On further reduction of complexity in tree pruning based sphere search

Shim, Byonghyo; Kang, Inhye

doi:10.1109/tcomm.2010.02.080340

Cited by 34 publications

(32 citation statements)

References 17 publications

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“…Using (11), it can be readily verified that this choice of δ i achieves a diversity order of K 0 . Interestingly, the cost threshold δ only depends on the SNR and K 0 but not on i.…”

Section: A Performance Analysismentioning

confidence: 94%

“…We simply set δ i = 0 for i ≤ n − K 0 + 1. In this case, the upper bound on P f is also given by (11).…”

Section: A Performance Analysismentioning

confidence: 99%

“…To extend the IRA, Shim and Kang [10] propose probabilistic tree pruning sphere decoding (PTP-SD), which prunes more nodes by adding a probabilistic noise constraint on top of the sphere constraint. Then, Shim and Kang [11] extend the PTP-SD and provide further improvement of the computational complexity with minimal extra cost and negligible performance penalty. Additional pruning methods are also proposed in [12] and [13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Probability-Distribution-Based Node Pruning for Sphere Decoding

Cui

Han

Tellambura

2013

IEEE Trans. Veh. Technol.

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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,

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“…Using (11), it can be readily verified that this choice of δ i achieves a diversity order of K 0 . Interestingly, the cost threshold δ only depends on the SNR and K 0 but not on i.…”

Section: A Performance Analysismentioning

confidence: 94%

“…We simply set δ i = 0 for i ≤ n − K 0 + 1. In this case, the upper bound on P f is also given by (11).…”

Section: A Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probability-Distribution-Based Node Pruning for Sphere Decoding

Cui

Han

Tellambura

2013

IEEE Trans. Veh. Technol.

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“…Even though the ML solution can be alternatively computed using sphere decoder (SD) algorithms [20,21] that are very efficient for systems with a small number of antennas, the cost of SD algorithms depends on the noise variance, the number of data streams to be detected and the signal constellation, resulting in high computational costs for low signal-to-noise ratios (SNR), high-order constellations and a large number of users. The ML detector has a cost that is exponential in the number of users K and the modulation order, and which is too complex to be implemented in gateways associated to a large number of simultaneous users.…”

Section: Overview Of Multibeam Techniquesmentioning

confidence: 99%

Multibeam joint detection

Arnau

Lamare

Mosquera

2015

Cooperative and Cognitive Satellite Systems

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“…5) Compared to the original SD, the proposed scheme requires an additional computational effort in calculating the functions F and F −1 whenever an update of the radii is needed. The cost can be minimized by implementing a table lookup for an online operation of the algorithm, as suggested in [8].…”

Section: Features Of the Proposed Schemementioning

confidence: 99%

Reduced-complexity sphere decoding with dimension-dependent sphere radius design

Chang

Chung

2011

2011 IEEE Wireless Communications and Networking Conference

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Abstract-A modified sphere decoding (SD) scheme is proposed for multiple-input multiple-output (MIMO) communication systems in this paper. The conventional SD goes from the lower dimension to the higher dimension to examine whether a lattice point lies inside the sphere of some radius, which remains fixed for all dimensions. Since the sphere radius directly affects the search range and thus the complexity, it is an important parameter to design. The proposed scheme employs a set of dimension-dependent sphere radii, which performs more aggressive search-space reduction in low dimensions. The proposed method is shown by computer simulation to offer substantial complexity benefits with little symbol error rate (SER) performance loss (0.5 dB), compared to the optimal maximum likelihood (ML) decoding. The contribution of this paper includes the complexity advantage yielded by the proposed scheme as well as the introduction of a systematic approach to sphere radius design and control.

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On further reduction of complexity in tree pruning based sphere search

Cited by 34 publications

References 17 publications

Probability-Distribution-Based Node Pruning for Sphere Decoding

Probability-Distribution-Based Node Pruning for Sphere Decoding

Multibeam joint detection

Reduced-complexity sphere decoding with dimension-dependent sphere radius design

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