PR-NN: RNN-based Detection for Coded Partial-Response Channels

TL;DR

This paper introduces PR-NN, an RNN-based detection method for magnetic recording channels with ISI, demonstrating performance comparable or superior to traditional detectors under various noise conditions and channel scenarios.

Contribution

The paper presents a novel RNN-based detection approach for partial-response channels, showing robustness and improved performance over Viterbi and NPML detectors in noisy magnetic recording environments.

Findings

PR-NN approaches Viterbi detection in AWGN

PR-NN outperforms Viterbi in colored noise

PR-NN maintains robustness across SNRs and channel densities

Abstract

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.