The Third Generation Partnership Project (3GPP) and the International Telecommunication Union (ITU) identified the technical requirements that the fifth generation of mobile communications networks (5G) had to meet; within these parameters are the following: an improved data rate and a greater number of users connected simultaneously. 5G uses non-orthogonal multiple access (NOMA) to increase the number of simultaneously connected users, and by encoding data it is possible to increase the spectral efficiency (SE). In this work, eight codewords are used to transmit three bits simultaneously using Sparse Code Multiple Access (SCMA), and through singular value decomposition (SVD) the Euclidean distance between constellation points is optimized. On the other hand, applications of machine intelligence and machine intelligence in 5G and beyond communication systems are still developing; in this sense, in this work we propose to use machine learning for detecting and decoding the SCMA codewords using neural networks. This paper focuses on the Use Case of enhanced mobile broadband (eMBB), where higher data rates are required, with a large number of users connected and low mobility. The simulation results show that it is possible to transmit three bits simultaneously with a low bit error rate (BER) using SVD-SCMA in the uplink channel. Our simulation results were compared against recent methods that use spatial modulation (SM) and antenna arrays in order to increase spectral efficiency. In adverse Signal-to-Noise Ratio (SNR), our proposal performs better than SM, and antenna arrays are not needed for transmission or reception.

This work puts together two technologies that are in the interest of the scientific community, on the one hand, access methods for fifth generation systems of mobile communications, in this case Sparse Code Multiple Access (SCMA), and on the other hand supervised learning based on neural networks. SCMA is one of the proposed access techniques for fifth generation mobile communication systems. Until now, the detection algorithm in the receiver is based on Message Passing Algorithm (MPA) or minimum Euclidean distance. In this work, a new approach is proposed, which is based on supervised learning using neural networks to decode SCMA codewords. The simulation results show that the receiver based on neural networks learns quickly and obtains 100% accuracy in predictions on channels with high noise. In addition to being simpler in its implementation than its predecessors (MPA and minimum Euclidean distance).

The objective of this work is the development of a portable spectrum analyzer to monitor electromagnetic signals using a Software Defined Radio (SDR) that uses the free software program GNURadio and a dongle with a demodulator and tuner, a telescopic antenna and a Single Board Computer (SBC). The program acts a spectrum analyzer that allows to visualize the radiofrequency spectrum for Modulated Frequency (FM) and Digital Terrestrial Television (DTT) signals for 88-108 MHz and 470-608 MHz bands in México.

This work uses the Moore-Penrose pseudo-inverse (PINV) properties to improve the decoding of signals in the uplink channel of a 5th-Generation mobile communication technology (5G) and Beyond (B5G) communication system. The results of computational simulations show an improvement in the bit error rate (BER) and in the Error Vector Magnitude (EVM) under adverse conditions of the wireless communication channel, which is of great importance to meet the high transmission rate and high reliability requirements of 5G and B5G systems.