Data-Aided SMI Algorithm Using Common Correlation Matrix for Adaptive Array Interference Suppression

Kosuke SHIMA
Chang-Jun AHN

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E104-A    No.2    pp.404-411
Publication Date: 2021/02/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.2020SDP0001
Type of Manuscript: Special Section PAPER (Special Section on Signal Design and Its Applications in Communications)
Category: Digital Signal Processing
adaptive array,  channel estimation,  sample matrix invesion,  decision feedback,  

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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 broadband wireless communication system such as orthogonal frequency division multiplexing (OFDM) which possesses lots of subcarriers, the computation complexity is serious problem when using SMI algorithm to suppress unknown interference. To resolve this problem, CCM based SMI algorithm was previously 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. 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 proposal accomplishes better bit error rate (BER) performance in spite of the minimum pilot symbols with a slight additional computation complexity.

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