2018/11/30 by Qisheng Huang, Huang, Qisheng, Chunming Zhao +7
Computer Science · Engineering · #Error Correcting Code Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Radar Systems and Signal Processing #Signal Processing (eess.SP) #Wireless Signal Modulation Classification #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1812.00023
openalex publication_date 2018/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.