Dual-Branch Vector-Quantization-Aided Satellite Digital Semantic Communication with Index Compression for High-Resolution RSI Over AFDM

2026-09-28

AI Summary

This study addresses the trade-off between bandwidth constraints and compression robustness against channel impairments in high-resolution remote sensing image transmission by proposing the DVQ-SDSC framework. The method employs an asymmetric dual-branch architecture to separately process high-frequency residuals and low-frequency semantics, integrating PCA-assisted codebook reordering with G-DPCM differential coding for efficient index compression, while leveraging AFDM modulation to enhance interference resilience. Experimental results demonstrate that at an extremely low bitrate of 0.0625 BPP, the proposed approach significantly outperforms the JPEG-LDPC baseline. Furthermore, G-DPCM achieves additional bitrate reduction and error isolation without requiring retraining, substantially improving overall transmission efficiency.

Abstract

High-resolution remote sensing imagery (RSI) transmission is constrained by satellite-ground bandwidth and channel impairments, yet existing methods struggle to simultaneously achieve extreme compression and robust transmission. To address this, we propose a dual-branch vector-quantization aided satellite digital semantic communication (DVQ-SDSC) framework for RSI transmission over affine frequency division multiplexing (AFDM), whose bandwidth savings arise from two interrelated aspects. First, at the source-coding level, a dual-branch framework is developed to unify deep joint semantic coding, VQ-aided index transmission, channel estimation and adaption in an end-to-end architecture; departing from symmetric encoder designs, the codec is recast as an asymmetric dual-branch architecture that separately processes the high-frequency residuals and the low-frequency structural semantics, with gated fusion and channel-adaptive reconstruction jointly restoring the semantic content. Second, at the index-coding level, we develop a principal component analysis (PCA)-aided codebook reordering to align index topology with latent correlations, and devise group differential pulse-code modulation (G-DPCM) to encode prediction residuals rather than absolute indices, lowering the index bitrate while locally isolating clipping and channel errors. A two-stage training strategy further decouples channel impairments from the semantic codec. FAIR1M experiments over 3GPP NTN-TDL-D demonstrate that DVQ-SDSC with G-DPCM index coding outperforms the conventional JPEG-LDPC scheme at the base rate of 0.0625 bits per pixel (BPP), and that G-DPCM applies directly to the trained codec without retraining, yielding additional index compression at no extra cost.

Problem

• Remote Sensing Imagery
• Satellite Communication
• Semantic Communication
• Image Compression
• Channel Impairments

Innovation

• Semantic Communication
• Vector Quantization
• Dual-Branch Architecture
• Index Compression
• AFDM