Cyclostationarity-Based Detection of LTE OFDM Signals for Cognitive Radio Systems

In this paper, a distinctive cyclostationarity-based feature of the Long Term Evolution (LTE) Orthogonal Frequency Division Multiplexing (OFDM) signals used in the Frequency Division Duplex (FDD) downlink transmission is proved, and further employed for their detection. This relates to the existence of the reference signals (RSs) used for channel estimation and cell search/acquisition purposes. The analytical closed form expressions for the RS-induced cyclic autocorrelation function (CAF) and cyclic frequencies (CFs) are derived. Based on these findings, a signal detection algorithm is then developed. Simulation results show that the proposed algorithm achieves a good detection performance for low signal-to-noise ratios (SNRs), short sensing times, and under diverse channel conditions.