Suboptimal Joint Multi-Parameter Estimation and Performance Analysis for Wireless Sensor Networks over κ-μ Fading channels

This paper investigates suboptimal multi-parameter estimation and performance analysis for wireless sensor networks (WSNs) over κ-μ fading channels, with a particular focus on deployments in communications within confined metallic environments, such as photolithography machines and machine tools. The κ-μ model offers a flexible framework for characterizing complex fading conditions, making accurate parameter estimation crucial for reliable communication in industrial automation, aerospace monitoring, and other WSN applications. To overcome the inaccuracies of moment-based estimation and the computational challenges of maximum likelihood estimation (MLE) due to the modified Bessel function, we propose a novel suboptimal multi-parameter estimation algorithm for robust transmission and sensing (SMARTS) that integrates sequential number-theoretic optimization within a MLE framework to achieve high accuracy. An intuitive and efficient search strategy further optimizes the estimation process, reducing complexity and enabling real-time deployment in resource-constrained sensor nodes. Theoretical analysis, including the derivation of the Cramér-Rao Lower Bound (CRLB), demonstrates that the proposed algorithm closely approaches the performance limits of unbiased estimators. Additionally, we assess the impact of estimation accuracy on key system performance metrics, analyzing the effects of both overestimation and underestimation. Simulation results confirm the superiority of the proposed algorithm over existing methods in terms of estimation accuracy, noise resilience, and computational efficiency, highlighting its potential to enhance WSN reliability in confined metallic environments.