Abstract
In cellular systems, information signals must be transmitted at high rates and with high reliability. One of the possible solutions to meet such criteria is the use of systems with multiple transmitting and/or receiving antenna arranged in the form of a multiple-input, multiple-output (MIMO) system. However, signal processing techniques in MIMO systems are developed under the assumption of transmission on Gaussian channels, which may lead to the decrease of efficiency in non-Gaussian communication scenarios. In this context, the widespread use of MIMO systems in recent years has motivated the development of new processing techniques that can be employed in scenarios that also consider the presence of non-Gaussian noise in communication channels. This work proposes a novel signal detection technique for MIMO systems, which is called maximum correntropy detector (MCD), being adequate to environments characterized by Gaussian and non-Gaussian noise. The introduced approach is based on complex correntropy function and can be seen as a generalization of the maximum likelihood detector (MLD) concept. The MCD is evaluated on Gaussian and non-Gaussian channels, where superior performance is achieved when compared with the classic detectors, without significant increase of the computational complexity.
Highlights
In last-generation cellular systems, information signals must be transmitted at high rates with high reliability [1]
The results demonstrate that the proposed reception technique is a generalization of the maximum likelihood detector (MLD) detector concept, presenting good performance when compared with the classical methods reported in the literature
CONTRIBUTIONS The main contributions of this work include: 1) The analysis of signal detection techniques in MIMO systems considering scenarios characterized by impulsive noise; 2) Introduction of a novel signal detection technique for MIMO systems through the use of correntropy; 3) Analysis of the proposed technique in scenarios characterized by impulsive noise with α-stable distribution and Gaussian channels; 4) Modeling of the adaptive kernel size for the proposed problem; FIGURE 1
Summary
PEDRO T. V. DE SOUZA 1, ALUÍSIO I. R. FONTES2, VINÍCIUS S. V. DE SOUZA1, AND LUIZ F. SILVEIRA3 This work was supported in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)-Finance Code 001, and in part by the High-Performance Computing Center at UFRN (NPAD/UFRN).
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