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In the sixth-generation (6G) cellular networks, hybrid beamforming would be areal-time optimization problem that is becoming progressively more challenging.Although numerical computation-based iterative methods such as the minimal meansquare error (MMSE) and the alternative manifold-optimization (Alt-Min) canalready attain near-optimal performance, their computational cost renders themunsuitable for real-time applications. However, recent studies havedemonstrated that machine learning techniques like deep neural networks (DNN)can learn the mapping done by those algorithms between channel stateinformation (CSI) and near-optimal resource allocation, and then approximatethis mapping in near real-time. In light of this, we investigate various DNNarchitectures for beamforming challenges in the terahertz (THz) band forultra-massive multiple-input multiple-output (UM-MIMO) and explore theircontextual mathematical modeling. Specifically, we design a sophisticated 1Dconvolutional neural network and long short-term memory (1D CNN-LSTM) basedfusion-separation scheme, which can approach the performance of the Alt-Minalgorithm in terms of spectral efficiency (SE) and, at the same time, usesignificantly less computational effort. Simulation results indicate that theproposed system can attain almost the same level of SE as that of the numericaliterative algorithms, while incurring a substantial reduction in computationalcost. Our DNN-based approach also exhibits exceptional adaptability to diversenetwork setups and high scalability. Although the current model only addressesthe fully connected hybrid architecture, our approach can also be expanded toaddress a variety of other network topologies.  INDEX TERMS 6G, CNN, Hybrid Beamforming, LSTM, UM-MIMO

A CNN-LSTM-based Fusion Separation Deep Neural Network for 6G Ultra-Massive MIMO Hybrid Beamforming