Low-complexity SIC-MMSE for joint multiple-input multiple-output detection


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Resumo

Iterative detection and decoding based on a soft interference cancellation–minimum mean squared error (SIC-MMSE) scheme provides efficient performance for coded MIMO systems. The critical computational burden for a SIC-MMSE detector in a MIMO system lies in the multiple inverse operations of the complex matrix. In this paper, we present a new method to reduce the complexity of the SIC-MMSE scheme based on a MIMO detection scheme that uses a single universal matrix with a non-layer-dependent inversion process. We apply the Taylor series expansion approach and derive a simple non-layer-dependent inverse matrix. The simulation results reveal that the utilization of the universal matrices presented in this paper produces almost the same performance as the conventional SIC-MMSE scheme but with low computational complexity.

Sobre autores

F. Kamaha Ngayahala

Electronics and Information Engineering Chonbuk University, Republic of Korea

Email: saqibbhatti@ieee.org
República da Coreia, Jeonju, 54896

S. Ahmed

Computer and Telecommunication Engineering Dawood University of Engineering and Technology, Islamic Republic of Pakistan

Email: saqibbhatti@ieee.org
Paquistão, Karachi, 74800

D. Saqib Bhatti

Computer and Telecommunication Engineering Dawood University of Engineering and Technology, Islamic Republic of Pakistan

Autor responsável pela correspondência
Email: saqibbhatti@ieee.org
Paquistão, Karachi, 74800

N. Saeed

Department of Computer Science Iqra National University, Islamic Republic of Pakistan

Email: saqibbhatti@ieee.org
Paquistão, Peshavar

N. Kaimkhani

Electrical Engineering Bahria University of Engineering and Technology, Islamic Republic of Pakistan

Email: saqibbhatti@ieee.org
Paquistão, Karachi

M. Rasheed

Computer and Telecommunication Engineering Dawood University of Engineering and Technology, Islamic Republic of Pakistan

Email: saqibbhatti@ieee.org
Paquistão, Karachi, 74800


Declaração de direitos autorais © Pleiades Publishing, Inc., 2017

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