Three-dimensional-multiple-input-multiple-output (3D-MIMO) technology has attracted a lot of attention in the field of wireless communication. Most of the research mainly focuses on channel estimation model which is affected by additive-white-Gaussian-noise (AWGN). However, under the influence of some specified factors, such as electronic interference and man-made noise, the noise of the channel does not follow the Gaussian distribution anymore. Sometimes, the probability density function (PDF) of the noise is unknown at the receiver. Based on this reality, this paper tries to address the problem of channel estimation under non-Gaussian noise with unknown PDF. Firstly, the common support of angle domain channel matrix is estimated by compressed sensing (CS) reconstruction algorithm and a decision rule. Secondly, after modeling the received signal as a Gaussian mixture model (GMM), a data pruning algorithm is exerted to calculate the order of GMM. Lastly, an expectation maximization (EM) algorithm for linear regression is implemented to estimate the the channel matrix iteratively. Furthermore, sparsity, not only in the time domain, but in addition in the angle domain, is utilized to improve the channel estimation performance. The simulation results demonstrate the merits of the proposed algorithm compared with the traditional ones.
An Experimental Massive MIMO Channel Matrix Model for Hand-Held Scenarios