多用户无线图像语义传输框架，提升信道感知下的通信鲁棒性。
Multi-user Wireless Image Semantic Transmission over MIMO Multiple Access Channels
- 利用信道状态信息融合到编解码器，生成注意力掩码图。
- 解码端通过协作式干扰消除与掩码比率生成，性能比DeepJSCC-NOMA高3 dB PSNR。
- 适合高干扰环境下的多用户图像语义传输场景。
本文研究多输入多输出多址信道（MIMO-MAC）上的典型上行传输场景，提出一种多用户可学习信道状态信息融合语义通信（MU-LCFSC）框架。该框架将信道状态信息作为辅助信息融入语义编码器和解码器，生成合理的特征掩码图，以实现更鲁棒的注意力权重分布。尤其在解码端，采用协作式连续干扰消除机制与协作掩码比率生成器，灵活调控特征掩码图的掩码元素。数值结果表明，所提方法在PSNR上相比DeepJSCC-NOMA提升3 dB，验证了其优越性。
原文摘要 · Abstract (English)
This paper focuses on a typical uplink transmission scenario over multiple-input multiple-output multiple access channel (MIMO-MAC) and thus propose a multi-user learnable CSI fusion semantic communication (MU-LCFSC) framework. It incorporates CSI as the side information into both the semantic encoders and decoders to generate a proper feature mask map in order to produce a more robust attention weight distribution. Especially for the decoding end, a cooperative successive interference cancellation procedure is conducted along with a cooperative mask ratio generator, which flexibly controls the mask elements of feature mask maps. Numerical results verify the superiority of proposed MU-LCFSC compared to DeepJSCC-NOMA over 3 dB in terms of PSNR.
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