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In this paper, we investigate channel frequency response(CFR) matrix and interference-plus-noise covariance matrix(ICM) estimation in multiple-input multiple-output(MIMO) and orthogonal frequency division multiplexing(OFDM) systems to suppress co-channel interference at the receiver side. We employ least square criterion to perform the initial CFR estimation. Then we estimate the autocorrelation function of interference-plus-noise in the time domain, instead of estimating the ICM in the frequency domain directly.The autocorrelation function estimation has two steps. Firstly, we present the inherent relationship between the expectation of the sample autocorrelation function of the residual(SAFR) and the true autocorrelation function, which is actually a linear transformation. Based on this, we propose a compensating method. This compensation brings significant performance gains when the pilot OFDM symbol number is small. Secondly,since the compensated SAFR cannot be guaranteed to be an autocorrelation sequence(ACS), which will make the obtained ICM loss the positive semidefinite property, we utilize semidefinite programming(SDP) to find an ACS that is the nearest to the compensated SAFR. The SDP is solved in its dual form, which yields a significant reduced complexity. Finally the estimated ICM is reutilized to revise the CFR estimation. The estimated CFR and ICM show excellent interference suppression performances when being applied in an interference rejection combining receiver.
In this paper, we investigate channel frequency response (CFR) matrix and interference-plus-noise covariance matrix (ICM) estimation in multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) systems to suppress co-channel interference then we estimate the autocorrelation function of interference-plus-noise in the time domain, instead of estimating the ICM in the frequency domain directly. The autocorrelation function estimation has Firstly, we present the inherent relationship between the expectation of the sample autocorrelation function of the residual (SAFR) and the true autocorrelation function, which is actually a linear transformation. Based on this, we propose a compensating method. significant performance gains when the pilot OFDM symbol number is small. Secondly, since the compensated SAFR can not be guaranteed to be an autocorrelation sequence (ACS), which will make the acquired ICM loss the positive semidefinite property, we employ semidefinite programming (SDP) to find an ACS that is the nearest to the compensated SAFR. The SDP is solved in its dual form, which yields a Significant reduced complexity. Finally the estimated ICM is reutilized to revise the CFR estimation. The estimated CFR and ICM show excellent interference suppression performances when being applied in an interference rejection combining receiver.