Learning Quantization in LDPC Decoders
This work addresses the need for efficient hardware implementation of decoders in communication systems, representing an incremental improvement with specific gains.
The paper tackles the problem of optimizing message quantization for low-complexity belief propagation decoding in LDPC codes, achieving error-rate performance within 0.2 dB of floating-point decoding at an average message bitwidth of 3.1 bits.
Finding optimal message quantization is a key requirement for low complexity belief propagation (BP) decoding. To this end, we propose a floating-point surrogate model that imitates quantization effects as additions of uniform noise, whose amplitudes are trainable variables. We verify that the surrogate model closely matches the behavior of a fixed-point implementation and propose a hand-crafted loss function to realize a trade-off between complexity and error-rate performance. A deep learning-based method is then applied to optimize the message bitwidths. Moreover, we show that parameter sharing can both ensure implementation-friendly solutions and results in faster training convergence than independent parameters. We provide simulation results for 5G low-density parity-check (LDPC) codes and report an error-rate performance within 0.2 dB of floating-point decoding at an average message quantization bitwidth of 3.1 bits. In addition, we show that the learned bitwidths also generalize to other code rates and channels.