The Adaptive GMM for Rician Parameters Estimation in Industrial IoT Systems with Reverse KLD to Distinguish Redundant Kernels

In industrial environments, the wireless link of IoT systems often experiences complex channel fading effects, making accurate online estimation of link quality crucial for improving system performance. Using Gaussian Mixture Model (GMM) to fit I/Q symbols allows estimation of Rician channel parameters, but traditional GMMs typically rely on prior knowledge of the number of Gaussian components to ensure clustering accuracy, posing challenges for adaptive channel modulation schemes in industrial settings. This paper proposes an adaptive Gaussian mixture model based on Kullback-Leibler divergence (KLD), which autonomously determines the optimal number of clusters through iterative evaluation, achieving optimal clustering performance. Firstly, this study proposes the utilization of forward KLD as an optimization target, leveraging its known optimal prior of zero to avoid local optima. Secondly, the redundancy in the number of clusters is assessed using reverse KLD constructed with the single Gaussian distribution. These improvements ensure that the GMM converges correctly to the global optimum regardless of the initial cluster count settings.