1 research outputs found
Generalized Mutual Information-Maximizing Quantized Decoding of LDPC Codes with Layered Scheduling
In this paper, we propose a framework of the mutual information-maximizing
(MIM) quantized decoding for low-density parity-check (LDPC) codes by using
simple mappings and fixed-point additions. Our decoding method is generic in
the sense that it can be applied to LDPC codes with arbitrary degree
distributions, and can be implemented based on either the belief propagation
(BP) algorithm or the min-sum (MS) algorithm. In particular, we propose the MIM
density evolution (MIM-DE) to construct the lookup tables (LUTs) for the node
updates. The computational complexity and memory requirements are discussed and
compared to the LUT decoder variants. For applications with low-latency
requirement, we consider the layered schedule to accelerate the convergence
speed of decoding quasi-cyclic LDPC codes. In particular, we develop the
layered MIM-DE to design the LUTs based on the MS algorithm, leading to the MIM
layered quantized MS (MIM-LQMS) decoder. An optimization method is further
introduced to reduce the memory requirement for storing the LUTs. Simulation
results show that the MIM quantized decoders outperform the state-of-the-art
LUT decoders in the waterfall region with both 3-bit and 4-bit precision over
the additive white Gaussian noise channels. Moreover, the 4-bit MIM-LQMS
decoder can approach the error performance of the floating-point layered BP
decoder within 0.3 dB over the fast fading channels.Comment: 13 pages main body, 8 figures, journal manuscrip