Circular Sphere Decoding: A Low Complexity Detection for MIMO Systems with General Two-dimensional Signal Constellations

We propose a low complexity complex valued Sphere Decoding (CV-SD) algorithm, referred to as Circular Sphere Decoding (CSD) which is applicable to multiple-input multiple-output (MIMO) systems with arbitrary two dimensional (2D) constellations. CSD provides a new constraint test. This constraint test is carefully designed so that the element-wise dependency is removed in the metric computation for the test. As a result, the constraint test becomes simple to perform without restriction on its constellation structure. By additionally employing this simple test as a prescreening test, CSD reduces the complexity of the CV-SD search. We show that the complexity reduction is significant while its maximum-likelihood (ML) performance is not compromised. We also provide a powerful tool to estimate the pruning capacity of any particular search tree. Using this tool, we propose the Predict-And-Change strategy which leads to a further complexity reduction in CSD. Extension of the proposed methods to soft output SD is also presented.Comment: Published in IEEE Trans. Vehicular Technolog

Reduced-complexity orthotope sphere decoding for multiple-input multiple-output antenna system

In this paper, we propose a maximum likelihood (ML)-like performance reduced computational complexity sorted orthotope sphere decoding (OSD), and zero forced (ZF) sorted OSD algorithms for the spatial multiplexing (SM) in a multiple-input multiple-output (MIMO) system. In comparison with the original OSD our technique reduces the number of partial Euclidean distance (PED) computations by up to 28%, and 25% for QPSK and 16-QAM 4×4 MIMO systems, respectively. © 2010 IEEE

Predicting the pruning potential in sphere decoding for multiple-input multiple-output detection

In this work, a method for predicting the pruning potential of a sphere constraint (SC) for sphere decoding (SD) is developed. Because the direct prediction of the pruning potential is not easy, the orthotope constraint (OC), an approximation of SC, is used instead of SC. This pruning potential prediction makes it possible to increase pruning at the root of the search tree in SD, considering it is the most desirable location for pruning. © 2011 IEEE