In this paper, we present an energy-efficient joint machine learning based beam-user
selection and low complexity hybrid beamforming for the multiuser massive multiple-input …

In this study, a ResNeSt-based deep learning approach to beamforming for 5G massive
multiple-input multiple-output (MIMO) systems is presented. The ResNeSt-based deep …

In a large-scale massive Multi User-Multiple Input Multiple Output (MU-MIMO) environment
channel estimation and beamforming is a breathtaking task for enhancing the array gain …

Massive multiple-input and multiple-output systems combined with hybrid beamforming
technique is a key approach to achieve high data rate and extended cell coverage in the fifth …

Hybrid multiple-input multiple-output (MIMO) systems have been thought as a promising
technology in future 5G. Compared with conventional digital MIMO systems, such a structure …

This paper presents a deep reinforcement learning-based beam-user selection and hybrid
beamforming design for the multiuser massive multiple-input multiple-output (MIMO) non …

In this paper, we present a machine learning based radio resource management (RRM)
scheme for a rank-constrained massive multiple-input multiple-output (MIMO) system. We …

Hybrid beamforming architecture that combines analog beamforming and digital
beamforming has been widely accepted in the emerging millimeter-wave (mmWave) …

Communications over millimeter-wave (mm-Wave) frequencies are considered as a new
revolution of wireless communications, specifically with the official launching of 5G …

In this paper, we study the channel estimation problem in microwave correlated massive
multiple-input-multiple-output systems with reduced number of radio-frequency chains. We …