Lattice basis reduction: improved practical algorithms and solving subset sum problems
Mathematical Programming: Series A and B
A universal lattice code decoder for fading channels
IEEE Transactions on Information Theory
Closest point search in lattices
IEEE Transactions on Information Theory
Algorithm and implementation of the K-best sphere decoding for MIMO detection
IEEE Journal on Selected Areas in Communications
Soft-output sphere decoding: algorithms and VLSI implementation
IEEE Journal on Selected Areas in Communications
Search sequence determination for tree search based detection algorithms
SARNOFF'09 Proceedings of the 32nd international conference on Sarnoff symposium
VLSI Architecture for MIMO Soft-Input Soft-Output Sphere Detection
Journal of Signal Processing Systems
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Depth-first tree search algorithms provide a promising approach to solve the detection problems in MIMO systems. Realizations like the List Sphere Detector (LSD) or the Single Tree Search (STS) enable near max-log detection at reduced but still high complexity. In this paper we show how the complexity of List Sphere Detection can be significantly reduced by MMSE preprocessing in combination with a novel unbiased and separated candidate handling. Therefor, we propose an extension of the LSD by search tuples. Without any performance loss, the resulting Tuple Search (TS) algorithm enables major reduction of sphere sizes and enables moreover a detection with flexible performance respectively complexity. Avoiding loss of useful status information, caused by unbiased MMSE preprocessing or small candidate storage, is provided by a novel matched candidate determination, leading also to reduced hardware complexity. The combination of these methods enable high-performance soft-out detection at very low complexity. More specifically, this enables a performance improvement up to 1 dB at half the complexity of common LSD or STS algorithms.