Nesta dissertaÃÃo sÃo propostos mÃtodos baseados em processamento tensorial de sinais para a estimaÃÃo de parÃmetros em arranjos de antenas vetoriais elÃtricas (Tripolo), considerando diferentes estruturas de arranjos (ULA, L-shape e UPA). Inicialmente, à utilizado um arranjo em L-shape,resultando em um modelo tensorial de terceira ordem (3-D) que, junto ao algoritmo de estimaÃÃo T-ALS (do inglÃs, Trilinear Alternating Least Squares), possibilita a identificaÃÃo cega de pelo menos o dobro de fontes estimadas pelos modelos tradicionais. Em seguida, sob transmissÃo supervisionada, à proposto um mÃtodo alternativo, utilizando a decomposiÃÃo SVD, o qual à comparado ao mÃtodo tensorial com uso do algoritmo T-ALS. Uma segunda abordagem à proposta utilizando-se uma estrutura de arranjo planar de antenas (UPA), a qual faz uso de um modelo tensorial de quarta ordem (4-D) junto ao algoritmo de estimaÃÃo Q-ALS (do inglÃs, Quadrilinear Alternating Least Squares). Neste caso, um mÃtodo alternativo à proposto usando a fatoraÃÃo do produto de Khatri-Rao e uma anÃlise comparativa destes mÃtodos à realizada. Considerando-se o caso supervisionado, à feito ainda um estudo comparativo dos algoritmos Q-ALS, T-ALS e SVD, e um novo algoritmo, chamado Nested-SVD à proposto. Por fim, foi realizada a modelagem computacional do tripolo elÃtrico com uso de software de simulaÃÃo de alta frequÃncia (HFSS), possibilitando a extraÃÃo do parÃmetro de ganho espacial dos arranjos L-shape e UPA. Em seguida, à feita a avaliaÃÃo do desempenho dos mÃtodos tensoriais propostos usando este parÃmetro em uma situaÃÃo mais realista, e comparado ao desempenho usando modelos idealizados de arranjos de antenas com ganho unitÃrio e omnidirecional. O desempenho dos mÃtodos propostos à avaliado atravÃs de simulaÃÃes de Monte Carlo em diferentes cenÃrios e configuraÃÃes de arranjo / In this dissertation, we propose methods based on tensor signal processing for the parameter estimation in electric vector (Tripole) antenna arrays, considering different structures of arrays (ULA, L-shape and UPA). Initially, using a L-shape array, we develop a third order (3-D) tensor model for the received data. Based on this model, a trilinear alternating least squares (T-ALS) algorithm is used for the blind estimation of the sourceâs parameters. Then, under supervised transmission an alternative method is proposed by resorting to the SVD decomposition, which is compared to the T-ALS algorithm. A second approach is proposed, which is based on a uniform planar array antenna (UPA). In this case a fourth-order (4-D) tensor model is obtained, and the Q-ALS (Quadrilinear Alternating Least Squares) algorithm is used for parameter estimation. An alternative method is also proposed, which exploits the factorization of the Khatri-Rao product. Considering the supervised case, a new algorithm called Nested-SVD is proposed and a comparative study with Q-ALS, T-ALS and SVD algorithms is carried out. The performance of the proposed methods is evaluated through Monte Carlo simulations in different scenarios and array settings. Finally, computational modeling of electric tripole using the high frequency simulation software (HFSS) was performed, enabling the extraction of the L-shape and UPA spatial array gain. Then, the performance of the proposed tensor methods is evaluated in a more realistic scenario, and compared to idealized omnidirectional and unitary gain antenna array models

The data traffic has been growing drastic over the past few years due to digitization and new technologies that are introduced to the market, such as autonomous cars. In order to meet this demand, the MIMO-OFDM system is used in the fifth generation wireless network, 5G. Designing the optimal wireless network is currently the main research within the area of telecommunication. In order to achieve such a system, multiple factors has to be taken into account, such as the suppression of interference from other users. A traditional method called linear minimum mean square error filter is currently used to suppress the interferences. To derive such a filter, a selection of parameters has to be estimated. One of these parameters is the ideal interference plus noise covariance matrix. By gathering prior knowledge of the underlying structure of the interference channel matrices in terms of the number of interferers and their corresponding bandwidths, the estimation of the ideal covariance matrix could be facilitated. As for this thesis, machine learning algorithms were used to extract these prior knowledge. More specifically, a two or three hidden layer feedforward neural network and a support vector machine with a linear kernel was used. The empirical findings implies promising results with accuracies above 95% for each model. / Under de senaste åren har dataanvändningen ökat drastiskt på grund av digitaliseringen och allteftersom nya teknologier introduceras på marknaden, exempelvis självkörande bilar. För att bemöta denna efterfrågan används ett s.k. MIMO-OFDM system i den femte generationens trådlösa nätverk, 5G. Att designa det optimala trådlösa nätverket är för närvarande huvudforskningen inom telekommunikation och för att uppnå ett sådant system måste flera faktorer beaktas, bland annat störningar från andra användare. En traditionell metod som används för att dämpa störningarna kallas för linjära minsta medelkvadratfelsfilter. För att hitta ett sådant filter måste flera olika parametrar estimeras, en av dessa är den ideala störning samt bruskovariansmatrisen. Genom att ta reda på den underliggande strukturen i störningsmatriserna i termer av antal störningar samt deras motsvarande bandbredd, är något som underlättar uppskattningen av den ideala kovariansmatrisen. I följande avhandling har olika maskininlärningsalgoritmer applicerats för att extrahera dessa informationer. Mer specifikt, ett neuralt nätverk med två eller tre gömda lager samt stödvektormaskin med en linjär kärna har använts. De slutliga resultaten är lovande med en noggrannhet på minst 95% för respektive modell.