Multi-Parameter Estimation for κ-μ Shadowed Fading Model in Wireless Sensor Networks

In wireless sensor networks (WSNs), reliable link-quality estimation depends strongly on channel characteristics, which can be flexibly captured by the κ-μ shadowed fading model in complex closed metallic cavity (CMC) environments. However, a fundamental tension arises: conventional numerical methods for estimating the parameters of this model generally rely on the iterative solution of coupled nonlinear moment equations, which may introduce considerable computational cost and sensitivity to initialization and convergence settings, thereby limiting their applicability to rapid channel characterization in WSNs. We propose a closed-form multi-parameter estimation algorithm based on the method of moments that computes parameters directly without recourse to iterative numerical solvers. The key insight is that restructuring the normalized moment equations reveals a hidden quadratic structure, enabling explicit parameter extraction without iterative solver convergence requirements. Simulation results demonstrate sub-millisecond closed-form evaluation in the representative MATLAB implementation and substantial computational savings over the considered iterative solvers. Under the same prescribed composite parameter, it achieves essentially the same distribution-fitting accuracy as the corresponding constrained iterative estimator while requiring substantially less computation time. Furthermore, the reconstructed fading distributions provide accurate ergodic-capacity predictions, indicating the potential of the proposed method for computationally efficient link-quality characterization in WSNs.