A Non-uniform Quantization-based Hardware Architecture for BP Decoding of Polar Codes
Xinyi Gu, Mohammad Rowshan, Yixuan Xie, Jinhong Yuan · 2023
Belief propagation (BP) decoding of polar codes benefits from the high parallelism in hardware implementation and consequently provides high throughput compared to successive cancellation-based decoding algorithms. As the evolved log-likelihood ratios (LLRs) in the decoding process have a wide dynamic range, the quantization error can degrade the error correction performance of the BP decoder. To reduce the quantization error in a uniform quantization scheme, we may need to use more bits for each LLR value which is not desirable due to the required memory space. In this paper, we design an efficient hardware architecture for non-uniformly quantized LLR messages in BP decoding where the arithmetic operations on logarithmically compressed messages are replaced with the mapping to precomputed results in lookup tables. By employing 5-bit non-uniform quantization, the designed BP decoder architecture reduces the required memory space by 37.5 percent compared to 8-bit uniform quantization while additionally improving the block error rate (BLER) by more than 0.1 dB in high SNR regimes. Compared to uniform quantization, the BLER improvement of the designed architecture is up to 0.4 dB when both quantization schemes use 5 bits for LLRs.