Beam Prediction in 5G and 6G Systems: A Deep Learning Approach

Exploiting beamforming techniques to direct radio waves into narrow beams targeted at users are essential in 5G and 6G to ensure high data rate requirements with low latency. However, beamforming in a dynamic environment is challenging due to the high sensitivity to shift in the receiver location. Therefore, an adaptive beamforming approach is needed. In this paper, we utilize DeepSense 6G and 5G MIMO datasets for optimal beam prediction using deep learning approaches. We introduce a simple approach to transform radar input into vector embeddings in the DeepSense 6G dataset. Moreover, we address the severe minority classes problem in the 5G MIMO dataset. Finally, we conduct a thorough analysis of the state-of-the-art models. The proposed model shows significant improvements in the Top-1, Top-3, Top-5, weighted average and distance based accuracies.