Title :
MIMO Detection With High-Level Modulations Using Power Equality Constraints
Author :
Nevat, Ido ; Yang, Tao ; Avnit, Karin ; Yuan, Jinhong
Author_Institution :
Univ. of New South Wales, Sydney, NSW, Australia
Abstract :
This paper proposes computationally efficient algorithms for the detection of symbols of high-level modulation constellations in multiple-input-multiple-output (MIMO) systems. The proposed approach is based on replacing the maximum-likelihood constraint with a finite set of power equality constraints (PECs), resulting in a set of nonconvex optimization problems, which can efficiently be solved using the hidden convexity methodology. Next, using specific structural properties of the proposed approach, we develop an ordered PEC algorithm, which provides a significant computational complexity reduction with no performance degradation. Based on that, an improved OPEC algorithm, which incorporates a heuristic local search, is proposed. Numerical results show that the proposed detectors significantly outperform the conventional minimum-mean-square-error (MMSE) detector in terms of bit error rate (BER) performance, with only a slightly higher complexity.
Keywords :
MIMO communication; computational complexity; error statistics; least mean squares methods; optimisation; BER; MIMO detection; MMSE detector; OPEC algorithm; bit error rate; computational complexity reduction; heuristic local search; hidden convexity; high-level modulation constellations; maximum-likelihood constraint; minimum mean square error detector; multiple input multiple output systems; nonconvex optimization; power equality constraints; symbol detection; Australia; Bit error rate; Computational complexity; Constraint optimization; Degradation; Detectors; Least squares methods; MIMO; Power engineering computing; Telecommunication computing; Constrained least squares; maximum-likelihood (ML) detection; multiple-input–multiple-output (MIMO) detection;
Journal_Title :
Vehicular Technology, IEEE Transactions on
DOI :
10.1109/TVT.2010.2047957