Performance, efficiency and complexity in multiple access large-scale MIMO Systems.

• Autor(a) principal: Mussi, Alex Miyamoto
• Data de Publicação: 2019
• Tipo de documento: Tese
• Idioma: eng
• Título da fonte: Biblioteca Digital de Teses e Dissertações da USP
• Texto Completo: http://www.teses.usp.br/teses/disponiveis/3/3142/tde-26082019-131340/
• topic: Compromisso desempenho-complexidade; Detector por amostragem mistas de gibbs; Detector por troca de mensagens; Detectores de baixa complexidade; Lattice reduction; message passing detector; Low complexity detectors; LS-MIMO; Mixed gibbs sampling detector; Performance-complexity tradeoff; Processamento de sinais; Redução treliça; Sistemas de comunicação; Sistemas de múltiplas antenas em larga-escala

Systems with multiple transmitting and receiving antennas in large-scale (LS-MIMO - large-scale multipleinput multiple-output) enable high spectral and energy efficiency gains, which results in an increase in the data transmission rate in the same band, without increasing the transmitted power per user. In addition, with the increase of the number of antennas in the base station (BS) it is possible to attend to a larger number of users per cell, in the same occupied band. Furthermore, it has been found in the literature that the reported advantages of LS-MIMO systems can be obtained with a large number of antennas on at least one side of the communication, usually in BS due to physical restriction in user equipments. However, such advantages have their cost: the use of a large number of antennas also difficult tasks involving signal processing, such as estimation of channel coefficients, precoding and signal detection. It is at this juncture that this Doctoral Thesis is developed, in which the computational complexity of performing efficient detection methods in LSMIMO communication systems is explored through the analysis of algorithms and optimization techniques in the solution of specific problems and still open. More precisely, this Thesis discusses and proposes promising detection techniques in LS-MIMO systems, aiming to improve performance metrics - in terms of error rate - and computational complexity - in terms of the number of mathematical operations. Initially, the problem is introduced through a conventional MIMO system model, where channels with imperfect estimates and correlation between transmitter (Tx) and receiver (Rx) antennas are considered. Preprocessing techniques based on lattice reduction (LR) are applied in linear detectors, in addition to the sphere decoder (SD), which proposes a lookup table procedure in order to provide a reduction in computational complexity. It is shown that the LR method in the pre-detection results in a significant performance gain in both the condition of uncorrelated and correlated channels, and in the latter scenario the improvement is even more remarkable due to the diversity gain provided. On the other hand, the complexity involved in the application of LR in high correlation scenarios becomes preponderant in linear detectors. In the LR-SD using the lookup table procedure, the optimum gain was reached in all scenarios, as expected, and resulted in a lower complexity than maximum likelihood (ML) detector, even with maximum correlation between antennas, which represents the most complex scenario for the LR technique. Next, the message passing (MP) detector is investigated, which makes use of Markov random fields (MRF) and factor graph (FG) graphical models. Moreover, it is shown in the literature that the message damping (MD) method applied to the MRF detector brings relevant performance gain without increasing computational complexity. On the other hand, the DF value is specified for only a restricted range of scenarios. Numerical results are extensively generated, in order to obtain a range of analysis of the MRF with MD, which resulted in the proposition of an optimal value for the DF, based on numerical curve fitting. Finally, in the face of the MGS detector, two approaches are proposed to reduce the negative impact caused by the random solution when high modulation orders are employed. The first is based on an average between multiple samples, called aMGS (averaged MGS). The second approach deploys a direct restriction on the range of the random solution, limiting in d the neighborhood of symbols that can be sorted, being called d-sMGS. Numerical simulation results show that both approaches result in gain of convergence in relation to MGS, especially: in regions of high system loading, d-sMGS detection demonstrated significant gain in both performance and complexity compared to aMGS and MGS; although in low-medium loading, the aMGS strategy showed less complexity, with performance marginally similar to the others. Furthermore, it is concluded that increasing the dimensions of the system favors a smaller restriction in the neighborhood.