Compressive Sensing-Based Channel Estimation for FBMC-OQAM System Under Doubly Selective Channels

Filter-bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) has been considered as an alternative scheme to orthogonal frequency division multiplexing (OFDM). However, the traditional channel estimation techniques of the OFDM cannot be directly applied to the FBMC-OQAM system because of the real-field orthogonality of the FBMC-OQAM signals. Although traditional channel estimation techniques, such as least square (LS) and minimum mean square error (MMSE), are widely applied to the FBMC-OQAM system via canceling the intrinsic imaginary interference from adjacent data symbols, the LS algorithm is subject to noise enhancement and it results in large mean square error (MSE), while the mmse algorithm needs to know the statistical information of channel in advance. Due to sparsity of the wireless channel, channel estimation is investigated as a compressive sensing (CS) problem. In this paper, we first introduce the coding method to cancel intrinsic imaginary interference for the FBMC-OQAM system. Then, a novel sparse adaptive subspace pursuit (SASP) method is proposed to improve the accuracy of LS channel estimation. Finally, we develop two distinctive algorithms, namely, auxiliary pilot (AP) SASP and coding-SASP, to estimate channel frequency respond (CFR) in the FBMC-OQAM system. The simulation results show that the AP-SASP and coding-SASP algorithms can offer a low complexity and fewer measurements compared with conventional orthogonal matching pursuit (OMP) and regularized OMP (ROMP) methods. Moreover, the proposed AP-SASP and coding-SASP algorithms have a better bit error ratio (BER) performance than the conventional OMP and ROMP methods for FBMC system in the doubly selective channels.