A robust radio access technology classification scheme with practical considerations

This paper presents a robust radio access technology (RAT) classification framework for acquiring comprehensive knowledge on multiple types of coexisting wireless systems. It is built upon the combination of a group of RAT-specific feature metrics using maximum likelihood estimation (MIE) based decision rules. The classification scheme is enhanced by our proposed dimension cancellation (DIC) method for mitigating the noise uncertainty in practical receivers. Based on this framework, a signal classifier for TV white space (TVWS) is designed and implemented which is capable of detecting and classifying DVB-T, 3GPP LTE, IEEE 802.22, ECMA-392 and wireless microphone signals. The classifier is validated by both simulation and real-world experiment using a spectrum sensing testbed. The simulation and experiment performances agree with each other well, which confirms the effectiveness and robustness of the proposed classification scheme. The proposed classification framework will be further applied to our on-going projects kogLTE and ABSOLUTE in which the LTE network is enhanced with cognitive radio for operating in complex radio environment.