Deeplearning Based Adaptive Beam Management Technique for Mobile Highspeed 5G mmWave Networks 5G core frequency band, mmWave, is about ten times wider than the existing commercial frequency band, enabling various services to be created. However, due to the characteristics of the mmWave frequency, it has various limitations. In the mobile environment, the misaligned beam problem, in which the SNR is degraded because the beam align between the sender and the receiver does not match, is one of the biggest problems to be solved. In this paper, we propose a adaptive beam management scheme based on deeplearning to solve misaligned beam problem. In the proposed scheme, 5G basestation (gNB) learns the mobility information, SNR, and current beam information of the associated user equipment (UE) by the deeplearning agent, and predicts whether the beam is aligned or not. From the prediction result, gNB and UE perform beam handsoff in advance before loss of connectivity.

Deep Neural Hybrid Beamforming for MultiUser mmWave Massive MIMO System Hybrid beamforming (HB) has emerged as a promising technology to support ultra high transmission capacity and with low complexity for Millimeter Wave (mmWave) multipleinput and multipleoutput (MIMO) system. However, the design of digital and analog beamformer is a challenge task with nonconvex optimization, especially for the multiuser scenario. Recently, the blooming of deep learning research provides a new vision for the signal processing of communication system. In this work, we propose a deep neural network based HB for the multiUser mmWave massive MIMO system, referred as DNHB. The HB system is formulated as an autoencoder neural network, which is trained in a style of endtoend selfsupervised learning. With the strong representation capability of deep neural network, the proposed DNHB exhibits superior performance than the traditional linear processing methods. According to the simulation results, DNHB outperforms about 2 dB in terms of bit error rate (BER) performance compared with existing methods.