科研之友
微信
新浪微博
Facebook
分享链接
摘要
For iterative mean-square error (MSE) quantizers with alphabet size M = 2(K) using low-density generator-matrix (LDGM) code constructions, an efficient recovery algorithm is proposed, which adjusts the priors used in belief propagation (BP) to limit the impact of previous non-ideal decimation steps. Based on an analysis of the BP process under ideal or non-ideal decimation, the algorithm first estimates the conditional probability distributions describing the effect of non-ideal decimation, then adjusts the priors to make the distributions match the ideal situation. As shown in simulation results, the recovery algorithm can improve quantization performance greatly, reducing the shaping loss to as low as 0.012 dB, while the increase in computational complexity is modest thanks to the use of FFT techniques.
- 出版日期2011-7
- 单位上海交通大学
