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,

I. Introduction

The transmission and sensing quality of intra-vehicle wireless sensor networks (IVWSNs) hinges on channel characteristics in vehicle-mounted environments [1]. Before establishing a WSN, it is crucial to model the probability distribution of channels and characterize them through channel estimation to enhance its compatibility [2]. In the vehicle environment, dense and unevenly distributed occlusion and reflection objects create small-scale line-of-sight (LOS) and non-line-of-sight (NLOS) composite channels with rich multipath propagation. This complexity requires specialized channel models tailored to IVWSNs, as existing models for indoor [3], [4], outdoor [5], [6], and bus [7] scenarios may not apply directly.