  In this paper, we consider using deep neural network for OFDM symbol
detection and demonstrate its performance advantages in combating large Doppler
Shift. In particular, a new architecture named Cascade-Net is proposed for
detection, where deep neural network is cascading with a zero-forcing
preprocessor to prevent the network stucking in a saddle point or a local
minimum point. In addition, we propose a sliding detection approach in order to
detect OFDM symbols with large number of subcarriers. We evaluate this new
architecture, as well as the sliding algorithm, using the Rayleigh channel with
large Doppler spread, which could degrade detection performance in an OFDM
system and is especially severe for high frequency band and mmWave
communications. The numerical results of OFDM detection in SISO scenario show
that cascade-net can achieve better performance than zero-forcing method while
providing robustness against ill conditioned channels. We also show the better
performance of the sliding cascade network (SCN) compared to sliding
zero-forcing detector through numerical simulation.
