Abstract
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.
Highlights
IntroductionThe improvement of data transmission rate in wireless communication is of great meaning
The improvement of data transmission rate in wireless communication is of great meaning.Three-dimensional-multiple-input-multiple-output (3D-MIMO) can improve channel capacity without increasing the transmission power and bandwidth
The traditional MIMO system has fewer ports that can only adjust the beam direction in the horizontal dimension, and cannot concentrate the vertical dimension energy on the receivers, while 3D-MIMO commonly used a two-dimensional antenna array in a large scale such as uniform planar array (UPA) [1] where hundreds of antennas can be placed in a small area
Summary
The improvement of data transmission rate in wireless communication is of great meaning. Three-dimensional-multiple-input-multiple-output (3D-MIMO) can improve channel capacity without increasing the transmission power and bandwidth It has been one of the key technologies in the 5th-Generation communication systems. Most of the existing communication theory assumes that the noise at the receivers follows a Gaussian distribution [3,4,5]. As the distance of each antenna on the array are relatively close, the support set of all elements can be considered as the same [16] Inspired by these works, this paper uses a CS-based reconstruction algorithm to estimate the common support of the angle domain channel matrix and obtain the sparse position of channel matrix firstly. The contribution of this paper is that it provides a framework of 3D-MIMO sparse channel estimation under non-Gaussian noise with unknown PDF.
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