Data-aided Weight with Subcarrier Grouping for Adaptive Array Interference Suppression

The effect of additive noise on the channel state information (CSI) quality is a crucial issue in mobile communication systems. adaptive subcarrier grouping (ASG) for sample matrix inversion (SMI) based minimum mean square error (MMSE) array has been previously proposed. However, this method needs to know signal-to-noise ratio (SNR) advance set threshold, perform grouping, and take average, causing an insufficient number signal samples. As result, ability eliminate limited. In paper, we propose new data-aided weight calculation least (LMS) algorithm without SNR information, which increases decision results initial are obtained by SMI with then LMS applied reduce estimation as well amount computation. Simulation demonstrate that proposed scheme efficient approach improve Bit Error Rate (BER) performance under various Rician K factors.