FPGA Implementation of Recurrent Neural Network-Based Polar Decoder

Abstract

Polar codes are part of the 5G technology enablers as they nearly achieve memoryless channels capacity. Recently, many researchers explored Machine-learning (ML) techniques to increase the effectiveness of polar codes decoders. Recurrent Neural Network Belief Propagation decoders (RNN-BP) demonstrated superior efficiency in fewer cycles than standard Polar decoders, as traditional Belief propagation decoders performed poorly throughout a limited number of iterations. In this paper, we present the first implementation of the RNN-BP on Field Programmable Gate Arrays (FPGA). Two implementations are presented in this paper. The first one uses a single processing unit, and the second one is enhanced by multiple processing units with a pipeline register. The multiple processing units’ design of RNN-BP shows much higher throughput which is 17x of belief propagation decoder implementation and 3x of the Soft-output CANcellation (SCAN) decoder implementation. We also achieved better BER than the two mentioned implementations 7. 08x than the original BP and 4. 5x better than the SCAN implementation. The combined memory and registers resource consumption in our design is less than the compared two implementations while consuming a larger number of look-up tables (LUTs).