Multi-user Wireless Image Semantic Transmission over MIMO Multiple Access Channels
This addresses efficient multi-user image transmission in wireless networks, but appears incremental as it builds on existing semantic communication approaches.
The paper tackles multi-user wireless image transmission over MIMO channels by proposing a learnable CSI fusion semantic communication framework that incorporates channel state information into encoders and decoders to improve robustness. The method achieves over 3 dB higher PSNR compared to DeepJSCC-NOMA.
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.