DocumentCode :
2431085
Title :
A low-complexity implementation of sampling-based MIMO detection
Author :
Ding, Rui ; Gao, Xiqi ; You, Xiaohu
Author_Institution :
IEEE Nat. Mobile Commun. Res. Lab., Southeast Univ., Nanjing
fYear :
2008
fDate :
7-11 June 2008
Firstpage :
705
Lastpage :
710
Abstract :
A low-complexity implementation of sequential Monte Carlo (SMC) sampling-based detector is developed for multiple-input multiple-output (MIMO) communication systems. Unlike previous reports about SMC sampling that widely sequentially draw samples and process each sample independently, we present a novel sampling method which collaboratively processes all samples and extracts information from a collection of samples to establish the sampling space to draw next samples. Simultaneously, the proposed method adopts a reselection step to save storage resource and decrease computation burden. Simulations indicate that the proposed solution decreases the necessary amount of samples and improves the system performance compared with the classical SMC detector. The revised detector is also compared with sphere decoding (SD) that has the comparable computation burden, and simulation result shows that it can obtain the same performance as SD with lower complexity.
Keywords :
MIMO communication; Monte Carlo methods; signal detection; signal sampling; MIMO detection; SMC sampling-based detector; information extraction; multiple-input multiple-output communication; resource storage saving; sequential Monte Carlo method; Collaboration; Computational modeling; Data mining; Decoding; Detectors; MIMO; Monte Carlo methods; Sampling methods; Sliding mode control; System performance; Multiple-input multiple-output (MIMO); Sequential Monte Carlo (SMC); sampling-based detection; sphere decoding (SD);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks and Signal Processing, 2008 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-2310-1
Electronic_ISBN :
978-1-4244-2311-8
Type :
conf
DOI :
10.1109/ICNNSP.2008.4590442
Filename :
4590442
Link To Document :
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