MÃtodos Tensoriais para EstimaÃÃo de Canal em Sistemas MIMO-STBC

• Autor(a) principal: Gilderlan Tavares de AraÃjo
• Data de Publicação: 2014
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFC
• Texto Completo: http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=11600
• topic: EstimaÃÃo de Canal; DecomposiÃÃes tensoriais; MIMO System; Channel Estimation; Space-Time Coding; Tensor Decomposition; TELECOMUNICACOES

In this work, the performance of MIMO systems based on space-time coding is investigated through multilinear algebra, more specifically, by means of tensor decompositions, pulling away a bit from commonly used matrix models. We assume a system composed of P transmit and M receive antennas, consisting of a combination of a space-time block code (STBC) with a formatting filter. This filter is formed by a precoding matrix and a matrix that maps the precoded signal onto the transmit antennas. For the considered system, two contributions are presented to solve the problem of channel estimation. First, we propose a tensor-based channel estimation method for orthogonal STBCs in MIMO systems, by focusing on the specific case of the Alamouti scheme. We resort to a third order PARATUCK2 tensor model for the received signal, the third dimension of which is related to the presence of the formatting filter. By capitalizing on this tensor model, a channel estimation method based on the alternating least squares (ALS) algorithm is proposed. As a second contribution, a generalization of this method to an arbitrary nonorthogonal STBC is made, where a generalized structure is proposed for the formatting filter, introducing a fourth dimension into the tensor signal model. In this case, we make use of the PARATUCK(2-4) model followed by its reduction to a structured PARAFAC model, from which a closed-form solution to the channel estimation problem is established. The performance metrics considered for evaluating the proposed channel estimation method are: (I) the quality of the estimation in terms of NMSE and (II) the system reliability in terms of Bit Error Rate.