Neural Network-Assisted Receiver Design via Learning Trellis Diagram Online

Abstract

This paper studies machine learning-assisted optimal receivers for a communication system with memory, which can be modelled by a trellis diagram. The prerequisite of the optimal receiver is to obtain the likelihoods of the received samples under different state transitions. We propose to learn the trellis diagram real-time using an artificial neural network (ANN) trained by a pilot sequence. This approach, termed as the online learning trellis diagram (OLTD), requires neither the channel state information (CSI) nor statistics of the noise, and can be incorporated into the classic Viterbi and the BCJR algorithm. In a channel with non-Gaussian noise, the OLTD method can significantly outperform the model-based methods that use Gaussian assumption. It requires much less training overhead than the state-of-the-art ANN-assisted methods. As an illustrative example, the OLTD-based BCJR is applied to a Bluetooth low energy (BLE) receiver trained only by a 256-sample pilot sequence. Moreover, the OLTD-based BCJR can accommodate for turbo equalization. As an interesting by-product, we propose an enhancement to the BLE standard by introducing a bit interleaver to its physical layer; the resultant improvement of the receiver sensitivity can make it a better fit for some Internet of Things (IoT) communications.