# Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling

arXiv: https://arxiv.org/abs/2608.04155 · HTML: https://mlsanity.com/paper/2608.04155/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2026.

## Built on by (most cited first, 1 of 1)

- [Pre-Whitening and BCJR Posterior Distillation for Bi-LSTM Detection in Faster-than-Nyquist Signaling](https://mlsanity.com/paper/2609.07762/) (2026)

## Abstract (arXiv)

This study proposes a low-complexity, bidirectional, single-pass Elman recurrent neural network detector for binary phase-shift keying signals transmitted with faster-than-Nyquist signaling. Since the faster-than-Nyquist intersymbol interference has a short, finite memory, the classical Elman recurrent neural network, which contains no gating mechanism, is a sufficient and parameter-efficient model. The proposed detector processes the received sequence in both forward and backward directions in a single pass, forming a learned counterpart of the optimal BCJR forward-backward recursion. Under a root-raised-cosine pulse over an additive white Gaussian noise channel, simulations for two compression factors show that the proposed detector, with only twenty-five to sixty-five trainable parameters, attains a bit error rate very close to that of the M-BCJR algorithm, while reducing the look-up-table hardware cost by thirty-eight to forty-six percent and using no explicit division or exponential operations. A more compact configuration offers up to a sixty-seven percent reduction at a small performance penalty. To the best of our knowledge, this is the first study to investigate the classical Elman recurrent neural network architecture for the faster-than-Nyquist detection problem.