High data rates can be achieved with large-scale MIMO systems by using hybrid beamforming (HBF), which also simplifies and lowers MIMO systems' costs, but designing hybrid precoders is an extremely challenging task that requires handling CSI feedback and solving demanding optimization problems. With the RSSI-based unsupervised deep learning approach introduced in this paper, hybrid beamforming is designed for large-scale MIMO systems. In addition, there are two approaches provided: one for designing the codebook for the simulated pre-encoder, and another for designing the synchronization signal (SS) for the initial access (IA), and different scenarios and realistic channel models are used to evaluate the system performance.
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Hybrid beamforming design for massive MIMO system: a deep learning approach
Semantic Scholar · Engineering · 2023
Abstract
High data rates can be achieved with large-scale MIMO systems by using hybrid beamforming (HBF), which also simplifies and lowers MIMO systems' costs, but designing hybrid precoders is an extremely challenging task that requires handling CSI feedback and solving demanding optimization problems. With the RSSI-based unsupervised deep learning approach introduced in this paper, hybrid beamforming is designed for large-scale MIMO systems. In addition, there are two approaches provided: one for designing the codebook for the simulated pre-encoder, and another for designing the synchronization signal (SS) for the initial access (IA), and different scenarios and realistic channel models are used to evaluate the system performance.