SCUNet-Based Decoding Algorithm for Rayleigh Fading Channels Integrating Feature Extraction and Recovery Mechanisms

WANG Leijun; WANG Kuan; XIE Jinfa; PENG Xidong; LI Jiawen; CHEN Rongjun

doi:10.11999/JEIT251138

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WANG Leijun, WANG Kuan, XIE Jinfa, PENG Xidong, LI Jiawen, CHEN Rongjun. SCUNet-Based Decoding Algorithm for Rayleigh Fading Channels Integrating Feature Extraction and Recovery Mechanisms[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251138

Citation:

WANG Leijun, WANG Kuan, XIE Jinfa, PENG Xidong, LI Jiawen, CHEN Rongjun. SCUNet-Based Decoding Algorithm for Rayleigh Fading Channels Integrating Feature Extraction and Recovery Mechanisms[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251138

Citation:

WANG Leijun, WANG Kuan, XIE Jinfa, PENG Xidong, LI Jiawen, CHEN Rongjun. SCUNet-Based Decoding Algorithm for Rayleigh Fading Channels Integrating Feature Extraction and Recovery Mechanisms[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251138

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SCUNet-Based Decoding Algorithm for Rayleigh Fading Channels Integrating Feature Extraction and Recovery Mechanisms

doi: 10.11999/JEIT251138 cstr: 32379.14.JEIT251138

School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China

Funds: The Key Discipline Improvement Project of Guangdong Province (2022ZDJS015, 2025ZDJS023), The Scientific Research Capacity Improvement Project of the Doctoral Program Construction Unit of Guangdong Polytechnic Normal University (22GPNUZDJS17), The Graduate Education Demonstration Base Project of Guangdong Polytechnic Normal University (2023YJSY04002), The Guangzhou Science and Technology Plan Project (2024B03J1361, 2023B03J1327)

Received Date: 2025-10-29
Accepted Date: 2026-01-22
Rev Recd Date: 2026-01-22

Available Online: 2026-02-11

Abstract

Abstract

Objective This study examines limitations of conventional Deep Neural Network (DNN) decoding algorithms in Rayleigh fading channels, including constrained performance, limited generalization, and weak fading resistance. To address these issues, a decoding algorithm based on the SCUNet (Swin Conv UNet) architecture, termed SCUNetDec, is proposed. In 6G communication scenarios, wireless channels exhibit strong dynamics and complexity, which restrict the ability of traditional decoding methods to meet requirements for high reliability, low latency, and robustness. Intelligent decoding methods with adaptive feature learning are therefore valuable. SCUNetDec integrates multi-dimensional feature extraction and recovery modules and uses a noise-level map to strengthen channel-state perception. These components enable the network to learn channel characteristics, reduce fading effects, and improve decoding performance. The study provides an approach for intelligent decoding in complex channel environments and supports the development of efficient 6G communication systems. Methods The SCUNetDec network combines three mechanisms—data preprocessing, feature extraction and recovery, and noise-level mapping—to enhance signal representation learning and decoding in Rayleigh fading channels. In the preprocessing stage, dimensionality expansion converts the one-dimensional received signal into a two-dimensional feature map, improving structural visibility and supporting spatial correlation learning. The feature extraction and recovery module uses multi-layer convolution and attention mechanisms to capture essential channel features, whereas deconvolution layers and residual connections suppress interference introduced during dimensionality transformation. This improves reconstruction quality and decoding accuracy. A noise-level map embeds SNR (Signal to Noise Ratio)-related information aligned with the feature maps, allowing the model to adjust to channel variation and adapt decoding strength. The combined effect of these mechanisms increases noise robustness, generalization, and decoding stability, offering a systematic decoding solution for complex 6G wireless environments. Results and Discussions SCUNetDec enhances signal learning and decoding in Rayleigh fading channels through its feature extraction–recovery module and noise-level map. Simulations under different coding schemes validate its effectiveness. For the (7,4) Hamming code, SCUNetDec outperforms conventional DNN decoding and approaches Maximum Likelihood (ML) performance; at BER (Bit Error Rate) = 10^–4, the gap to ML is about 1.5 dB, and at FER (Frame Error Rate) = 10^–3, the gap is about 2.0 dB (Fig. 4). This indicates that SCUNetDec captures complex signal relationships and learns associations between information and parity-check nodes. For the (2,1,3) convolutional code, SCUNetDec performs close to the Viterbi algorithm at BER = 10^–3, with a gap of roughly 2.0 dB, while conventional DNN decoding degrades at high SNRs (Fig. 5). For Polar codes with a rate of 0.5, SCUNetDec shows a gain of about 4.0 dB over successive cancellation (SC) decoding at BER = 10^–4 and maintains an advantage of about 1.0 dB at FER = 10^–3, with SC performing slightly better only in the low-SNR region (Fig. 6). Decoding-time comparisons show that SCUNetDec reduces decoding latency relative to traditional methods (Table S1). Ablation experiments confirm that integrating the feature extraction and recovery modules into SCUNet improves decoding performance (Fig. 7). Overall, results show that SCUNetDec provides robust decoding performance across coding schemes and SNR levels. Conclusions This study proposes SCUNetDec to address performance limitations of DNN decoders in Rayleigh fading channels. The method enhances SCUNet using signal feature extraction and recovery modules. Simulations and ablation experiments on Hamming, convolutional, and Polar codes show strong generalization capability and effectiveness. Compared with traditional DNN models, SCUNetDec achieves decoding performance close to optimal decoding algorithms and reduces decoding time. These findings indicate that SCUNetDec has practical potential for complex channel environments. Future work will examine fusion of neural and traditional algorithms to balance performance and complexity through dynamic parameter optimization and explore intelligent decoding strategies for long codes. Research will also investigate joint modulation–decoding modeling and end-to-end architectures to improve adaptability under high-order modulation and complex channels.
- Intelligent decoding,
- SCUNet,
- Feature extraction,
- Short codes,
- Rayleigh fading channels

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References(39)

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