This paper investigates a channel estimator based on Gaussian mixture models (GMMs). We fit a GMM to given channel samples to obtain an analytic probability density function (PDF) which approximates the true channel PDF. Then, a conditional mean channel estimator corresponding to this approximating PDF is computed in closed form and used as an approximation of the optimal conditional mean estimator based on the true channel PDF. This optimal estimator cannot be calculated analytically because the true channel PDF is generally not available. To motivate the GMM-based estimator, we show that it converges to the optimal conditional mean estimator as the number of GMM components is increased. In numerical experiments, a reasonable number of GMM components already shows promising estimation results.

翻译：本文基于 Gausian 混合模型( GMM) 调查频道估计值。 我们将 GMM 适合给定频道样本以获得一个分析概率密度函数( PDF), 接近真实频道 PDF 。 然后, 一个与这个相似的 PDF 相对应的有条件平均频道估计值( PDF ), 以封闭的形式计算, 并用作基于真实频道 PDF 的最佳平均估计值的近似值。 这个最佳估计值无法进行分析, 因为真正的 PDF 通常不可用 。 为了激励基于 GM 的测算器, 我们显示它与最佳的有条件平均估计值相匹配, 因为 GMM 组件的数量有所增加 。 在数字实验中, 合理数量的 GM 组件已经显示出有希望的估计结果 。