Massive MIMO systems have received a growing interest since the apogee of 5G and the perspective of 6G. Massive MIMO allows an efficient usage of spectral resources and provides higher data rates. When the number of antennas increases, the energy consumption and the hardware cost in the base station also increase. The high energy consumption and processing may be addressed through the selection of antennas. Among various possible structures in the literature, the present work proposes the combination of full-array architecture to make a virtual sectorization and antenna selection using matching pursuit. The algorithm was tested in different channels with complexity reduction in our tests while maintaining bit-error rate in most scenarios.

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In Brazil, there are around 10 million hard of hearing and deaf people. However, the majority of Brazilians are not fluent in the Brazilian Sign Language (Libras). Many members of the hearing impaired and deaf community have communication issues in everyday life situations. Technological solutions can aid in mitigating this problem. This work proposes a semi-supervised method to identify and classify signals in Libras from Youtube videos. The routine starts by segmenting the videos through a measure of movement intensity. We catalog the video segments according to the corresponding subtitles, which we extract by employing Optical Character Recognition (OCR) if embedded. It composes an ad-hoc dataset that we use to train a Libras recognition system. A Convolutional Neural Network (CNN) performs feature extraction frame-by-frame, and a Recurrent Neural Network (RNN) models the time correlation between the features, thus classifying the signal. The proposed method can achieve accuracy up to 61.6% in the ad-hoc dataset used in this work.

The Viterbi algorithm is the maximum likelihood algorithm that is used for the decoding of convolutional codes. In order to determine the survivor path in a trellis, it is necessary to calculate the metrics of each branch. In this paper, we propose a method that reduces the number of branches in the trellis and consequently its complexity, based on the reliability of the received signal samples. The complexity of the proposed algorithm is reduced with the number of reliable samples. The proposed algorithm achieves a performance close to the Viterbi algorithm, but with less complexity, depending on the reliability threshold. The performance of the proposed algorithm is evaluated in terms of bit error probability and complexity, obtained by simulation, for different signal to noise ratio and reliability threshold values. The results are obtained for different convolutional encoders by considering a Rayleigh fading channel.