orthogonal Multiple Access for Downlink Transmission of Correlated Information Sources

In this paper, we introduce an end-to-end non-orthogonal multiple access (NOMA) framework for the downlink transmission of correlated information sources in the multi-user scenario, in which the data required or transmitted by multiple users share similar content. To enhance the end-to-end transmission performance, we resort to the semantic communication paradigm and build our system based on the deep joint source-channel coding (D-JSCC) scheme. Inspired by Wyner’s common information, an information theoretical concept, the common information (CI) extraction is proposed to capture the correlation between multiple users effectively. By relaxing the constraint of the object function, equivalency can be established between common information extraction and mutual information maximization. Thereby, the Jenson-Shannon divergence (JSD) is adopted in the loss function for learning the common information representation (CIR). In order to categorize the theoretical performance limit of the proposed system, semantic synonymous mapping (SSM) based information theory is applied for analyzing the effect of correlation level and different decoding schemes on the achievable channel capacity. Specifically, the analytical expression of channel capacity under additive white Gaussian noise (AWGN) and Rayleigh channel is derived and verified by Monte-Carlo experiments. By conducting simulations on three different image datasets, it is verified that our proposed scheme can outperform a series of other state-of-the-art (SoTA) multiple access or distributed source coding (DSC) schemes under up to seven user scenarios. Besides, the visualization and ablation study results validate the effectiveness of the common information extraction.