A Low-Complexity Detection Method for Statistical Signals in OFDM Systems

Based on the bijective mapping between cyclostationary features and the cyclic delays in orthogonal frequency division multiplexing (OFDM) systems, cyclostationary signatures can be used for signal representation in statistical spectrum domain. However, the conventional detection method for this kind of statistical signals is hindered by huge computational complexity. In this letter, we propose a novel detection method for such statistical signals with cyclostationarity, which only extract local features on the cyclic autocorrelation function (CAF) coordinate plane. Analytical and simulation results show that the proposed method achieves significant performance gain over the conventional detection method under practical scenarios, even with the reduced computational complexity.