Circular Sphere Decoding: A Low Complexity Detection for MIMO Systems with General Two-dimensional Signal Constellations
Document Type
Article
Publication Date
3-1-2017
Abstract
© 2017 IEEE. We propose a low-complexity, complex-valued sphere decoding (CV-SD) algorithm, which is referred to as circular sphere decoding (CSD) and is applicable to multiple-input-multiple-output (MIMO) systems with arbitrary 2-D constellations. CSD provides a new constraint test. This constraint test is carefully designed so that the elementwise dependence 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 sphere decoding (SD) is also presented.
Publication Title
IEEE Transactions on Vehicular Technology
Recommended Citation
Jang, H.,
Nooshabadi, S.,
Kim, K.,
&
Lee, H.
(2017).
Circular Sphere Decoding: A Low Complexity Detection for MIMO Systems with General Two-dimensional Signal Constellations.
IEEE Transactions on Vehicular Technology,
66(3), 2085-2098.
http://doi.org/10.1109/TVT.2016.2570942
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/11165
