Title :
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
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;
Journal_Title :
Vehicular Technology, IEEE Transactions on
DOI :
10.1109/TVT.2012.2233226
