Abstract
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.
| Original language | English |
|---|---|
| Article number | 6701202 |
| Pages (from-to) | 343-346 |
| Number of pages | 4 |
| Journal | IEEE Signal Processing Letters |
| Volume | 21 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2014 Mar 1 |
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics
Cite this
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Exact ML criterion based on semidefinite relaxation for MIMO systems. / Kim, Minjoon; Park, Jangyong; Kim, Kilhwan; Kim, Jaeseok.
In: IEEE Signal Processing Letters, Vol. 21, No. 3, 6701202, 01.03.2014, p. 343-346.Research output: Contribution to journal › Article
TY - JOUR
T1 - Exact ML criterion based on semidefinite relaxation for MIMO systems
AU - Kim, Minjoon
AU - Park, Jangyong
AU - Kim, Kilhwan
AU - Kim, Jaeseok
PY - 2014/3/1
Y1 - 2014/3/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84894540863&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894540863&partnerID=8YFLogxK
U2 - 10.1109/LSP.2013.2297407
DO - 10.1109/LSP.2013.2297407
M3 - Article
VL - 21
SP - 343
EP - 346
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
SN - 1070-9908
IS - 3
M1 - 6701202
ER -