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

Low-complexity Recurrent Neural Network-based Polar Decoder with Weight Quantization Mechanism