Paper Title
PERFORMANCE ANALYSIS OF SINGLE USER MASSIVE MIMO HYBRID BEAMFORMING SYSTEM AT 5G MILLIMETER WAVE

Abstract
Massive multiple-input multiple-output (MIMO) systems can become simpler and less expensive with the use of hybrid beamforming, which also offers high data speeds. Millimeter wave (mmWave) MIMO systems require careful consideration of hybrid beamformer design.The combination of massive MIMO systems, mmWave, and hybrid beamforming techniques achieve higher data rates and cell coverage in 5G wireless networks. Long-short term memory (LSTM) network is used to train numeric values from sequence data and predict new sequence data. In this work, the LSTM framework was investigated for the design of precoders and combiners. The base station (BS) has the channel statistics only and feeds the statistics into LSTM-1 and LSTM-2 to obtain the hybrid precoders. At the receiver, three LSTMs are employed. The first one is used for channel estimation, and the other two networks are employed to design the hybrid combiners. The performance is evaluated under different parameters, including signal-to-noise (SNR) ratio, number of transmit antennas, number of RF chains at the transmitter, and number of clusters at the carrier frequency (28GHz) through computer simulation using MATLAB. The simulation results illustrate that the system using the proposed LSTM-based approach the spectral efficiency achievable while the training, testing, and prediction process is offline deployment. Keywords - Hybrid Precoders and Combiners, Massive MIMO, Millimeter Wave (mmWave), LSTM,5G.