SCUNet-Based Decoding Algorithm for Rayleigh Fading Channels Integrating Feature Extraction and Recovery Mechanisms
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摘要: 人工智能的快速发展为无线通信系统性能的优化和提升提供了新思路。针对瑞利衰落信道下常规深度神经网络(DNN)译码算法性能受限的问题，该文提出一种融合特征提取与恢复机制的SCUNet译码算法，记为SCUNetDec。该网络设计中融入了数据预处理、特征提取与恢复以及噪声水平图3方面机制：首先通过升维操作将一维信号映射为二维特征图，以挖掘更丰富的结构信息；继而利用特征提取与恢复模块削弱维度转换中产生的不相关干扰，从而提升译码效果；同时引入噪声水平图，使网络能够更敏锐地感知和建模信噪比的变化，进一步增强在复杂信道环境下的适应能力。仿真结果表明，SCUNetDec在瑞利衰落信道下的误码性能优于常规神经网络译码方法，接近传统最优译码算法，且同时具备更快的译码速度。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. -
Key words:
- Intelligent decoding /
- SCUNet /
- Feature extraction /
- Short codes /
- Rayleigh fading channels
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1 SCUNetDec信号特征提取与恢复译码算法
输入：译码器接收L，信号大小为$ 1\times n $，即$ \boldsymbol{L}=\left({L}_{0},{L}_{1},\cdots ,{L}_{n-1}\right) $, n代表信号点的个数，然后将数量足够多的信号L转换为$ g\times g $大小的 二维信号，其中将每一行信号L的数量记为l,则$ g=n\times l $。 输出：恢复的消息序列$ \hat{\boldsymbol{u}} $。 (1) 初始化f = Conv2d，卷积核大小为(3,2)，步幅为(1,2)，填充为(1,0)； (2) 初始化$ {f}^{-1}=\mathrm{ConvTranspose}2\mathrm{d} $，根据不同消息序列长度选择合适的卷积核大小和步幅； (3) 初始化fSCUNet = SCUNet; (4) if$ n\geq 2 $且$ n\neq {2}^{q} $时,$ q\geq 1 $then 特征提取设计 (5) $ \left({L}_{0},{L}_{1},\cdots ,{L}_{n-1}\right)\rightarrow \left({L}_{0},{L}_{1},\cdots ,{L}_{n-1},0,\cdots ,0\right) $，给信号L尾部补0，将其长度补齐为$ {2}^{q} $，即需补$ {2}^{q}-n $个0，记为$ \overline{\boldsymbol{L}} $; (6) $ {\boldsymbol{L}}_{\mathrm{feature}}=f\cdots f\left(\overline{\boldsymbol{L}}\right) $，f层数为k; (7) $ {\boldsymbol{L}}_{\mathrm{SCUNetfeature}}=f_{\mathrm{SCUNet}}\left({\boldsymbol{L}}_{\mathrm{feature}}\right) $; 信号恢复设计 (8) $ \hat{\boldsymbol{u}}=f{f}^{-1}\cdots {f}^{-1}\left({\boldsymbol{L}}_{\mathrm{SCUNetfeature}}\right) $; else$ n\geq 2 $且$ n={2}^{\mathrm{q}} $时，$ q\geq 1 $ 特征提取设计 (9) $ {\boldsymbol{L}}_{\mathrm{feature}}=f\cdots f\left(\boldsymbol{L}\right) $, f层数为k; (10) $ {\boldsymbol{L}}_{\mathrm{SCUNetfeature}}=f_{\mathrm{SCUNet}}\left({\boldsymbol{L}}_{\mathrm{feature}}\right) $; 信号恢复设计 (11) $ \hat{\boldsymbol{u}}=f{f}^{-1}\cdots {f}^{-1}\left({\boldsymbol{L}}_{\mathrm{SCUNetfeature}}\right) $; 
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表 1 不同译码器译码时间对比
译码器 编码方式 码长L 译码时间(s) SCUNetDec 汉明码 7 0.65 ML 汉明码 7 149.09 DNN 汉明码 7 0.63 SCUNetDec 卷积码 16 0.60 Viterbi 卷积码 16 5.43 DNN 卷积码 16 1.54 SCUNetDec Polar 16 0.70 SC Polar 16 60.00 DNN Polar 16 0.68
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