On the Cramer-Rao lower bound for RSS-based positioning in wireless cellular networks

An accurate position of a mobile user's location is substantial in wireless cellular networks with respect to location based services (LBS) implementation. Among the available parameters used to estimate a desired location, low cost measurement of received signal strength (RSS) makes it suitable for outdoor and indoor positioning. Our aim here is to look at the performance of a theoretically optimal user's position estimator. We examine the Cramer-Rao lower bound (CRLB), which sets the lowest variance of any unbiased estimator. Due to the oversimplification with respect to reliance on detailed cognizance of environment, we deploy theoretical free-space and empirical COST-231-Hata, Stanford University Interim (SUI) and ECC-33 path loss models that are dominantly used in real scenarios aiming to form the system model equations. The findings show that the CRLB on estimation precision depends on the underlying path loss exponents (PLE) and the relative positions of the mobile and base stations. The calculated bounds provide a benchmark against which it is possible to evaluate different positioning algorithms, techniques and estimators relying on the specified path loss models.