eprintid: 7368
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/73/68
datestamp: 2023-11-09 16:19:10
lastmod: 2023-11-09 16:19:10
status_changed: 2023-11-09 16:09:11
type: article
metadata_visibility: show
creators_name: Bahloul, M.R.
creators_name: Yusoff, M.Z.
creators_name: Abdel-Aty, A.-H.
creators_name: Saad, M.N.M.
title: An efficient likelihood-based modulation classification algorithm for multiple-input multiple-output systems
ispublished: pub
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
note: cited By 10
abstract: 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.
date: 2016
publisher: American Scientific Publishers
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015159591&doi=10.1166%2fjctn.2016.5788&partnerID=40&md5=a80259c6c9cba296d147d8e04789ab21
id_number: 10.1166/jctn.2016.5788
full_text_status: none
publication: Journal of Computational and Theoretical Nanoscience
volume: 13
number: 11
pagerange: 7879-7885
refereed: TRUE
issn: 15461955
citation:   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.  ISSN 15461955