A novel likelihood particle filter is proposed to estimate the carrier frequency offset for uplink MIMO-OFDMA systems. In the proposed scheme, the likelihood is employed to approximate to the posterior rather than the prior, because the likelihood is more similar to the posterior than the prior. The importance density is an approximation to the posterior. Therefore, using a better approximation based on the likelihood, rather than the prior, can be expected to achieve better performance. In addition, the multiuser interference (MUI) cancellation strategy is combined in every recursion. Simulation results show that the proposed algorithm has lower mean square error (MSE) than the regular particle filter and the extended Kalman filter, while approaching the Cramer-Rao Bound (CRB) closely with quite reduced complexity.
['Zheng Jiang', 'Yang Zhang', 'Xin Zhang', 'Dacheng Yang']
Carrier Frequency Offset Estimation Using Likelihood Particle Filter for Uplink MIMO-OFDMA Systems