Probability-Distribution-Based Node Pruning for Sphere Decoding

Author

Tao Cui ; Shuangshuang Han ; Tellambura, C.

Author_Institution

Dept. of Electr. Eng., California Inst. of Technol. Pasadena, Pasadena, CA, USA

Volume

62

Issue

4

fYear

2013

fDate

41395

Firstpage

1586

Lastpage

1596

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.

Keywords

MIMO communication; maximum likelihood detection; statistical distributions; MIMO systems; depth-dependent pruning; geometric pruning; hybrid pruning; maximum-likelihood detection; probability distribution-based node pruning; signal-to-noise ratio region; spatial-multiplexing multiple-input-multiple-output systems; sphere decoding; symbol error probability; threshold pruning; uniform pruning; Complexity theory; Decoding; MIMO; Probability distribution; Signal to noise ratio; Vectors; Maximum likelihood (ML); multiple-input–multiple-output (MIMO); sphere decoder (SD); statistical pruning; wireless communications;

fLanguage

English

Journal_Title

Vehicular Technology, IEEE Transactions on

Publisher

ieee

ISSN

0018-9545

Type

jour

DOI

10.1109/TVT.2012.2233226

Filename

6377314