Copyright © 2008 The Institute of Electronics, Information and Communication Engineers
Regular Section -- Papers -- Wireless Communication Technologies |
Likelihood Estimation for Reduced-Complexity ML Detectors in a MIMO Spatial-Multiplexing System
1 The authors are with the Information Technology R&D Center, Mitsubishi Electric Corporation, Kamakura-shi, 247-8501 Japan. E-mail: Higashinaka.Masatsugu{at}ea.MitsubishiElectric.co.jp, 2 The author is with Mitsubishi Electric ITE-TCL, Rennes, France.
This paper proposes a likelihood estimation method for reduced-complexity maximum-likelihood (ML) detectors in a multiple-input multiple-output (MIMO) spatial-multiplexing (SM) system. Reduced-complexity ML detectors, e.g., Sphere Decoder (SD) and QR decomposition (QRD)-M algorithm, are very promising as MIMO detectors because they can estimate the ML or a quasi-ML symbol with very low computational complexity. However, they may lose likelihood information about signal vectors having the opposite bit to the hard decision and bit error rate performance of the reduced-complexity ML detectors are inferior to that of the ML detector when soft-decision decoding is employed. This paper proposes a simple estimation method of the lost likelihood information suitable for the reduced-complexity ML detectors. The proposed likelihood estimation method is applicable to any reduced-complexity ML detectors and produces accurate soft-decision bits. Computer simulation confirms that the proposed method provides excellent decoding performance, keeping the advantage of low computational cost of the reduced-complexity ML detectors.
Key Words: MIMO, sphere decoding, QRD-M algorithm, soft-decision outputs, computational complexity
Manuscript received March 27, 2007. Manuscript revised September 26, 2007.
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