Extreme Value Theory based OFDM Channel Estimation in the Presence of Narrowband Interference

Channel estimation in the presence of multitone narrowband interference (MNBI) in OFDM systems is addressed in this paper. While pilot based OFDM channel estimation in the presence of only thermal noise at the receiver is a Gaussian regression problem, the presence of MNBI leads to an outlier contaminated Gaussian regression problem. Since Gaussian probability density function (pdf) based maximum likelihood (ML) estimators are highly sensitive to outliers, we define a M estimator based on the theory of robust regression for channel estimation in the presence of MNBI. The proposed iterative M estimator minimizes the Huber's cost function for p iterations and then minimizes a cost function defined by a redescending M estimator based on extreme value theory in the last few iterations. Simulation results indicate that the proposed estimator outperforms both the Gaussian pdf based ML estimator and a M estimator based only on Huber's cost function.