scholarly journals A Survey of VLSI Implementations of Tree Search Algorithms for MIMO Detection

2015 ◽  
Vol 35 (10) ◽  
pp. 3644-3674 ◽  
Author(s):  
Ibrahim A. Bello ◽  
Basel Halak ◽  
Mohammed El-Hajjar ◽  
Mark Zwolinski
2002 ◽  
Vol 30 (5) ◽  
pp. A129-A129
Author(s):  
B.A. van der Molen ◽  
D. Bird ◽  
L.G. D'Cruz ◽  
S. Gove ◽  
C. Baboonian ◽  
...  

ICGA Journal ◽  
1996 ◽  
Vol 19 (3) ◽  
pp. 162-174
Author(s):  
M.G. Brockington

2019 ◽  
Author(s):  
Paula Breitling ◽  
Alexandros Stamatakis ◽  
Olga Chernomor ◽  
Ben Bettisworth ◽  
Lukasz Reszczynski

AbstractTerraces in phylogenetic tree space are, among other things, important for the design of tree space search strategies. While the phenomenon of phylogenetic terraces is already known for unlinked partition models on partitioned phylogenomic data sets, it has not yet been studied if an analogous structure is present under linked and scaled partition models. To this end, we analyze aspects such as the log-likelihood distributions, likelihood-based significance tests, and nearest neighborhood interchanges on the trees residing on a terrace and compare their distributions among unlinked, linked, and scaled partition models. Our study shows that there exists a terrace-like structure under linked and scaled partition models as well. We denote this phenomenon as quasi-terrace. Therefore quasi-terraces should be taken into account in the design of tree search algorithms as well as when reporting results on ‘the’ final tree topology in empirical phylogenetic studies.


Author(s):  
Erwan Lecarpentier ◽  
Guillaume Infantes ◽  
Charles Lesire ◽  
Emmanuel Rachelson

In the context of tree-search stochastic planning algorithms where a generative model is available, we consider on-line planning algorithms building trees in order to recommend an action. We investigate the question of avoiding re-planning in subsequent decision steps by directly using sub-trees as action recommender. Firstly, we propose a method for open loop control via a new algorithm taking the decision of re-planning or not at each time step based on an analysis of the statistics of the sub-tree. Secondly, we show that the probability of selecting a suboptimal action at any depth of the tree can be upper bounded and converges towards zero. Moreover, this upper bound decays in a logarithmic way between subsequent depths. This leads to a distinction between node-wise optimality and state-wise optimality. Finally, we empirically demonstrate that our method achieves a compromise between loss of performance and computational gain.


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