Joint Maximum Likelihood Channel and Covariance Estimation for Co-Channel Interference Rejection in OFDM Systems

For co-channel interferences (CCI) rejection, a maximum likelihood (ML)-based framework for joint channel and interference-plus-noise covariance matrix (ICM) estimation is developed, where the expectation-maximization (EM) algorithm is deployed combined with an extended probabilistic principal component analysis (PPCA) method. The information about the channel and ICM contained in the received samples on both the data resource elements (REs) and the demodulation reference signals (DMRS) is fully utilized by the method, which significantly improves the estimation accuracy compared to the conventional channel and ICM estimation based on the DMRS. Simulation results demonstrate that without any prior information about the channel and ICM, the proposed method offers considerable performance improvement compared to the state-of-the-art method and in certain cases achieves performance close to the ideal case where the channel and ICM are perfectly known.