1 research outputs found
Learning to Denoise and Decode: A Novel Residual Neural Network Decoder for Polar Codes
Polar codes have been adopted as the control channel coding scheme in the
fifth generation new radio (5G NR) standard due to its capacity-achievable
property. Traditional polar decoding algorithms such as successive cancellation
(SC) suffer from high latency problem because of their sequential decoding
nature. Neural network decoder (NND) has been proved to be a candidate for
polar decoder since it is capable of oneshot decoding and parallel computing.
Whereas, the bit-errorrate (BER) performance of NND is still inferior to that
of SC algorithm. In this paper, we propose a residual neural network decoder
(RNND) for polar codes. Different from previous works which directly use neural
network for decoding symbols received from the channel, the proposed RNND
introduces a denoising module based on residual learning before NND. The
proposed residual learning denoiser is able to remove remarkable amount of
noise from received signals. Numerical results show that our proposed RNND
outperforms traditional NND with regard to the BER performance under comparable
latency.Comment: 6 pages, 9 figure