Data-aided SMI Algorithm using Common Correlation Matrix for Adaptive Array Interference Suppression

This paper proposes a novel weight derivation method to improve adaptive array interference suppression performance based on our previously conceived Sample Matrix Inversion algorithm using Common Correlation Matrix (CCM-SMI), by data-aided approach. In recent wireless communication system which possesses lots of subcarriers, the computation complexity is serious problem when using SMI algorithm. To mitigate this problem, CCM based SMI algorithm has been proposed. It computes the correlation matrix by the received time domain signals before fast Fourier transform (FFT). However, due to the limited number of pilot symbols, the estimated channel state information (CSI) is often incorrect. Therefore, it leads limited interference suppression performance. In this paper, we newly employ a data-aided channel state estimation. Decision results of received symbols are obtained by CCM-SMI and then fed-back to the channel estimator. It assists improving CSI estimation accuracy. Computer simulation result reveals that our method accomplishes better Bit Error Rate (BER) performance in spite of the minimum pilot symbols with only a little additional computation complexity.