Probability-Distribution-Based Node Pruning for Sphere Decoding

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. Index Terms-Maximum likelihood (ML), multiple-inputmultiple-output (MIMO), sphere decoder (SD), statistical pruning, wireless communications.Tao Cui (S'04) received the M.Sc. degree from the University of Alberta, Edmonton, AB, Canada, in 2005 and the M.S. and Ph.D. degrees from California Institute of Technology, Pasadena, in 2006 and 2009, respectively. His research interests include the interactions between networking theory, communication theory, and information theory.Shuangshuang Han (S'11) received the B.Eng. degree in communication engineering and the M.Eng. degree in communication and information systems from Shandong University,

A Complexity Efficient DMT-Optimal Tree Pruning Based Sphere Decoding

“…Beyond the DMT optimality, we show that the proposed algorithm only needs to visit a single node at high SNR regime and high transmission rates. This result shows that, unlike the conventional SD algorithms which are shown to have a polynomial complexity behavior at high SNR regime Proposed Method PTP-SD+ISRC [11] Threshold Pruning SD [12] PARC-SD [10] Proposed Method PTP-SD+ISRC [11] Threshold Pruning SD [12] Fig. 4.…”

“…The cardinality of visited nodes for a 2 × 2 Golden coded MIMO system for diverse multiplexing gain • Probabilistic tree pruning SD with inter-search radius control (PTP-SD+ISRC) [11]. • Threshold pruning SD [12]. It can be seen that, although all the methods achieve near ML performance, the proposed pruning method benefits from a lower computational complexity in terms of average number of flops.…”

Enabling Sphere Decoding for SCMA

“…, K ′ − 1}, and d 2 1 = 0 when k ′ = K ′ . Let N v1 and N v2 denote the average number of visited layers [6], [8]- [10] for k ′ ∈ {1, . .…”

Sphere Decoding in Parallel Mode and its Performance

“…Among them, Sphere decoding is a very powerful algorithm to find the optimal solution in many scenarios [4][5][6][7][8][9][10][11][12]. Many works have been done to promote this algorithm [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. To utilize the benefit of parallel calculation in signal process is a good method to promote the performance [30][31].…”