TWO APPROACHES ON CARRIER FREQUENCY OFFSET ESTIMATION IN MIMO OFDM SYSTEMS

We consider the problem of joint carrier frequency offset and channel estimation
between transmitter and receiver in a frequency-selective channel MIMO-OFDM system. Recently
two high performance estimators based on the expectation-maximization (EM) algorithm have
been proposed. The main drawback of the maximum likelihood base algorithms, like EM
algorithm, is the high computational complexity. In this paper, we propose an extended Kalman
filter based estimator, which has higher performance than that of EM algorithm, while its
computational complexity is lower. In addition, the Particle Swarm Optimization (PSO) algorithm
is used for joint ML estimation of carrier frequency offset and channel parameters for the general
model. The proposed method has a lower computational complexity than that of traditional search
methods. Simulation results in comparison with Cramer-Rao bound show that the proposed
algorithms outperform the EM in all ranges of signal-to-noise ratio for both channel and frequency
offset estimations. Also, among them, the PSO algorithm is superior.