A Metaheuristics-Based Hyperparameter Optimization Approach to Beamforming Design

Abstract

The paradigm shift from ''connected things'' to ''connected intelligence'' is anticipated to be made possible by the sixth-generation wireless systems, which typically use millimeter wave beamforming to mitigate the significant propagation loss. However, beamforming design in millimeter wave communications poses many different challenges owing to the large antenna arrays with the limitation of radio frequency chains and analog beamforming architectures. To circumvent this problem, deep learning models have recently been utilized as a disruptive method for solving difficult optimization problems in sixth-generation mobile systems, such as maximizing spectral efficiency. However, it is still unclear how to produce high-performance deep learning models which require considering appropriate hyperparameters. This study proposes a metaheuristics-based approach for optimizing hyperparameters that are used to build optimized deep learning models to maximize spectral efficiency. The research results demonstrate that the proposed approach-based models establish higher spectral efficiency than the state-of-the-art approach-based models and the reference model whose hyperparameters are based on empirical trials.

Air pressure prediction model based on the fusion of laser-induced plasma images and spectra

“…18 Hyperparameter optimization is a common method for improving model performance. [19][20][21] Fernández et al utilized hyperparameter optimization techniques to train neural network models, achieving the bestperforming model in terms of accuracy with a minimal risk of overtting for the classication of spectral data; the model demonstrated an overall accuracy exceeding 80% on an 11-class spectral dataset, highlighting its robust generalization capabilities. 22 For specic tasks, pre-trained models oen fail to achieve optimal performance, necessitating improvements to the model.…”