Abstract
In this paper, we present a low-complexity list sphere detection algorithm for achieving near-optimal a posteriori probability (APP) detection in an iterative detection and decoding (IDD). Motivated by the fact that the list sphere decoding searching a fixed number of candidates is computationally inefficient in many scenarios, we design a criterion to search lattice points with non-vanishing likelihood and then derive a hypersphere radius satisfying this condition. Further, in order to exploit the original sphere constraint as it is instead of using necessary conditioned version, we combine a probabilistic tree pruning strategy and the proposed list sphere search. Two features, tightened hypersphere radius and probabilistic tree pruning, collaborate and improve the search efficiency in a complementary fashion. Through simulations on 4x4 MIMO system, we show that the proposed method provides substantial reduction in complexity while achieving negligible performance loss over the conventional list sphere detection.
| Original language | English |
|---|---|
| Article number | 6213035 |
| Pages (from-to) | 2848-2857 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Wireless Communications |
| Volume | 11 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2012 Jun 13 |
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Keywords
- a posteriori probability
- complexity reduction
- Iterative detection and decoding
- multiple-input multiple-output system
- probabilistic radius tightening
- Sphere decoding
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Science Applications
- Applied Mathematics
Cite this
Soft-input soft-output list sphere detection with a probabilistic radius tightening. / Lee, Jaeseok; Shim, Byonghyo; Kang, Insung.
In: IEEE Transactions on Wireless Communications, Vol. 11, No. 8, 6213035, 13.06.2012, p. 2848-2857.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Soft-input soft-output list sphere detection with a probabilistic radius tightening
AU - Lee, Jaeseok
AU - Shim, Byonghyo
AU - Kang, Insung
PY - 2012/6/13
Y1 - 2012/6/13
N2 - In this paper, we present a low-complexity list sphere detection algorithm for achieving near-optimal a posteriori probability (APP) detection in an iterative detection and decoding (IDD). Motivated by the fact that the list sphere decoding searching a fixed number of candidates is computationally inefficient in many scenarios, we design a criterion to search lattice points with non-vanishing likelihood and then derive a hypersphere radius satisfying this condition. Further, in order to exploit the original sphere constraint as it is instead of using necessary conditioned version, we combine a probabilistic tree pruning strategy and the proposed list sphere search. Two features, tightened hypersphere radius and probabilistic tree pruning, collaborate and improve the search efficiency in a complementary fashion. Through simulations on 4x4 MIMO system, we show that the proposed method provides substantial reduction in complexity while achieving negligible performance loss over the conventional list sphere detection.
AB - In this paper, we present a low-complexity list sphere detection algorithm for achieving near-optimal a posteriori probability (APP) detection in an iterative detection and decoding (IDD). Motivated by the fact that the list sphere decoding searching a fixed number of candidates is computationally inefficient in many scenarios, we design a criterion to search lattice points with non-vanishing likelihood and then derive a hypersphere radius satisfying this condition. Further, in order to exploit the original sphere constraint as it is instead of using necessary conditioned version, we combine a probabilistic tree pruning strategy and the proposed list sphere search. Two features, tightened hypersphere radius and probabilistic tree pruning, collaborate and improve the search efficiency in a complementary fashion. Through simulations on 4x4 MIMO system, we show that the proposed method provides substantial reduction in complexity while achieving negligible performance loss over the conventional list sphere detection.
KW - a posteriori probability
KW - complexity reduction
KW - Iterative detection and decoding
KW - multiple-input multiple-output system
KW - probabilistic radius tightening
KW - Sphere decoding
UR - http://www.scopus.com/inward/record.url?scp=84865398040&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84865398040&partnerID=8YFLogxK
U2 - 10.1109/TWC.2012.060212.111311
DO - 10.1109/TWC.2012.060212.111311
M3 - Article
VL - 11
SP - 2848
EP - 2857
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
SN - 1536-1276
IS - 8
M1 - 6213035
ER -