Open AccessDeep learning integrated reinforcement learning for adaptive beamforming in B5G networks
Author(s) -
Eappen Geoffrey,
Cosmas John,
T Shankar,
A Rajesh,
Nilavalan Rajagopal,
Thomas Joji
Publication year - 2022
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/cmu2.12501
Subject(s) - reinforcement learning , computer science , artificial neural network , beamforming , artificial intelligence , base station , deep learning , path (computing) , reduction (mathematics) , beam (structure) , q learning , machine learning , telecommunications , engineering , mathematics , computer network , civil engineering , geometry
Abstract 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 appropriate 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 finding 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 prediction. 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 ±3 o and ±5°, respectively, the DLIRL beamforming displays a deviation of ±2 o . Moreover, the DLIRL can track the user equipment and steer the beam in its direction with an accuracy of 92%.