Abstract

Many decoding schemes rely on the log-likelihood ratio (LLR) whose derivation depends on the knowledge of the noise distribution. In dense and heterogeneous network settings, this knowledge can be difficult to obtain from channel outputs. Besides, when interference exhibits an impulsive behavior, the LLR becomes highly non-linear and, consequently, computationally prohibitive. In this paper, we directly estimate the LLR, without relying on the interference plus noise knowledge. We propose to select the LLR in a parametric family of functions, flexible enough to be able to represent many different communication contexts. It allows limiting the number of parameters to be estimated. Furthermore, we propose an unsupervised estimation approach, avoiding the need of a training sequence. Our estimation method is shown to be efficient in large variety of noises and the receiver exhibits a near-optimal performance.

中文翻译：

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具有未知噪声分布的无监督LLR估计

摘要

许多解码方案依赖于对数似然比（LLR），其推导取决于对噪声分布的了解。在密集且异构的网络设置中，可能很难从通道输出中获得此知识。此外，当干扰表现出脉冲行为时，LLR变得高度非线性，因此，在计算上是禁止的。在本文中，我们直接估计LLR，而无需依赖干扰和噪声知识。我们建议在参数功能系列中选择LLR，该功能足够灵活以能够表示许多不同的通信上下文。它允许限制要估计的参数数量。此外，我们提出了一种无监督的估计方法，避免了训练序列的需要。