Suboptimal Joint Multi-Parameter Estimation for Wireless Sensor Networks Over $\kappa-\mu$ Fading Channels

This paper explores joint multi-parameter estimation for intra-vehicle wireless sensor networks (IVWSNs) over $\kappa-\mu$ fading channels. Precise parameter estimation is crucial for reliable transmission and accurate sensing in IVWSNs, yet existing moment-based estimations lack accuracy. To address this, we propose a novel suboptimal multi-parameter estimation algorithm for robust transmission and sensing (SMARTS) based on sequential number-theoretic optimization (SNTO) within the framework of maximum likelihood estimation (MLE). Its performance approaches the Cramér-Rao lower bound (CRLB), and an efficiency-enhancement strategy is introduced to improve real-time applications. Simulation and data analysis demonstrate its accuracy, robustness, and practicality,