Deep learning empowered channel estimation in massive MIMO: unveiling the efficiency of hybrid deep learning architecture

Massive Multiple-Input Multiple-Output (MIMO) technology has changed the way wireless connectivity works and promises to make spectral efficiency better than ever before. Traditional methods, like Maximum Ratio Transmission (MRT) beamforming, have problems with big antenna arrays, channel estimation errors, and wireless channel variability. To get rid of these problems, the proposed model presents a novel deep learning method that combines Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) networks. It uses both temporal and spatial relationships in channel data to make channel estimation and beamforming better in massive MIMO systems. The comparison results show the efficiency of the proposed method with respect to state-of-the art methods for channel estimation in massive MIMO. At the beginning of the study, massive MIMO technology is thoroughly evaluated, with a focus on both its benefits and drawbacks. We discuss the theoretical foundations of MRT beamforming and its limitations when dealing with large antenna arrays. To tackle these challenges, we describe a novel deep learning architecture that leverages the temporal and spatial relationships seen in the channel data through the use of GRU and LSTM layers. A comprehensive method including model designs, training schedules, metrics for performance assessment, and data generation is explained. We perform comprehensive controlled simulations that allow us to compare the GRU + LSTM approach and MRT’s spectrum efficiency. The results offer exciting new insights. This research not only shows improved spectral efficiency and robustness to channel variations, but it also elucidates the trade-offs between deep learning and conventional methods in wireless communication systems, indicating that deep learning could be essential to achieving the full benefits of Massive MIMO.