Exact ML Criterion Based on Semidefinite Relaxation for MIMO Systems
IEEE Signal Process. Lett.(2014)
摘要
In this letter, we propose an exact maximum likelihood (ML) criterion based on semidefinite relaxation (SDR) in multiple-input multiple-output systems. Although a conventional SDR criterion for determining whether a symbol is the ML solution exists, its results cannot be guaranteed when noise is present. In place of the conventional criterion's positive semidefinite (PSD) discriminant, we propose a new, exact ML criterion based on the condition that all diagonal values are positive (PDV), a simple characteristic and necessary condition of PSD. The proposed criterion has a lower calculation complexity for testing than does a PSD and can ensure that the ML solution is always satisfactory.
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关键词
pdv,exact maximum likelihood criterion,maximum likelihood detection (mld),multiple-input multiple-output systems,exact ml criterion,calculation complexity,mathematical programming,semidefinite relaxation,maximum likelihood detection,positive diagonal values,ml solution,sdr,positive semidefinite discriminant,mimo communication,psd,semidefinite relaxation (sdr),mimo systems,mimo,detectors,vectors,noise
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