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<p>This paper investigates the behavior of deep joint source–channel coding (DeepJSCC) for semantic image transmission over time-varying multiple-input multiple-output (MIMO) wireless channels. While DeepJSCC has demonstrated robustness to channel noise and imperfect channel knowledge, the impact of mobility-induced time variations and inter-stream semantic interference in MIMO systems requires a systematic evaluation. In this study, an autoencoder-based DeepJSCC model is trained under single-input single-output (SISO) additive white Gaussian noise (AWGN) conditions, assuming that MIMO equalization is performed independently. During the evaluation phase, image data are transmitted over time-varying MIMO channels with different precoding and postcoding configurations. Reconstruction performance is assessed using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) under a wide range of Doppler conditions and antenna configurations. Experimental results reveal that conventional separated source–channel coding (JPEG–LDPC–QPSK) exhibits a pronounced <i>cliff effect</i> under mobility, leading to rapid performance degradation as channel variations increase. In contrast, DeepJSCC maintains <i>graceful degradation</i>, demonstrating substantially higher resilience in dynamic wireless environments. Furthermore, increasing the number of receive antennas notably mitigates the effects of channel aging, highlighting the importance of spatial diversity in mobility-aware semantic communication systems.</p>

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Toward mobility-aware semantic communication: DeepJSCC over time-varying MIMO channels

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月21日

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  • Goki Sawada,
  • Shion Inokuma,
  • Daisuke Hisano,
  • Kazuki Maruta

摘要

This paper investigates the behavior of deep joint source–channel coding (DeepJSCC) for semantic image transmission over time-varying multiple-input multiple-output (MIMO) wireless channels. While DeepJSCC has demonstrated robustness to channel noise and imperfect channel knowledge, the impact of mobility-induced time variations and inter-stream semantic interference in MIMO systems requires a systematic evaluation. In this study, an autoencoder-based DeepJSCC model is trained under single-input single-output (SISO) additive white Gaussian noise (AWGN) conditions, assuming that MIMO equalization is performed independently. During the evaluation phase, image data are transmitted over time-varying MIMO channels with different precoding and postcoding configurations. Reconstruction performance is assessed using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) under a wide range of Doppler conditions and antenna configurations. Experimental results reveal that conventional separated source–channel coding (JPEG–LDPC–QPSK) exhibits a pronounced cliff effect under mobility, leading to rapid performance degradation as channel variations increase. In contrast, DeepJSCC maintains graceful degradation, demonstrating substantially higher resilience in dynamic wireless environments. Furthermore, increasing the number of receive antennas notably mitigates the effects of channel aging, highlighting the importance of spatial diversity in mobility-aware semantic communication systems.