A. Yu. Uglovskii, I. A. Melnikov, I. A. Alexeev, A. A. Kureev
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引用次数: 0
摘要
低密度奇偶校验(LDPC)码分析的一个关键问题是估计在高信噪比(SNR)下出现的极低误差底限。重要度采样 (IS) 方法是解决这一问题的常用方法。现有的方法通常使用具有移动平均值的正态采样概率密度函数 (PDF),这会产生较大的估计方差。与此相反,均匀分布在整个范围内的样本概率相等,因此应能减小方差,但却会导致估计值有偏差。本文提出了一种改进的 IS 方法(IS-U),允许将均匀分布作为采样 PDF,并证明这种估计方法优于传统方法。此外,本文还证明了现有标准无法用于评估 IS-U 在整个信噪比范围内的准确性。为了解决这个问题,本文提出了一种新的衡量标准,它只使用收敛率,而不依赖于真实数据。
Effective Error Floor Estimation Based on Importance Sampling with the Uniform Distribution
A key problem of low-density parity-check (LDPC) codes analysis is estimation of an extremely low error floor that occurs at a high level of the signal-to-noise ratio (SNR). The importance sampling (IS) method is a popular approach to address this problem. Existing works typically use a normal sampling probability density function (PDF) with shifted mean, which yields a large variance of the estimate. In contrast, uniform distribution has equally probable samples on the entire range and thus should reduce the variance, but results in a biased estimation. This paper proposes a modified IS approach (IS-U) that allows considering the uniform distribution as a sampling PDF, and shows that this estimation is better than the traditional one. Also, this paper demonstrates that the existing criteria cannot be applied to evaluate the accuracy of the IS-U on the whole SNR range. To address this issue, a new metric is proposed, which uses only the convergence rate and does not depend on the true data.
期刊介绍:
Problems of Information Transmission is of interest to researcher in all fields concerned with the research and development of communication systems. This quarterly journal features coverage of statistical information theory; coding theory and techniques; noisy channels; error detection and correction; signal detection, extraction, and analysis; analysis of communication networks; optimal processing and routing; the theory of random processes; and bionics.