BR-FiLM：用于自动调制识别的有界残差信道质量条件模块
BR-FiLM: Bounded Residual Channel-Quality Conditioning for Automatic Modulation Recognition
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中文总结 AI 辅助
该研究针对低信噪比下自动调制识别性能下降问题，提出BR-FiLM模块构建BR-FiLMNet，在RadioML 2016.10a数据集上较MCLDNN提升了平均及低信噪比下的识别准确率。
中文摘要 AI 辅助
自动调制识别（AMR）对实现军事和民用应用中鲁棒、自适应且安全的通信至关重要。深度学习已能实现有效的AMR方法，克服了传统方法计算效率低下的问题，但这些基于深度学习的方法在低信噪比（SNR）条件下性能常大幅下降，此时噪声会掩盖调制区分性的波形特征。本文提出有界残差特征级线性调制（BR-FiLM），这是一种用于AMR的信道质量条件模块，可插入到具有中间特征表示的AMR分类器中。我们通过将BR-FiLM模块插入多通道卷积长短期深度神经网络（MCLDNN）骨干网络，构建了BR-FiLMNet。BR-FiLMNet通过门控残差校正对卷积、循环和密集特征进行条件调整，同时保留原始的同相/正交（I/Q）驱动特征路径。在RadioML 2016.10a数据集上的实验表明，与MCLDNN相比，BR-FiLMNet将平均准确率从61.79%提升至67.74%，低SNR（SNR ≤ 0 dB）下的准确率从37.12%提升至46.44%。我们进一步将BR-FiLMNet与近期的Transformer风格基准模型进行评估，结果显示，通过配对统计测试验证，BR-FiLMNet可带来显著且可靠的性能提升。
英文摘要
Automatic Modulation Recognition (AMR) plays a crucial role in enabling robust, adaptive, and secure communication for military and civilian applications. Deep learning has enabled effective AMR methods that overcome the computational inefficiency of traditional approaches. However, these deep learning based methods often degrade significantly in low SNR conditions, where noise obscures modulation-discriminative waveform features. In this paper, we propose Bounded Residual Feature-wise Linear Modulation (BR-FiLM), a channel-quality conditioning block for AMR, which can be inserted into AMR classifiers with intermediate feature representations. We construct BR-FiLMNet by inserting the proposed BR-FiLM block into a Multi-Channel Convolutional Long Short-Term Deep Neural Network (MCLDNN) backbone. BR-FiLMNet conditions convolutional, recurrent, and dense features through gated residual corrections while preserving the original I/Q-driven feature path. Experimental results on RadioML 2016.10a show that BR-FiLMNet improves mean accuracy from 61.79% to 67.74% and low-SNR accuracy (SNR <= 0 dB) from 37.12% to 46.44% compared to MCLDNN. We further evaluate BR-FiLMNet against recent transformer-style baselines. The results indicate that BR-FiLMNet delivers significant and reliable performance gains, as validated through paired statistical testing.
