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A wireless image semantic transmission approach with joint spatial-channel-power domain adaptation
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DOI：10.23919/jcc.fa.2025-0313.202604.png)
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Abstract
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This paper proposes a novel end-to-end learnable framework for semantic image transmission, pioneering joint optimization across the spatial, channel, and power domains. In this scheme, the transmitter employs semantic analysis with spatial-channel domain adaptation to extract and compress vital semantic features from image latent representations, enabling efficient compression by integrating spatial structures and retaining channel priority attributes. Subsequently, a dynamic power allocation strategy intelligently adjusts the transmission power of these features based on real-time noise conditions to mitigate channel impairments. At the receiver, a hierarchical reconstruction network subsequently decodes images through cross-feature analysis of semantic relationships from distorted features. Extensive experimental validation under Rayleigh fading channels demonstrates that the proposed framework achieves significantly superior bandwidth utilization and reconstruction quality compared to existing seep joint source channel coding (DJSCC) schemes. It exhibits robust performance across diverse channel conditions and compression ratios, thereby establishing a new benchmark for semantic communications (Sem-Com).
Keywords:
DJSCC
image transmission
power allocation
SemCom
spatial-channel compression
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