预白化与BCJR后验蒸馏用于超奈奎斯特信令中的Bi-LSTM检测
Pre-Whitening and BCJR Posterior Distillation for Bi-LSTM Detection in Faster-than-Nyquist Signaling
- Ankara Yildirim Beyazit University（安卡拉耶尔德勒姆贝亚兹特大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过预白化和BCJR后验蒸馏，在超奈奎斯特信令中提升Bi-LSTM检测性能，以少量参数增加接近最优BCJR误码率。
AI中文摘要：
循环检测器（如双向长短期记忆（Bi-LSTM）网络）是超奈奎斯特（FTN）信令中最佳Bahl-Cocke-Jelinek-Raviv（BCJR）检测器的低复杂度替代方案。受将符号间干扰（ISI）结构构建到架构中的卷积检测器的启发，我们探究在分离的循环分支中处理嵌套ISI窗口是否能改善Bi-LSTM的误码率（BER）。在约260次受控训练中，答案是否定的：在匹配的参数预算和匹配的读出下，多窗口架构从未显著优于普通Bi-LSTM。嵌套窗口化是一种可逆重排，不增加信息，额外分支仅增加瓶颈，蒸馏诊断显示网络已接近其窗口的最优性能。因此，限制在于观测模型而非架构。保持架构固定，我们对输入进行预白化，恢复被有色匹配滤波器噪声破坏的条件独立性，并将BCJR软后验蒸馏到网络中。仅增加3.4%的参数，在压缩因子0.8时达到BCJR BER的1.05倍，在0.7时达到1.89倍，当白化窗口加宽时改善至1.47倍。在0.8时23.7%的BER降低需要病态的ISI矩阵，但并非随条件数单调变化，且该结果在五个独立噪声实现和符号级McNemar检验中均成立。
英文摘要：
Recurrent detectors such as bidirectional long short-term memory (Bi-LSTM) networks are low-complexity alternatives to the optimal Bahl-Cocke-Jelinek-Raviv (BCJR) detector for faster-than-Nyquist (FTN) signaling. Motivated by convolutional detectors that build the intersymbol interference (ISI) structure into their architecture, we ask whether processing nested ISI windows in separate recurrent branches improves the bit error rate (BER) of a Bi-LSTM. Across roughly 260 controlled trainings it does not: at a matched parameter budget and a matched readout, the multi-window architecture never significantly beats a plain Bi-LSTM. Nested windowing is an invertible rearrangement that adds no information, extra branches only add bottlenecks, and a distillation diagnostic shows the network is already near optimal for its window. The limitation is therefore the observation model, not the architecture. Keeping the architecture fixed, we pre-whiten the input, restoring the conditional independence that colored matched-filter noise violates, and distill the BCJR soft posterior into the network. With 3.4% more parameters this reaches 1.05 times the BCJR BER at a compression factor of 0.8 and 1.89 times at 0.7, improving to 1.47 times when the whitened window is widened. The 23.7% BER reduction at 0.8 requires an ill-conditioned ISI matrix but is not monotone in the conditioning, and it holds across five independent noise realizations and a symbol-level McNemar test.
