The training complexity of deep learning-based channel decoders scales exponentially with the codebook size and therefore with the number of information bits. Thus, neural network decoding (NND) is currently only feasible for very short block lengths. In this work, we show that the conventional iterative decoding algorithm for polar codes can be enhanced when sub-blocks of the decoder are replaced by neural network (NN) based components. Thus, we partition the encoding graph into smaller sub-blocks and train them individually, closely approaching maximum a posteriori (MAP) performance per sub-block. These blocks are then connected via the remaining conventional belief propagation decoding stage(s). The resulting decoding algorithm is non-iterative and inherently enables a highlevel of parallelization, while showing a competitive bit error rate (BER) performance. We examine the degradation through partitioning and compare the resulting decoder to state-of-theart polar decoders such as successive cancellation list and belief propagation decoding. 978-1-5090-5019-2/17/$31.00 ©2017 IEEE
Specifically for Polar codes, where the encoding graph can be partitioned into sub-blocks, Cammerer et al. REF performed neural decoding for each sub-block separately and then combine the sub-blocks and use a belief propagation decoding algorithm.
Scaling Deep Learning-Based Decoding of Polar Codes via Partitioning
GLOBECOM 2017 - 2017 IEEE Global Communications Conference
GLOBECOM 2017 - 2017 IEEE Global Communications Conference
Mathematics Computer Science