Abstract
This paper presents an innovative Convolutional Neural Network (CNN) architecture designed for the prediction of beamforming vectors in Massive Multiple-Input Multiple-Output (MIMO) systems, a cornerstone technology in 5G and emerging wireless communication networks. As mmWave frequencies become central to modern MIMO systems, ensuring precise beam alignment amid susceptibility to blockages is imperative for maintaining robust communication links. Traditional beamforming algorithms, challenged by high complexity and sluggish adaptation to dynamic environmental conditions, are outperformed by the proposed CNN, which effectively captures complex channel patterns for accurate beam prediction. Leveraging a rich dataset from the DeepSense 6G scenarios, the study meticulously preprocesses the data through normalization and scaling before dividing it into training and testing subsets. The CNN, evaluated against real-world signal propagation behaviors, demonstrates a high prediction accuracy in beam indices, particularly in clear line-of-sight scenarios. The paper concludes with an assessment of the model's predictive performance, using Mean Absolute Error as a key metric, and proposes future enhancements to the model's architecture and training process. The implications of this work are significant, heralding a new era of intelligent, self-optimizing wireless networks that can adapt autonomously to environmental changes and user mobility

Key words: Massive MIMO; CNN; Beamforming; 5G