论文标题
PR-NN：基于RNN的检测，用于编码部分响应通道
PR-NN: RNN-based Detection for Coded Partial-Response Channels
论文作者
论文摘要
在本文中，我们研究了基于复发性神经网络（RNN）基于符号间干扰（ISI）的磁记录通道的使用。我们指的是拟议的检测方法，该方法旨在记录具有部分响应均衡的通道，作为部分响应神经网络（PR-NN）。我们训练双向封闭式复发单元（BI-GRU），从嘈杂的通道输出序列中恢复ISI通道输入，并在应用于连续的流媒体数据时评估网络性能。 PR-NN在评估过程中的计算复杂性与Viterbi检测器的计算复杂性相当。 The recording system on which the experiments were conducted uses a rate-2/3, (1,7) runlength-limited (RLL) code with an E2PR4 partial-response channel target.理想的PR信号的实验结果表明，PR-NN检测的性能方法是在加性白色高斯噪声（AWGN）中检测的方法。此外，PR-NN检测器的表现优于Viterbi检测，并在不同的通道密度下在添加色噪声（ACN）中实现了预测性最大似然（NPML）检测的性能。接受AWGN和ACN训练的PR-NN检测器保持在单独训练下观察到的性能。同样，当用ACN培训与两个不同的通道密度相对应时，PR-NN在两个密度下保持其性能。实验证实，这种鲁棒性在广泛的信噪比（SNR）中是一致的。最后，当用MMSE平等的Lorentzian信号应用于更现实的磁记录通道时，PR-NN显示出鲁棒的性能。
In this paper, we investigate the use of recurrent neural network (RNN)-based detection of magnetic recording channels with inter-symbol interference (ISI). We refer to the proposed detection method, which is intended for recording channels with partial-response equalization, as Partial-Response Neural Network (PR-NN). We train bi-directional gated recurrent units (bi-GRUs) to recover the ISI channel inputs from noisy channel output sequences and evaluate the network performance when applied to continuous, streaming data. The computational complexity of PR-NN during the evaluation process is comparable to that of a Viterbi detector. The recording system on which the experiments were conducted uses a rate-2/3, (1,7) runlength-limited (RLL) code with an E2PR4 partial-response channel target. Experimental results with ideal PR signals show that the performance of PR-NN detection approaches that of Viterbi detection in additive white gaussian noise (AWGN). Moreover, the PR-NN detector outperforms Viterbi detection and achieves the performance of Noise-Predictive Maximum Likelihood (NPML) detection in additive colored noise (ACN) at different channel densities. A PR-NN detector trained with both AWGN and ACN maintains the performance observed under separate training. Similarly, when trained with ACN corresponding to two different channel densities, PR-NN maintains its performance at both densities. Experiments confirm that this robustness is consistent over a wide range of signal-to-noise ratios (SNRs). Finally, PR-NN displays robust performance when applied to a more realistic magnetic recording channel with MMSE-equalized Lorentzian signals.
