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 proceedingConference contributionScientificpeer-review

7 Citations (Scopus)


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 languageEnglish
Title of host publicationIEEE Vehicular Technology Conference (VTC Spring)
Number of pages6
Publication statusPublished - May 2011
Externally publishedYes
Publication typeA4 Article in a conference publication


Dive into the research topics of 'Complexity Reduction in Iterative Soft-In Soft-Out Sphere Detection'. Together they form a unique fingerprint.

Cite this