Threshold Selection Algorithm Based on Skewness and Standard Deviation Using Back Propagation Artificial Neural Networks in the 60GHz Wireless Communication Systems

Accurate localization has gained significant interest in the field of sensor networks, impulse radio 60GHz signals which is low cost, low complexity are even much more practical for ranging, localization and tracking systems because of the high time and multipath resolution and so on. Typically, accurate Time of Arrival (TOA) estimation of the 60GHz signals is very important. In order to improve the precision of the TOA estimation, a new threshold selection algorithm using Back Propagation Artificial Neural Networks (BP-ANN) is proposed which is based on a joint metric of Skewness and Standard Deviation after Energy Detection. The best threshold based on the signal-to-noise ratio (SNR) is investigated and the effects of the integration period and channel model are examined. Simulation results are presented which show that for the IEEE802.15.3c channel models CM1.1 and CM2.1, the proposed BP-ANN algorithm provides better precision and robustness in both high and low SNR environments than other ED-based algorithms.