Deep-Learning Based Beam Selection Technique for 6G Millimeter Wave Communication

One of the key technologies of next-generation 6G networks is millimeter-wave communications that will deploy a large number of antennas at the base station enabling narrow beams toward user locations to mitigate the path loss. Conventional methods have resulted in high training overhead in finding the best beam pair to obtain beam alignment between the base station and a user. This paper proposes a data-driven neural network approach to intelligently perform the beam selection between the transmitter-receiver pair. We propose a convolution neural network (CNN) based beam selection method trained from simulator-generated beam dataset. We use skip connections and hyperparameter optimization to balance the trade-off in accuracy and computational complexity. We validate the efficacy of our proposed method by comparing it with other conventional and machine learning-based approaches. Evaluation results show higher accuracy (> 70%) while reducing the computational complexity upto 15%.