TY - GEN
T1 - Applications of SDR exact-ML criterion to tree-searching detection for MIMO systems
AU - Kim, Minjoon
AU - Kim, Jaeseok
PY - 2014/1/23
Y1 - 2014/1/23
N2 - In previous work, we proposed the positive diagonal values (PDV) criterion, which is an exact-ML criterion of semidefinite relaxation (SDR) optimality condition. In this paper, we apply the PDV criterion to the tree-searching based MIMO detection by two ways. The first application is node-pruning algorithm for depth first search such as sphere decoding (SD). The proposed node-pruning algorithm using PDV criterion is not based on the Euclidean distance mostly used for node-pruning algorithm, instead, it uses an absolute test in each node so that it can be worked independently with many existing node-pruning algorithms. Furthermore, the proposed node-pruning algorithm can guarantee the exact-ML performance and reduce the number of nodes visited significantly. The second application is K-best algorithm of breadth first search. The proposed K-best algorithm takes K candidates at each stage based on PDV criterion. As a result, the proposed K-best algorithm can achieve near-ML performance.
AB - In previous work, we proposed the positive diagonal values (PDV) criterion, which is an exact-ML criterion of semidefinite relaxation (SDR) optimality condition. In this paper, we apply the PDV criterion to the tree-searching based MIMO detection by two ways. The first application is node-pruning algorithm for depth first search such as sphere decoding (SD). The proposed node-pruning algorithm using PDV criterion is not based on the Euclidean distance mostly used for node-pruning algorithm, instead, it uses an absolute test in each node so that it can be worked independently with many existing node-pruning algorithms. Furthermore, the proposed node-pruning algorithm can guarantee the exact-ML performance and reduce the number of nodes visited significantly. The second application is K-best algorithm of breadth first search. The proposed K-best algorithm takes K candidates at each stage based on PDV criterion. As a result, the proposed K-best algorithm can achieve near-ML performance.
UR - http://www.scopus.com/inward/record.url?scp=84988273919&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988273919&partnerID=8YFLogxK
U2 - 10.1109/ICSPCS.2014.7021135
DO - 10.1109/ICSPCS.2014.7021135
M3 - Conference contribution
AN - SCOPUS:84988273919
T3 - 2014, 8th International Conference on Signal Processing and Communication Systems, ICSPCS 2014 - Proceedings
BT - 2014, 8th International Conference on Signal Processing and Communication Systems, ICSPCS 2014 - Proceedings
A2 - Wysocki, Beata J.
A2 - Wysocki, Tadeusz A.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Signal Processing and Communication Systems, ICSPCS 2014
Y2 - 15 December 2014 through 17 December 2014
ER -