  This paper proposes to use a deep neural network (DNN)-based symbol detector
for mmWave systems such that CSI acquisition can be bypassed. In particular, we
consider a sliding bidirectional recurrent neural network (BRNN) architecture
that is suitable for the long memory length of typical mmWave channels. The
performance of the DNN detector is evaluated in comparison to that of the
Viterbi detector. The results show that the performance of the DNN detector is
close to that of the optimal Viterbi detector with perfect CSI, and that it
outperforms the Viterbi algorithm with CSI estimation error. Further
experiments show that the DNN detector is robust to a wide range of noise
levels and varying channel conditions, and that a pretrained detector can be
reliably applied to different mmWave channel realizations with minimal
overhead.
