Self-Estimation of Path-Loss Exponent in Wireless Networks and Applications

TL;DR

This paper introduces two novel self-estimation methods for the path-loss exponent in wireless networks, enabling nodes to independently estimate propagation parameters using only RSS data, enhancing robustness and security.

Contribution

It presents the first self-estimator for PLE using a linear regression model and closed-form TLS methods, eliminating the need for external assistance or auxiliary devices.

Findings

Estimators are reliable even in harsh environments.

Methods are simple and easily integrated into wireless stacks.

Potential applications include secure localization and routing.

Abstract

The path-loss exponent (PLE) is one of the most crucial parameters in wireless communications to characterize the propagation of fading channels. It is currently adopted for many different kinds of wireless network problems such as power consumption issues, modelling the communication environment, and received signal strength (RSS)-based localization. PLE estimation is thus of great use to assist in wireless networking. However, a majority of methods to estimate the PLE require either some particular information of the wireless network, which might be unknown or some external auxiliary devices, such as anchor nodes or the Global Positioning System. Moreover, this external information might sometimes be unreliable, spoofed, or difficult to obtain. Therefore, a self-estimator for the PLE, which is able to work independently, becomes an urgent demand to robustly and securely get a grip on the PLE for various wireless network applications. This paper is the first to introduce two methods that can solely and locally estimate the PLE. To start, a new linear regression model for the PLE is presented. Based on this model, a closed-form total least squares (TLS) method to estimate the PLE is first proposed, in which, with no other assistance or external information, each node can estimate the PLE merely by collecting RSSs. Second, to suppress the estimation errors, a closed-form weighted TLS method is further developed, having a better performance. Due to their simplicity and independence of any auxiliary system, our two proposed methods can be easily incorporated into any kind of wireless communication stack. Simulation results show that our estimators are reliable, even in harsh environments, where the PLE is high. Many potential applications are also explicitly illustrated in this paper, such as secure RSS-based localization, kth nearest neighbour routing, etc.