APPROXIMATE ITERATIVE BAYES OPTIMAL ESTIMATES FOR HIGH-RATE SPARSE SUPERPOSITION CODES
APPROXIMATE ITERATIVE BAYES OPTIMAL ESTIMATES FOR HIGH-RATE SPARSE SUPERPOSITION CODES
复制标题
高速稀疏叠加码的近似迭代贝叶斯最优估计
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
A. Barron
中科院分区：
文献类型：
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作者：
Sanghee Cho;A. Barron
本文研究了加性白色高斯噪声信道下具有迭代项选择的稀疏叠加码。特别是，我们认为软决策解码器与贝叶斯最优估计在每一步，假设统一的优先选择的条款发送。贝叶斯最优估计公式，并显示有一个鞅属性，提供了替代表示的后验概率的错误。由于贝叶斯最优估计是不可行的，提出了一种近似方法。我们分析的近似方法的性能相比，不可行的估计。
This paper is concerned with sparse superposition codes with iterative term selection for additive white Gaussian noise channel with power control. In particular, we consider a soft decision decoder with Bayes optimal estimates at each step, presuming uniform prior on the choice of the terms that are sent. Bayes optimal estimates are formulated and shown to have a Martingale property that provides alternative representations of a posterior probability of error. Since the Bayes optimal estimates are infeasible, an approximation method is suggested. We analyze the performance of the approximation method in comparison with the infeasible estimates.