List Autoencoder: Towards Deep Learning Based Reliable Transmission Over Noisy Channels

There has been a growing interest in automating the design of channel encoders and decoders in an auto-encoder(AE) framework in recent years for reliable transmission of data over noisy channels. In this paper we present a new framework for designing AEs for this purpose. In particular, we present an AE framework, namely listAE, in which the decoder network outputs a list of decoded message word candidates. A genie is assumed to be available at the output of the decoder and specific loss functions are proposed to optimize the performance of the genie-aided (GA)-listAE. The listAE is a general AE framework and can be used with any network architecture. We propose a specific end-to-end network architecture which decodes the received word on a sequence of component codes with decreasing rates. The listAE based on the proposed architecture, referred to as incremental redundancy listAE (IR-listAE), improves the state-of-the-art AE performance by 1 dB at low block error rates under GA decoding. We then employ cyclic redundancy check (CRC) codes to replace the genie at the decoder, giving CRC-aided (CA)-listAE with negligible performance loss compared to the GA-listAE. The CA-listAE shows meaningful coding gain at the price of a slight decrease in the rate due to appending CRC to the message word.