Abstract
This paper addresses the performance evaluation of the multiple input multiple output (MIMO) precoding technique, referred to as max-dmin precoding, over fading channel with impulsive noise in a railway tunnel. Measurements showed that the received signal at the antenna on the moving train roof near the catenary suffers from electromagnetic noise interference (EMI). This implies that the traditional Gaussian noise model is no longer valid and an impulsive noise model has to be considered. Based on this observation, we investigate the performance of the max-dmin MIMO precoding technique, based on the minimum distance criterion, in an impulsive noise modeled as an α-stable distribution. The main contributions are (i) a general approximation of the error probability of the max-dmin precoder, in the presence of Cauchy noise for an n r ×n t MIMO system, and (ii) the performance evaluation, in terms of bit error rate, of a complete communication system, considering a MIMO channel model in tunnel and impulsive noise, both obtained by measurements. Two soft detection techniques, providing the soft decisions to the channel decoder, are proposed based on the approximation of the probability density function of the impulsive noise by either a Gaussian or a Cauchy law.
Highlights
Spatial diversity offered by multiple input multiple output (MIMO) techniques can help to provide efficient transmissions in underground tunnels [1]
In [16], the performance analysis of three typical MIMO systems - zero forcing (ZF) system, maximum likelihood (ML) system, and space-time block coding (STBC) system - was performed in a mixture of Gaussian noise and impulsive noise
We focus on the max-dmin MIMO precoder in the presence of a specific impulsive noise, whose model was obtained thanks to measurements on the antenna dedicated to Global System for Mobile Communications-Railways (GSM-R) situated on the roof of a running train
Summary
Spatial diversity offered by multiple input multiple output (MIMO) techniques can help to provide efficient transmissions in underground tunnels [1]. In [17], authors analyzed the symmetric α-stable (SαS) noise component after performing ZF filtering in the receiver and deduced a probability density function (pdf ) approximation of the SαS noise component by using Cauchy-Gaussian mixture with bi-parameter model. Based on this approximated pdf, they provided a closedform expression of the BER performance in MIMO systems. 2.1 Impulsive noise: α-stable distribution α-stable random processes provide a suitable model for a wide range of non-Gaussian heavy-tailed impulsive noise encountered in wireless communication channels [12] This can be justified by the generalized central limit theorem considering that the noise results from a large number of possible impulsive effects [11].
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