Joint modulation classification and antenna number detection for MIMO systems

Turan, Merve, Öner, Mengüç and Çirpan, Hakan (2015) Joint modulation classification and antenna number detection for MIMO systems. IEEE Communications Letters, 20 (1). pp. 193-196. ISSN 1089-7798

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Abstract

Noncooperative classification of the modulation type of communication signals finds application in both civilian and military contexts. Existing modulation classification methods for multiple-input multiple-output (MIMO) communication systems commonly require a priori information on the number of transmit antennas employed by the multiantenna transmitter, which, in most of the noncooperative scenarios involving modulation classi- fication, is unknown and needs to be blindly extracted from the received signal. Since the problems of MIMO modulation classification and detection of the number of transmit antennas are highly coupled, we propose a decision theoretic approach for spatial multiplexing MIMO systems that considers these two tasks as a joint multiple hypothesis testing problem. The proposed method exhibits a high performance even in moderate to low SNR regimes while requiring no a priori knowledge of the channel state information and the noise variance.

Item Type: Article
Additional Information: (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication Including telegraphy, telephone, radio, radar, television > TK5103.2 Wireless communication systems
Depositing User: Mustafa Menguc Oner
Date Deposited: 22 Feb 2017 08:50
Last Modified: 22 May 2018 11:20
URI: http://sro.sussex.ac.uk/id/eprint/66843

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