Low-complexity decoding via reduced dimension maximum-likelihood search

In this paper, we consider a low-complexity detection technique referred to as a reduced dimension maximum-likelihood search (RD-MLS). RD-MLS is based on a partitioned search which approximates the maximum-likelihood (ML) estimate of symbols by searching a partitioned symbol vector space rather than that spanned by the whole symbol vector. The inevitable performance loss due to a reduction in the search space is compensated by 1) the use of a list tree search, which is an extension of a single best searching algorithm called sphere decoding, and 2) the recomputation of a set of weak symbols, i.e., those ignored in the reduced dimension search, for each strong symbol candidate found during the list tree search. Through simulations on -quadrature amplitude modulation (QAM) transmission in frequency nonselective multi-input-multioutput (MIMO) channels, we demonstrate that the RD-MLS algorithm shows near constant complexity over a wide range of bit error rate (BER) (10 1 10 4), while limit...

• Type: Journal
• Year: 2010
• Where: TSP
• Authors: Jun Won Choi, Byonghyo Shim, Andrew C. Singer, Nam Ik Cho
• Keywords: Artificial Intelligence, List Tree Search, Reduced Dimension, Symbol Vector, TSP 2010