期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2016
卷号：5
期号：3
页码：15986-15990
DOI：10.18535/ijecs/v5i3.18
出版社：IJECS
摘要：Pilot contamination posts a elementary limit on the performance of huge multiple-input–multiple-output (MIMO)antenna systems owing to failure in correct channel estimation. To address this drawback, we tend to propose estimationof solely the channel parameters of the specified links during a target cell, however those of the interference links fromadjacent cells. The desired estimation is, nonetheless, AN underdetermined system. During this paper, we show that if thepropagation properties of huge MIMO systems will be exploited, it's potential to get a correct estimate of the channelparameters. Our strategy is impressed by the observation that for a cellular network, the channel from userinstrumentality to a base station consists of solely a number of clustered methods in space. With an awfully massiveantenna array, signals may be discovered under extraordinarily sharp regions in space. As a result, if the signals arediscovered within the beam domain (using Fourier transform), the channel is around thin, i.e., the channel matrix containsonly a little fraction of huge elements, and different elements are near zero. This observation then permits channelestimation based on thin Bayesian learning strategies, wherever thin channel components may be reconstructed employinga little variety of observations. Results illustrate that compared to traditional estimators; the planned approach achievesfar better performance in terms of the channel estimation accuracy and doable rates in the presence of pilotcontamination.
关键词：channel estimation; Gaussian mixture;massive MIMO; pilot contamination
