Deep learning approach for hybrid beamforming design in MU-MISO mmWave systems

Hybrid beamforming is a promising approach to alleviate hardware complexity in multi-user multiple-input single-output (MU-MISO) systems while maintaining high data rate performance. Unfortunately, hybrid beamforming architecture design is a challenging non-convex optimization problem due to stringent hardware constraints. However, traditional hybrid beamforming design methods, such as alternating minimization (AltMin) algorithms, rely on iterative optimization procedures that introduce heavy computational overhead and make them impractical for real-time applications. In this paper, we propose a deep learning (DL)-based hybrid beamforming method (DL-HBF) that aims to reduce computational latency while achieving acceptable sum-rate performance. Furthermore, we evaluate these methods based on a realistic channel model to ensure practical significance and their performance on imperfect channel state information (CSI). Additionally, we propose dataset generation procedures, which reduce the dataset creation and training overhead compared to existing DL-based hybrid beamforming methods that help in rapid deployment and scalability. Simulation results show that the proposed DL-HBF achieves an acceptable sum rate compared to traditional methods while reducing the computational complexity and maintaining robustness against channel estimation errors, which provides a practical solution for real-time hybrid beamforming for next-generation wireless systems.