A Decomposition Mapping based Quantized Belief Propagation Decoding for 5G LDPC Codes

Since the degree-1 variable nodes (VNs) in the low-density parity-check (LDPC) codes for the 5th generation (5G) mobile communications are very sensitive to be erroneous, the quantized min-sum (QMS) and offset min-sum (QOMS) decodings suffer from poor error-correction performance due to the imprecise estimation of the check-to-variable (C2V) message magnitudes. For this reason, this paper proposes a decomposition mapping based quantized belief propagation (DM-QBP) decoding for 5G LDPC codes. In order to reduce the computation complexity, the check node (CN) update function is realized by look-up tables (LUTs). Furthermore, a decomposition method is presented to eliminate the high memory cost of using large tables without performance loss. Therefore, the CN update function can be implemented based only on simple mappings and fixed-point additions. Simulation results show that, the DM-QBP decoder considerably outperforms the state-of-the-art ones for several 5G LDPC codes. With a small number of quantization bits, its performance is even better than the floating-point OMS decoding in some cases.