Adaptive GMM for Rician Parameters Estimation in Industrial Temporal Fading Channel

Accurate online link quality metrics represented by the Rician parameter are critical to enhancing the reliability of industrial wireless networks subject to temporal fading channels. The Rician parameters can be estimated by fitting the received I/Q symbols with GMM (Gaussian Mixture Model). However, the classical Expectation-Maximization estimations of GMM rely on the preset hyper-parameter of kernel numbers to guarantee the convergence, making it hard to work under adaptive modulation schemes. To address this challenge, we first reveal that the derivative of likelihood is less capable of representing the global optimal, which leads to the well-known local optimal problem and the failure to recognize the false convergence caused by incorrectly configured kernel numbers. A new empirical metric derived from KLD (Kullback-Leibler divergence) has been proposed to identify the local optimal convergence, as well as a new metric tuple to discriminate redundant kernels. A novel estimation algorithm has then been designed to shift the number of kernels from the preset hyper-parameter to the adjustable parameter. This improvement guarantees the global optimal convergence of the GMM with any initial number of kernels. Extensive experiments demonstrate that the proposed method achieves over ten times better accuracy, while requires less than half the iterations.