Resilient Semantic Communication for Space-Air-Ground Networks

WANG Wenyuan; ZHOU Mingyu; WANG Chaowei; XU Jisong; ZHANG Yunze; PANG Mingliang; JIANG Fan; XU Lexi; ZHANG Zhi

doi:10.11999/JEIT250077

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WANG Wenyuan, ZHOU Mingyu, WANG Chaowei, XU Jisong, ZHANG Yunze, PANG Mingliang, JIANG Fan, XU Lexi, ZHANG Zhi. Resilient Semantic Communication for Space-Air-Ground Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250077

Citation:

WANG Wenyuan, ZHOU Mingyu, WANG Chaowei, XU Jisong, ZHANG Yunze, PANG Mingliang, JIANG Fan, XU Lexi, ZHANG Zhi. Resilient Semantic Communication for Space-Air-Ground Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250077

Citation:

WANG Wenyuan, ZHOU Mingyu, WANG Chaowei, XU Jisong, ZHANG Yunze, PANG Mingliang, JIANG Fan, XU Lexi, ZHANG Zhi. Resilient Semantic Communication for Space-Air-Ground Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250077

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Resilient Semantic Communication for Space-Air-Ground Networks

doi: 10.11999/JEIT250077 cstr: 32379.14.JEIT250077

1.
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
2.
Beijing Science And Technology Project Manager Management Corporation , Ltd., Beijing 100083, China
3.
Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing 100876, China
4.
School of Communications and Information Engineering & School of Artificial Intelligence, Xi’an University of Posts & Telecommunications, Xi’an 710121, China
5.
Research Institute of China United Network Communications Co., Ltd., Beijing 100048, China
6.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Funds: The National Natural Science Foundation of China (62471052)

Received Date: 2025-02-12
Rev Recd Date: 2025-07-02

Available Online: 2025-07-14

Abstract

Abstract

Objective Space-air-ground networks represent a fundamental evolution in wireless communication infrastructure by extending conventional terrestrial networks into airspace and outer space through the integration of satellites, unmanned aerial vehicles, and ground-based nodes. These networks have become a cornerstone technology for future sixth-generation wireless communications, providing wide-area coverage and flexible networking capabilities that overcome the limitations of geographically constrained terrestrial systems. However, image transmission over space-air-ground networks faces considerable challenges due to inherent bandwidth constraints, severe channel impairments, and highly dynamic propagation environments. Traditional Separate Source-Channel Coding (SSCC) methods, although theoretically sound, exhibit poor performance under these adverse conditions, particularly near information-theoretic limits. The rapid expansion of multimedia applications and service demands requires new approaches to information extraction and transmission that meet ultra-reliable low-latency communication requirements. Conventional image coding algorithms, such as JPEG and JPEG2000, experience significant performance degradation in satellite communication systems due to channel impairments. These limitations highlight the need for advanced semantic communication frameworks that move beyond conventional bit-level transmission and prioritize the preservation of semantic content integrity under complex wireless conditions. Methods A resilient semantic communication framework is proposed for image transmission in space-air-ground networks, based on enhanced information bottleneck theory. The key innovation is the development of an augmented rate-distortion function that jointly optimizes system capacity, pixel-level reconstruction fidelity, semantic feature preservation, and perceptual quality. The framework combines deep learning with information-theoretic principles to ensure reliable performance under varying channel conditions. A Gumbel-Softmax approach is integrated with variable rate networks to enable dynamic rate adaptation in response to fluctuating channel states. The system employs a weighted multi-asymmetric Gaussian distribution model to characterize the probability density functions of heterogeneous semantic features, providing accurate entropy estimation in the latent space. The architecture incorporates attention mechanisms and residual learning structures to enhance feature extraction and semantic preservation. A reconfigurable neural network architecture is introduced, featuring adaptive module selection driven by real-time signal-to-noise ratio assessments and service quality requirements. The encoder subsystem consists of semantic extractor networks that identify and isolate critical image features, joint source-channel encoders that perform integrated compression and error protection, and adaptive networks that generate binary mask vectors for intelligent symbol selection. The semantic extractor combines convolutional neural networks with fully connected layers to capture hierarchical feature representations. The joint source-channel coding architecture integrates residual convolution blocks, attention feature blocks, and residual transpose convolution blocks to optimize rate-distortion performance. The adaptive network produces dynamic masks through Gumbel-Softmax sampling, controlling the transmission of semantic symbols based on their relevance and channel state. A two-stage training strategy is implemented. First, end-to-end optimization of the joint source-channel encoders and decoders is performed using mean squared error loss. This is followed by full system training based on a composite loss function that jointly considers transmission rate, pixel-level distortion, semantic distortion, and perceptual quality. Results and Discussions Comprehensive experimental validation is conducted using the CIFAR-10, CIFAR-100, and Kodak24 datasets to assess the effectiveness of the proposed framework. Performance is evaluated using multiple metrics, including Peak Signal-to-Noise Ratio (PSNR) for objective image quality assessment and USC metric for overall system efficiency. Comparative analysis with conventional JPEG combined with LDPC and QAM schemes shows substantial performance gains, particularly under low Signal-to-Noise Ratio (SNR) conditions (Fig. 3, Fig. 4). The proposed resilient semantic communication framework consistently outperforms conventional methods across different compression ratios, demonstrating robust resistance to channel impairments and effectively mitigating the cliff effect observed in traditional SSCC systems. When compared with advanced deep learning-based approaches such as ADJSCC and DeepJSCC-V, the proposed method achieves significant improvements in both PSNR and transmission efficiency (Fig. 5, Fig. 6). Efficiency evaluation using USC metrics shows that the proposed framework achieves higher utility values across various compression settings, with performance advantages becoming more evident as SNR decreases (Fig. 7). Further analysis of different network configurations demonstrates the adaptability of the architecture in balancing computational complexity and transmission performance (Fig. 8, Fig. 9). Configurations with five network units consistently provide higher PSNR values compared to three- and four-unit designs. However, four-unit configurations achieve optimal efficiency under high SNR conditions, indicating effective resource allocation in response to varying channel states. Visual quality assessment using the Kodak dataset confirms improved reconstruction performance with reduced channel bandwidth requirements, supporting the practical feasibility of the proposed approach (Fig. 10). Computational complexity analysis shows that the five-unit configuration maintains complexity comparable to existing benchmarks, while three- and four-unit designs significantly reduce computational demands, supporting deployment on resource-constrained platforms (Table 4). These experimental results demonstrate that the proposed framework provides superior reconstruction quality, transmission efficiency, and adaptive capability across diverse and challenging wireless environments. Conclusions This study presents a resilient semantic communication framework that addresses key challenges in image transmission for space–air–ground networks. Experimental validation confirms that the proposed method achieves substantial improvements over conventional approaches in reconstruction quality, transmission efficiency, and adaptability.
- Space-Air-Ground networks,
- Semantic communication,
- Channel adaptive,
- Enhanced information bottleneck theory,
- Joint source-channel coding

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References(31)

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