OFDMA system identification using cyclic autocorrelation function: A software defined radio testbed

In this paper, cyclic autocorrelation function (CAF) is used for signal type identification. Orthogonal frequency division multiple access (OFDMA) signals with different cyclic prefix lengths are generated by using software defined radio nodes. According to test results, by using the OFDMA frame structure, it is possible to distinguished between OFDMA signals, hence signal parameters can be blindly differentiated. Additionally, jamming signal and OFDMA signals can also be distinguished from each other. Real-time measurement results are given to demonstrate the performance of the investigated approach.