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Near Maximum-Likelihood (ML) detections based on the tree search can approach the optimal performance with reduced complexity in Multiple-Input Multiple-Output (MIMO) systems. The breadth-first scheme is widely applied in practical systems for its stable and upper-bounded throughput. However, the major drawback of breadth-first detection is still the relatively high computational complexity. In this paper, the authors propose a Variable Breadth based Adaptive tree search (VBA) scheme to further reduce the complexity. In particular, they introduce a variable metric constraint to dynamically regulate the searching breadth, which is determined by the accumulated metric of the partial Zero-Forcing (ZF) sequence at each layer of the searching tree during the adaptive candidate selection process.
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