An efficient likelihood-based modulation classification algorithm for multiple-input multiple-output systems

Bahloul, M.R. and Yusoff, M.Z. and Abdel-Aty, A.-H. and Saad, M.N.M. (2016) An efficient likelihood-based modulation classification algorithm for multiple-input multiple-output systems. Journal of Computational and Theoretical Nanoscience, 13 (11). pp. 7879-7885.

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Blind algorithms for multiple-inputmultiple-output (MIMO) signals interception have recently received considerable attention because of their important applications in modern civil and military communication fields. One key step in the interception process is to blindly recognize the modulation type of the MIMO signals. This can be performed by employing a Modulation Classification (MC) algorithm, which can be feature-based or likelihood-based. To overcome the problems associated with the existing likelihood-based MC algorithms, a new algorithm is developed in this paper. We formulated the MC problem as maximizing a global likelihood function formed by combining the likelihood functions for the estimated transmitted signals, where Minimum Mean Square Error (MMSE) filtering is employed to separate the MIMO channel into several sub-channels. Simulation results showed that the proposed algorithm works well under various operating conditions, and performs close to the performance upper bound with reasonable complexity. © 2016 American Scientific Publishers All rights reserved.

Item Type:Article
Impact Factor:cited By 7
Uncontrolled Keywords:Blind equalization; Channel estimation; Codes (symbols); Communication channels (information theory); Feedback control; Mean square error; Military applications; Military communications; MIMO systems; Pattern recognition; Telecommunication repeaters, Blind channel estimation; Likelihood functions; Minimum mean square errors; Minimum mean square errors (MMSE); Modulation classification; Multiple input multiple output system; Operating condition; Transmitted signal, Modulation
ID Code:25308
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:27 Aug 2021 12:57
Last Modified:27 Aug 2021 12:57

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