Reduce the Correlation Phenomena over Massive-MIMO System by Deep Learning Algorithms

Abstract

A novel design for degrading or decreasing the system performance due to the spatially channel correlated phenomenon by using of the artificial intelligence (AI) will be proposed in this project. Based on the AI the deep learning algorithms will be applied to solve the problems mentioned above. Frequently, the issues of correlated channel are solved by applying the data scrambling over forward link and avoiding the intersymbol interference (ISI) repeatedly, respectively. It is predicated that there will meet many challenges in the way to establish the 3-D correlated channel model. The beamforming technology used in the antenna pattern can be adopted to collect the data for feeding to the input of deep learning. The simulation and analyzed results will compare to the one that obtains from the traditional MIMO radio systems with correlated channels. The system performance is going to be optimized by the work out for the 3-D massive-MIMO communication in order to decrease the degradation of spatially correlated channel.