Desarrollo de un marco de referencia para sistemas mimo masivo que permita la optimización de la eficiencia espectral mediante la selección de antenas, para la tecnología 5g.

MIMO Massive is an emerging technology in wireless communication that increases Spectral Efficiency (SE) to a large extent compared to MIMO systems. MIMO Massive considers a base station equipped with a large number of antennas (for example, from tens to hundreds) and that serves many users of a single antenna on the same frequency and time resource. However, these base stations require multiple radiofrequency (RF) chains, which consist of an amplifier, mixer, converters, filter, etc. Therefore, due to the multiple RF chains, the cost and complexity of the system hardware increases. To reduce costs and hardware complexity, antenna selection techniques are used that minimize complexity with almost the same capacity. This work analyzes the optimization of the Spectral Efficiency through antenna selection schemes such as Precoding, Beamforming and Antenna Selection Algorithms and the effect on the performance of the MIMO Massive systems for the future 5G technology (Fifth Generation). With the help of the bibliographic review and analyzing mathematically the different techniques of Antenna Selection, elements of each scheme that allow a variety in the study of optimization were chosen. On the part of the Pre-codings, the minimum squared error estimator (MMSE), Zero-forcing (ZF) and Matched Filter (MF) were chosen. Also in Beamforming a Hybrid scheme was studied according to the Kalman, MMSE and ZF Precodes. Upon arriving at the Antenna Selection Algorithms, we proceeded to choose the Antenna Selection Algorithms: Random, Rapid and Based on Quantification. MATLAB facilitated the task of analyzing Spectral Efficiency, by developing programs that allowed the visualization of the operation of each of the aforementioned techniques based on determining parameters. Once the results are obtained, it is possible to define what scheme and in which scenario they can optimize the Spectral Efficiency. When examining the precoding, MMSE and ZF achieve a high performance, but due to the degree of computational complexity of the first one, ZF has a viable option. In Beamforming, Kalman due to its mathematical structure surpasses MMSE and ZF, compensating its high computational processing with its high rates of Spectral Efficiency. While for the Antenna Selection Algorithms, the Quantification Based Algorithm provides better optimization options, which by means of the inclusion of the quantization error achieves superior results than the Random and Rapid Selection Algorithm.

• Hovedforfatter: Tene Uyaguari, Jaime Missael (author)
• Format: bachelorThesis
• Sprog: spa
• Udgivet: 2019
• Fag: <TECNOLOGÍA 5G><EFICIENCIA ESPECTRAL><MIMO MASIVO><PRECODIFICACIONES><SELECCIÓN DE ANTENAS>
• Online adgang: http://dspace.unl.edu.ec/jspui/handle/123456789/22159