Abstract
Generally, it is easier to compute the derivation and maximization of the full-likelihood expectation than the calculations of incompletely data maximizing likelihood function. In some cases, even if it is easy to find the full-likelihood expectation, it is difficult to achieve the maximization of the full-likelihood expectation. So a novel approach for multi-user detection based on the ECM iterative algorithm is proposed. Compared with the EM algorithm, the ECM algorithm reduces the computational complexity of the M-step. The results show that the proposed algorithm has well performance and Convergence in Gaussian noise.
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© 2012 Springer-Verlag GmbH Berlin Heidelberg
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Xian, J., Liu, Z. (2012). Multi-user Detection Based on the ECM Iterative Algorithm in Gaussian Noise. In: Jin, D., Lin, S. (eds) Advances in Computer Science and Information Engineering. Advances in Intelligent and Soft Computing, vol 168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30126-1_15
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DOI: https://doi.org/10.1007/978-3-642-30126-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30125-4
Online ISBN: 978-3-642-30126-1
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