title: MIMO transmit scheme based on morphological perceptron with competitive learning.
abstract: This paper proposes a new multi-input multi-output (MIMO) transmit scheme aided by artificial neural network (ANN). The morphological perceptron with competitive learning (MP/CL) concept is deployed as a decision rule in the MIMO detection stage. The proposed MIMO transmission scheme is able to achieve double spectral efficiency; hence, in each time-slot the receiver decodes two symbols at a time instead one as Alamouti scheme. Other advantage of the proposed transmit scheme with MP/CL-aided detector is its polynomial complexity according to modulation order, while it becomes linear when the data stream length is greater than modulation order. The performance of the proposed scheme is compared to the traditional MIMO schemes, namely Alamouti scheme and maximum-likelihood MIMO (ML-MIMO) detector. Also, the proposed scheme is evaluated in a scenario with variable channel information along the frame. Numerical results have shown that the diversity gain under space-time coding Alamouti scheme is partially lost, which slightly reduces the bit-error rate (BER) performance of the proposed MP/CL-NN MIMO scheme.
authors.affiliation: Department of Electrical Engineering, State University of Londrina (DEEL-UEL), Rod. Celso Garcia Cid - PR445, s/n, Campus Universitário, P.O. Box 10.011, 86057-970, Londrina, PR, Brazil. Electronic address: raulavalente@gmail.com.
authors.forename: Raul Ambrozio
authors.initials: RA
authors.lastname: Valente
authors.affiliation: Department of Electrical Engineering, State University of Londrina (DEEL-UEL), Rod. Celso Garcia Cid - PR445, s/n, Campus Universitário, P.O. Box 10.011, 86057-970, Londrina, PR, Brazil. Electronic address: taufik@uel.br.
authors.forename: Taufik
authors.initials: T
authors.lastname: Abrão
mesh_terms: D000465:Algorithms; D007858:Learning; D016571:Neural Networks, Computer
keywords: Alamouti scheme; Channel state information; MIMO transmit; Morphological perceptron with competitive learning; Multi-layer perceptron; Neural networks