Near-ML detection over a reduced dimension hypersphere

Jun Won Choi, Byonghyo Shim, Andrew C. Singer

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we propose a near-maximum likelihood (ML) detection method referred to as reduced dimension ML search (RD-MLS). The RD-MLS detector is based on a partitioned search method that divides the symbol space into two groups and searches over the vector space of one group instead of that comprising all of the symbols. First, a minimum mean square error (MMSE) dimension reduction operator suppressing the interference from the second group is applied, and then a list tree search (LTS) is performed over the symbols in the first group. For each lattice point of symbols for the first group found from the LTS, the rest of symbols are estimated by MMSE-decision feedback (MMSE-DF) estimation. Among these lattice point candidates, a final solution is chosen as a minimizer of the L2-norm criterion. From an asymptotic error probability analysis, we show that the dimension reduction loss is potentially compensated by the LTS gain proportional to the size of the list. Furthermore, we demonstrate through simulation on multi-input multi-output (MIMO) transmissions that the RD-MLS detector achieves substantial complexity reduction with relatively little performance loss over ML detection.

Original languageEnglish
Title of host publicationGLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference
Publication statusPublished - 2009
Event2009 IEEE Global Telecommunications Conference, GLOBECOM 2009 - Honolulu, HI, United States
Duration: 2009 Nov 302009 Dec 4

Publication series

NameGLOBECOM - IEEE Global Telecommunications Conference


Other2009 IEEE Global Telecommunications Conference, GLOBECOM 2009
Country/TerritoryUnited States
CityHonolulu, HI

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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