2019/06/15 by Tianjie Mu, Mu, Tianjie, Li Chen +6
Biochemistry, Genetics and Molecular Biology · Computer Science · #Blind Source Separation Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Fractal and DNA sequence analysis #Information Theory (cs.IT) #Signal Processing (eess.SP) #Wireless Signal Modulation Classification #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1906.06563
openalex publication_date 2019/06/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Deep Learning has been widely applied in the area of image processing and natural language processing. In this paper, we propose an end-to-end communication structure based on autoencoder where the transceiver can be optimized jointly. A neural network roles as a combination of channel encoder and modulator. In order to deal with input sequences parallelly, we introduce block scheme, which means that the autoencoder divides the input sequence into a series of blocks. Each block contains fixed number of bits for encoding and modulating operation. Through training, the proposed system is able to produce the modulated constellation diagram of each block. The simulation results show that our autoencoder performs better than other autoencoder-based systems under additive Gaussian white noise (AWGN) and fading channels. We also prove that the bit error rate (BER) of proposed system can achieve an acceptable range with increasing the number of symbols.