High-SNR Asymptotics of Mutual Information for Discrete Constellations With Applications to BICM

IEEE Transactions on Information Theory(2014)

引用 49|浏览67
暂无评分
摘要
ymptotic expressions of the mutual information between any discrete input and the corresponding output of the scalar additive white Gaussian noise channel are presented in the limit as the signal-to-noise ratio (SNR) tends to infinity. Asymptotic expressions of the symbol-error probability (SEP) and the minimum mean-square error (MMSE) achieved by estimating the channel input given the channel output are also developed. It is shown that for any input distribution, the conditional entropy of the channel input given the output, MMSE, and SEP have an asymptotic behavior proportional to the Gaussian Q-function. The argument of the Q-function depends only on the minimum Euclidean distance (MED) of the constellation and the SNR, and the proportionality constants are functions of the MED and the probabilities of the pairs of constellation points at MED. The developed expressions are then generalized to study the high-SNR behavior of the generalized mutual information (GMI) for bit-interleaved coded modulation (BICM). By means of these asymptotic expressions, the long-standing conjecture that Gray codes are the binary labelings that maximize the BICM-GMI at high SNR is proven. It is further shown that for any equally spaced constellation whose size is a power of two, there always exists an anti-Gray code giving the lowest BICM-GMI at high SNR.
更多
查看译文
关键词
symbol error probability,awgn channels,binary labelings,gaussian q function,bit-interleaved coded modulation,channel output,channel input,high-snr asymptotics,signal to noise ratio,additive white gaussian noise channel,conditional entropy,bit interleaved coded modulation,discrete constellations,proportionality constants,bicm,mmse,least mean squares methods,gray code,minimum-mean square error,high snr asymptotics,asymptotic expressions,anti-gray code,generalized mutual information,minimum euclidean distance,mutual information,entropy,error statistics,scalar additive white gaussian noise channel,gray codes,minimum mean square error,interleaved codes,allocation,power
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要