MSE Based Resource Optimization in Wireless Localization Networks

Abstract

Proper resource allocation can improve the positioning accuracy, as well as the energy efficiency of wireless localization networks. Most existing investigations are carried out based on the Cramer Rao Lower Bound (CRLB), which is not always achievable, especially in low signal to noise ratio (SNR) regimes. In this paper, we mainly focus on the mean square error (MSE) achieved directly from various localization algorithms. Due to the fact that, MSE can not be handled in a closed form, learning based frameworks are thus provided. Aiming at the exponential increased state space in the multi-agent-scenario, low complexity alternating solutions are provided. In addition, a robust scheme is given considering the measurement error, which provide the solution for ranging links with clock deviation or obstruction. Numerical results including both simulations and practical experiments validate our analysis, and show great improvements of the proposed frameworks.