Robust CSI Estimation Under Complex Communication Environment

Channel estimation is the critical and fundamental problem in wireless communication techniques, however, the complexity environment, including interference and noise, post a fundamental limit on the accuracy of channel estimation on practical applications. Most existing channel estimation techniques are based on the simple assumption of Gaussian white noise, which makes the performance poorly within real communication environment. To address this problem, we propose a new channel estimation method by assuming the environment as Mixture of Gaussian (MoG) distributions and penalized MoG (PMoG) model by combining the penalized likelihood method with MoG distributions. This model is proposed by the first time in the research of wireless communication, and the superiority of this method lies on its approximation capability to wide range of scenarios of complex communication environments adaptively and analyzing the environment by learning the proper number of statistical components. Moreover, we design an Expectation Maximization (EM) algorithm to estimate the parameters of the PMoG model. The advantage of our method is demonstrated by simulation experiments.