We present a novel approach for estimating the parameters of the extended generalized-$\mathcal{K}$ (EGK) distribution commonly used as a fading model in wireless and optical communications links. The proposed method is based on the Gibbs sampling technique and does not require solving nonlinear equations nor performing numerical integrations. Numerical and simulation results are presented showing that the estimated and original distributions are virtually indistinguishable and formal metrics like Kullback-Leibler (KL) divergence, the mean integrated squared bias (MISB), the mean integrated variance (MIV) and the mean integrated squared error (MISE) all show excellent agreement between the two as well.

我们提出了一种估计扩展广义参数的新方法$\mathcal{钾}$(EGK) 分布通常用作无线和光通信链路中的衰落模型。所提出的方法基于吉布斯采样技术，不需要求解非线性方程，也不需要进行数值积分。数值和模拟结果表明，估计分布和原始分布几乎无法区分，并且是形式化的度量，例如 Kullback-Leibler (KL) 散度、均值积分平方偏差 (MISB)、均值积分方差 (MIV) 和均值积分平方误差(MISE) 两者都表现出极好的一致性。