An Efficient Algorithm for CFO Estimation in FBMC and GFDM under SUI Channel Models

Abstract

Like other multicarrier schemes, the most likely 5G schemes, Filter Bank Multicarrier (FBMC) and Generalized Frequency Division Multiplexing (GFDM) suffer from carrier frequency offset (CFO) which causes significant degradation in the BER of either modulation scheme. After presenting a brief block level implementation architecture and representation of both FBMC and GFDM, we propose novel usage of Data-Aided (DA) efficient algorithm based on modified Maximum Likelihood (ML) principle for the estimation of CFO in FBMC and GFDM schemes. The estimation performance is analyzed in terms of Mean Square Error (MSE), both in the presence of Gaussian noise and stanford university interim (SUI) channel models. To verify the effectiveness of the proposed estimation, the performance is compared with Cramer-Rao Lower Bound (CRLB) and it is shown that MSE achieves CRLB for moderate and higher values of SNR. we also compare the performance of the proposed estimator with data-aided near maximum likelihood estimator for FBMC under 6-tap Rician channel and Kalman filter based iterative algorithm for GFDM in Gaussian noise.