Complexity Reduction in Iterative Soft-In Soft-Out Sphere Detection

M.A. Shah; Björn Mennenga; Janis Werner; Gerhard Fettweis

doi:10.1109/VETECS.2011.5956695

Complexity Reduction in Iterative Soft-In Soft-Out Sphere Detection

M.A. Shah, Björn Mennenga, Janis Werner, Gerhard Fettweis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

7 Citations (Scopus)

Abstract

Soft-In Soft-Out (SISO) MIMO detection algorithms providing soft information to subsequent channel decoder are computationally high complex. Realizations based on depth-first search e.g. the Tuple Search (TS) algorithm enables near full MaxLogAPP optimal detection at much reduced but still high complexity. This paper presents a novel method for the complexity reduction of SISO MIMO detection algorithms. This method is based on the pruning of tree nodes and the corresponding subtrees. The pruning is decided based on the absolute value of a priori information of bits greater than or equal to a threshold value. Simulation results for the TS algorithm show that up to 25% reduction in complexity can be achieved without any BER performance degradation.

Original language	English
Title of host publication	IEEE Vehicular Technology Conference (VTC Spring)
Number of pages	6
DOIs	https://doi.org/10.1109/VETECS.2011.5956695
Publication status	Published - May 2011
Externally published	Yes
Publication type	A4 Article in a conference publication

Access to Document

10.1109/VETECS.2011.5956695

Cite this

@inproceedings{7dd9f95fbbd14a7b88e328b2fe2a1388,

title = "Complexity Reduction in Iterative Soft-In Soft-Out Sphere Detection",

abstract = "Soft-In Soft-Out (SISO) MIMO detection algorithms providing soft information to subsequent channel decoder are computationally high complex. Realizations based on depth-first search e.g. the Tuple Search (TS) algorithm enables near full MaxLogAPP optimal detection at much reduced but still high complexity. This paper presents a novel method for the complexity reduction of SISO MIMO detection algorithms. This method is based on the pruning of tree nodes and the corresponding subtrees. The pruning is decided based on the absolute value of a priori information of bits greater than or equal to a threshold value. Simulation results for the TS algorithm show that up to 25% reduction in complexity can be achieved without any BER performance degradation.",

author = "M.A. Shah and Bj{\"o}rn Mennenga and Janis Werner and Gerhard Fettweis",

year = "2011",

month = may,

doi = "10.1109/VETECS.2011.5956695",

language = "English",

booktitle = "IEEE Vehicular Technology Conference (VTC Spring)",

}

TY - GEN

T1 - Complexity Reduction in Iterative Soft-In Soft-Out Sphere Detection

AU - Shah, M.A.

AU - Mennenga, Björn

AU - Werner, Janis

AU - Fettweis, Gerhard

PY - 2011/5

Y1 - 2011/5

N2 - Soft-In Soft-Out (SISO) MIMO detection algorithms providing soft information to subsequent channel decoder are computationally high complex. Realizations based on depth-first search e.g. the Tuple Search (TS) algorithm enables near full MaxLogAPP optimal detection at much reduced but still high complexity. This paper presents a novel method for the complexity reduction of SISO MIMO detection algorithms. This method is based on the pruning of tree nodes and the corresponding subtrees. The pruning is decided based on the absolute value of a priori information of bits greater than or equal to a threshold value. Simulation results for the TS algorithm show that up to 25% reduction in complexity can be achieved without any BER performance degradation.

AB - Soft-In Soft-Out (SISO) MIMO detection algorithms providing soft information to subsequent channel decoder are computationally high complex. Realizations based on depth-first search e.g. the Tuple Search (TS) algorithm enables near full MaxLogAPP optimal detection at much reduced but still high complexity. This paper presents a novel method for the complexity reduction of SISO MIMO detection algorithms. This method is based on the pruning of tree nodes and the corresponding subtrees. The pruning is decided based on the absolute value of a priori information of bits greater than or equal to a threshold value. Simulation results for the TS algorithm show that up to 25% reduction in complexity can be achieved without any BER performance degradation.

U2 - 10.1109/VETECS.2011.5956695

DO - 10.1109/VETECS.2011.5956695

M3 - Conference contribution

BT - IEEE Vehicular Technology Conference (VTC Spring)

ER -

Complexity Reduction in Iterative Soft-In Soft-Out Sphere Detection

Abstract

Access to Document

Fingerprint

Cite this