Deep learning integrated reinforcement learning for adaptive beamforming in B5G networks

In this paper, a deep learning integrated reinforcement learning (DLIRL) algorithm is proposed for comprehending intelligent beamsteering in Beyond Fifth Generation (B5G) networks. The smart base station in B5G networks aims to steer the beam towards appro priate user equipment based on the acquaintance of isotropic transmissions. The foremost methodology is to optimize beam direction through reinforcement learning that delivers significant improvement in signal to noise ratio (SNR). This includes alternate path find ing during path obstruction and steering the beam appropriately between the smart base station and user equipment. The DLIRL is realized through supervised learning with deep neural networks and deep Q-learning schemes. The proposed algorithm comprises of an online learning phase for training the weights and a working phase for carrying out the pre diction. Results confirm that the performance of the B5G system is improved considerably as compared to its counterparts with a spectral efficiency of 11 bps/Hz at SNR = 10 dB for a bit error rate performance of 10−5. As compared to reinforced learning and deep neural network with a deviation of ±3o and ±5◦, respectively, the DLIRL beamforming displays a deviation of ±2o. Moreover, the DLIRL can track the user equipment and steer the beam in its direction with an accuracy of 92%.