# Deep Neural Network Symbol Detection for Millimeter Wave Communications

## TL;DR

This paper introduces a deep neural network-based symbol detector for mmWave communications that bypasses the need for channel state information, achieving near-optimal performance and robustness across various conditions.

## Contribution

It presents a novel sliding BRNN architecture for mmWave symbol detection that outperforms traditional methods with CSI estimation errors and is adaptable to different channel conditions.

## Key findings

- DNN detector performs close to Viterbi with perfect CSI
- Outperforms Viterbi with CSI estimation errors
- Robust to noise and channel variations

## Abstract

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.