Recognition of OFDM signal based on cyclic cumulant reconstruction with sub-Nyquist sampling

Abstract

In recent years, to meet the challenge of spectrum sensing with ultra wide band and big data in cooperative and cognitive radio networks, the theory of compressed sensing is introduced in, which can solve the problem of high sampling rate requirement due to Shannon-Nyquist sampling theory. In this paper, considering the property of signals’ cyclostationarity, we innovatively propose a method in OFDM signal detection using sub-Nyquist samples. By doing sparsity analysis combined with detection necessities, we present a partial-scale reconstruction method to reduce the recovery iteration and lower the algorithm complexity. Furthermore, we find out an equivalent cyclic cumulant calculation method for OFDM signals to simplify the calculation and lower the high memory consumption during signal processing. From the simulations we can see the optimized method introduced in effectively eliminates the constraints for compressed detection of OFDM signals and possesses a far-reaching significance in further researches and applications.