Study the Performance of Channel Estimation Techniques for Massive MIMO Systems

Abstract

The number of wirelessly connected gadgets has increased dramatically during the past ten years. Wireless networks connect and manage billions of devices. . At the same time, each device needs a high performance connection for applications like as voice, real-time video, movies and games. The demand is always strong for wireless throughput and the quantity of wireless devices. Massive MIMO, coupled with 5G and other technologies, can satisfy this need with more capacity and efficiency. An important part of Massive MIMO system is channel estimation which is essential to optimize link performance. Acquiring channel state information (CSI) is particularly challenging for Massive MIMO systems because of the massive number of antennas, the complex architecture of the Transceiver and the large exploited bandwidth. But which one of the channel estimators should be used for a specific situation is another problem. This thesis will analyze the performance of channel estimation techniques specifically the least square (LS) and minimum mean square error channel (MMSE) Element-wise MMSE Channel Estimator ,(EW MMSE) estimation methods for massive MIMO systems and based on the analysis results, different discussions and conclusions are made. Furthermore, comparison among their characteristics is simulated in MATLAB and useful conclusions are given. To achieve this goal different algorithms and MATLAB simulation results will be used. When the performance of the channel estimators was tested using the maximum ratio (MR) detection algorithms, the result showed that the MMSE estimator performed better In terms of spectral efficiency. While EM MMSE and LS estimators less Computational complexity. Different comparison techniques were also run and they showed that the MMSE estimator to be shown more complex than the LS and EW MMSE estimator by the increase in the number of users and the number of antennas array used.