Deep Neural Network Symbol Detection for Millimeter Wave Communications
This addresses symbol detection for mmWave communication systems, offering a robust and efficient alternative to traditional methods, though it is incremental as it applies existing DNN techniques to a specific domain.
The paper tackles symbol detection in millimeter wave communications by proposing a deep neural network-based detector that bypasses channel state information acquisition, achieving performance close to the optimal Viterbi detector with perfect CSI and outperforming it with CSI estimation errors.
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.