  Polar codes have drawn much attention and been adopted in 5G New Radio (NR)
due to their capacity-achieving performance. Recently, as the emerging deep
learning (DL) technique has breakthrough achievements in many fields, neural
network decoder was proposed to obtain faster convergence and better
performance than belief propagation (BP) decoding. However, neural networks are
memory-intensive and hinder the deployment of DL in communication systems. In
this work, a low-complexity recurrent neural network (RNN) polar decoder with
codebook-based weight quantization is proposed. Our test results show that we
can effectively reduce the memory overhead by 98% and alleviate computational
complexity with slight performance loss.
