TY - JOUR
T1 - Soft-input soft-output list sphere detection with a probabilistic radius tightening
AU - Lee, Jaeseok
AU - Shim, Byonghyo
AU - Kang, Insung
N1 - Funding Information:
Manuscript received July 11, 2011; revised December 23, 2011, March 20 and May 7, 2012; accepted May 9, 2012. The associate editor coordinating the review of this paper and approving it for publication was H. Shin. J. Lee and B. Shim are with the School of Information and Communication, Korea University, Seoul, Korea (e-mail: {jslee, bshim}@isl.korea.ac.kr). I. Kang is with Qualcomm Inc., CA 92121 USA (e-mail: in-sungk@qualcomm.com). This work is supported by the Basic Science Research Program through the National Research Foundation (NRF) funded by the MEST (No. 2011-0012525) and second Brain Korea 21 project. A part of this paper was presented in Globecom 2010 [1]. Digital Object Identifier 10.1109/TWC.2012.060212.111311
PY - 2012
Y1 - 2012
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 - Iterative detection and decoding
KW - Sphere decoding
KW - a posteriori probability
KW - complexity reduction
KW - multiple-input multiple-output system
KW - probabilistic radius tightening
UR - http://www.scopus.com/inward/record.url?scp=84865398040&partnerID=8YFLogxK
U2 - 10.1109/TWC.2012.060212.111311
DO - 10.1109/TWC.2012.060212.111311
M3 - Article
AN - SCOPUS:84865398040
SN - 1536-1276
VL - 11
SP - 2848
EP - 2857
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 8
M1 - 6213035
ER -