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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 ±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%. [ABSTRACT FROM AUTHOR]

In this paper, massive multiple‐input‐multiple‐output (MIMO) wireless communication systems are considered to investigate joint transceiver beamforming. A base station (BS) equipped with a uniform planar array (UPA) serves several multi‐antennas users in a single cell. Based on the channel state information (CSI), the low complexity design of transceiver beamforming to minimize the transmit power subject to some quality of service (QoS) constraints is investigated. As the upper bound of the transmit power performance, the existing iteration‐based algorithms are leveraged as a reference. A general deep learning (DL)‐based framework and deep neural network (DNN) structure are proposed to reduce the complexity of the existing algorithms, where the properly trained DNN structure can learn directly from CSI. Consider the complexity of the DNN structure itself, a heuristic algorithm is proposed to replace the DNN structure, which takes the max‐eigenvalue‐eigenvector of the CSI as the direction of receive beamforming directly. The DNN structure is trained in the offline stage, therefore, only the complexity in the online stage is taken into consideration. Based on the numerical simulation, the complexity of the proposed DL‐based framework and the transceiver beamforming algorithms is reduced significantly while maintaining nearly the optimal performance compared with the existing iterative algorithms. [ABSTRACT FROM AUTHOR]