Probability-Distribution-Based Node Pruning for Sphere Decoding

Creators: Cui, Tao; Han, Shuangshuang; Tellambura, Chintha

Abstract

Node pruning strategies based on probability distributions are developed for maximum-likelihood (ML) detection for spatial-multiplexing multiple-input-multiple-output (MIMO) systems. Uniform pruning, geometric pruning, threshold pruning, hybrid pruning, and depth-dependent pruning are thus developed in detail. By considering the symbol error probability in the high signal-to-noise ratio (SNR) region, the desirable diversity order of uniform pruning and the threshold level for threshold pruning are derived. Simulation results show that threshold pruning saves complexity compared with popular sphere decoder (SD) algorithms, such as the K-best SD, the fixed-complexity SD (FSD), and the probabilistic tree pruning SD (PTP-SD), particularly for high SNRs and large-antenna MIMO systems. Furthermore, our proposed node pruning strategies may also be applied to other systems, including coded MIMO systems and relay networks.

Additional Information

© 2012 IEEE. Manuscript received October 25, 2011; revised November 16, 2012; accepted December 2, 2012. Date of publication December 10, 2012; date of current version May 8, 2013. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada and in part by the China Scholarship Council. This paper was presented in part at the IEEE International Conference on Communications 2007, Scottish Exhibition & Conference Centre, Glasgow, U.K., June 2007. The review of this paper was coordinated by Prof. J. Chun.

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Probability-Distribution-Based Node Pruning for Sphere Decoding

Abstract

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