Identification of Cellular Networks for Intelligent Radio Measurements

Abstract

Signal identification systems recognize the type of the received signal usually without a priori information of some of the signal parameters and without the requirement of preprocessing tasks, such as frequency and timing recovery. These instruments have been employed in different military and commercial applications, such as spectrum surveillance, electronic warfare, and software-defined and cognitive radios. The focus of this paper is automatic instruments that can identify cellular network signals. Novel identification features are presented, which are based on the statistical properties and characteristic features of the candidate signals. In particular, the signal cumulative distribution function is employed as an identification feature for the global system for mobile communication and long term evolution (LTE) downlink signals. The presence of the cyclic prefix is exploited to identify the LTE uplink signal, while the estimated signal bandwidth is used to differentiate between the universal mobile telecommunication system and code division multiple access 2000 signals. Experimental tests are performed to verify the validity of the proposed identification method. The experimental results show that the identification method achieves very good performance with short observation intervals leading to improved response time under different channel conditions. Moreover, the presented method is robust to timing and frequency offsets.