The RNN BCJR Detector in Time-Varying Channels

A Recurrent Neural Network (RNN) detector is obtained by interfacing the RNN BCJR, introduced by the authors, with the channel output through a convolutional linear layer that mimics the presence of a shortening filter. The RNN detector can be trained to implement a channel and modulation-agnostic detector, including the functions of channel shortening, Maximum Likelihood (ML) sequence detection, and symbol or bit Log-Likelihood (LL) computation for the following soft input channel decoder. The RNN detector has a processing complexity that matches that of the corresponding classical receiver. In this paper we explore the effectiveness of its employment in static and time-selective scenarios.