End‐to‐end waveform level receiver with deep learning

Abstract

Numerous researches on communication receiver design with deep learning algorithms have been implemented effectively recently. In this paper, an innovative scheme is proposed to construct an end-to-end neural network (NN) receiver to directly recover information bits from unsynchronized waveform sequences. Considering the correlation between samples, multiple bidirectional long-short term memory (BiLSTM) layers to process oversampled signals. Then, shifted binary cross-entropy (SBCE) function is devised to tackle the overfitting issue and eliminate instances with abnormal bit error rate (BER), which originate from standard binary cross-entropy (BCE) loss function. Substantial simulation results demonstrate that the trained NN receiver can achieve theory BER values under excellent conditions for transceivers. Compared with traditional synchronization algorithms, the proposed method can obtain significant BER performance gain under harsh conditions, such as low oversample rate, small roll-off factor, short frame length.