A bidirectional Elman RNN with 25 to 65 trainable parameters matches M-BCJR bit error rate within 0.1 to 0.4 dB for FTN-BPSK at tau=0.8/0.9 while cutting LUT hardware cost by 38 to 67 percent.
Reduced-complexity equalization for faster-than-Nyquist signaling: New methods based on Ungerboeck observation model,
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Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling
A bidirectional Elman RNN with 25 to 65 trainable parameters matches M-BCJR bit error rate within 0.1 to 0.4 dB for FTN-BPSK at tau=0.8/0.9 while cutting LUT hardware cost by 38 to 67 percent.