Likelihood Estimation for Reduced-Complexity ML Detectors in a MIMO System

Abstract

This paper proposes a likelihood estimation method for reduced-complexity maximum-likelihood (ML) detectors in a multiple-input multiple-output (MIMO) system. Reduced-complexity ML detectors, e.g., sphere decoder (SD) and QR decomposition (QRD)-M algorithm, are very promising candidates as a MIMO detector because they can estimate the ML or quasi-ML symbol with very low computational complexity. However, they may lose likelihood information about signal vectors having the opposite bit to hard decisions. Therefore, bit error rate performances 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. Computer simulation confirms that the proposed method provides excellent decoding performance, keeping the advantage of computational cost of the reduced-complexity ML detectors.