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This paper presents DeepIA, a deep learning solution for faster and moreaccurate initial access (IA) in 5G millimeter wave (mmWave) networks whencompared to conventional IA. By utilizing a subset of beams in the IA process,DeepIA removes the need for an exhaustive beam search thereby reducing the beamsweep time in IA. A deep neural network (DNN) is trained to learn the complexmapping from the received signal strengths (RSSs) collected with a reducednumber of beams to the optimal spatial beam of the receiver (among a larger setof beams). In test time, DeepIA measures RSSs only from a small number of beamsand runs the DNN to predict the best beam for IA. We show that DeepIA reducesthe IA time by sweeping fewer beams and significantly outperforms theconventional IA's beam prediction accuracy in both line of sight (LoS) andnon-line of sight (NLoS) mmWave channel conditions.

Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networks