NDLI: Schnorr-Euchner sphere decoder with statistical pruning for MIMO systems

Content Provider	IEEE Xplore Digital Library
Author	Ahn, J. Heung-No Lee Kiseon Kim
Copyright Year	2009
Description	Author affiliation: School of Information and Mechatronics (SIM), Gwangju Institute of Science and Technology (GIST), 1 Oryong-dong, Buk-gu, Gwangju, 500-712, Republic of Korea (Ahn, J.; Heung-No Lee; Kiseon Kim)
Abstract	A near-maximum-likelihood (ML) detection algorithm for spatially multiplexed multiple-input multiple-output (MIMO) systems has been considered. The sphere decoder (SD) is one of the promising techniques to solve the ML problem. However the SD has a loose necessary condition for pruning branches, and it becomes impractical in large dimensional systems. We propose a Schnorr-Euchner SD with statistical pruning (SP-SESD) in order to further reduce complexity with small performance degradation. Squared statistical constraint radius (SCR) and expected partial path metric from unvisited levels are defined from statistics of noises, and two pruning conditions are jointly applied to search tree for detection efficiency. A flexible trade-off between bit error rate (BER) and complexity can be supported by selecting two pruning probabilities in the proposed scheme, and hence one can design various MIMO detectors according to system demands. Simulation results show the proposed SP-SESD requires lower computational complexity than any statistical pruning approaches, although performance degradation is negligible. The proposed algorithm is effective for MIMO systems with any number of antennas.
Starting Page	619
Ending Page	623
File Size	169301
Page Count	5
File Format	PDF
ISBN	9781424435845
DOI	10.1109/ISWCS.2009.5285307
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-07
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Degradation Thyristors Bit error rate Detectors Probability MIMO Decoding Detection algorithms Statistics Noise level
Content Type	Text
Resource Type	Article

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