Smart Device Localization Under α-KMS Fading Environment using Feedback Distance based Gradient Ascent

Abstract

In this paper, we propose a novel method for location estimation of smart devices considering a generic shadowed $\alpha-\kappa-\mu$ distribution based $\alpha$-KMS fading environment, which is not considered for localization hitherto. Most of the existing path loss-based methods utilize a standard log-normal model only for localization; however, fading effects need to be considered to appropriately model the Received Signal Strength (RSS) values. Some of the localization methods utilize standard fading models such as Rayleigh, Nakagami-m, and Rician, to name a few; however, such assumptions lead to erroneous location estimates. The generic location estimator is applicable for all environments and provides accurate location estimates with correct estimates of $\alpha-\kappa-\mu$. We propose a feedback-induced gradient ascent algorithm based on feedback distance that maximizes the derived log-likelihood estimate of the actual location. The proposed method also addresses the non-convex nature of the maximum likelihood estimator and is computationally efficient. The performance is evaluated on a simulated testbed, and the localization results outperform existing state-of-the-art methods.