Original scientific paper
https://doi.org/10.24138/jcomss-2022-0109
Data-aided Weight with Subcarrier Grouping for Adaptive Array Interference Suppression
He He
; Chiba University, Japan
Jun-Han Wang
; Chiba University, Japan
Shun Kojima
; Utsunomiya University, Japan
Kazuki Maruta
; Tokyo University of Science, Japan
Chang-Jun Ahn
; Chiba University, Japan
Abstract
The effect of additive noise on the channel state information (CSI) quality is a crucial issue in mobile communication systems. The adaptive subcarrier grouping (ASG) for sample matrix inversion (SMI) based minimum mean square error (MMSE) adaptive array has been previously proposed. However, this method needs to know the signal-to-noise ratio (SNR) in advance to set the threshold, perform grouping, and take the average, causing an insufficient number of signal samples. As a result, the ability to eliminate noise is limited. In this paper, we propose a new method based on data-aided weight calculation and the least mean square (LMS) algorithm without SNR information, which increases the number of samples. The decision results and initial weight are obtained by the SMI method with subcarrier grouping, and then the LMS method with subcarrier grouping is applied to reduce the channel estimation error as well as the amount of computation. Simulation results demonstrate that the proposed scheme is an efficient approach to improve Bit Error Rate (BER) performance under various Rician K factors.
Keywords
Noise effect; sample matrix inversion; subcarrier grouping; decision feedback; least mean square
Hrčak ID:
291235
URI
Publication date:
30.12.2022.
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