Deep learning driven beam selection for orthogonal beamforming with limited feedback

This letter studies deep learning methods for beam selection in multiuser beamforming with limited feedback. We construct a set of orthogonal random beams and allocate the to users maximize sum rate, based on feedback regarding channel power beams. formulate allocation problem as classification or regression task using neural network (DNN). The results demonstrate that DNN-based achieve higher rates than conventional solution low signal-to-noise ratio regime under Rician fading, thanks their robustness noisy