Quasi-Neural Network-Based Decoder for Single-Carrier Communications

Abstract

This paper proposes a novel algorithm, named a quasi-neural network-based decoder (QNN-decoder), for a single-carrier communication system. The algorithm is designed for an inter-symbol-interference (ISI) channel that a trellis diagram can model. According to the trellis diagram, a quasi-neural network (QNN) is built to acquire the likelihoods of the received samples enabling the subsequent decoding. The QNN-decoder differs from artificial neural network (ANN) based algorithms, such as the online learning trellis diagram (OLTD), as it doesn’t rely on data but instead utilizes the physical system model. This means the QNN-decoder can use a much shorter pilot to train its network via backpropagation than OLTD. Meanwhile, the QNN-decoder doesn’t require explicit channel state information (CSI) or statistics of interference and noise. Instead, it can efficiently suppress non-Gaussian interference by learning the CSI and interference and noise statistics. Simulation results verify the QNN-decoder outperforms the state-of-the-art methods and approaches the performance limits provided by the conventional Viterbi with perfect CSI in Gaussian noise only. The QNN-detector outperforms the conventional Viterbi and OLTD with non-Gaussian interference. A few redundant nodes make the QNN-decoder robust to channel length uncertainty, and it may be easily extended to a multi-antenna system for even greater interference suppression.