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Purpose Multiple input multiple-output (MIMO) has emerged as one among the many noteworthy technologies in recent wireless applications because of its powerful ability to improve bandwidth efficiency and performance, i.e. through developing its unique spatial multiplexing capability and spatial diversity gain. For carrying out an enhanced communication in next-generation networks, the MIMO and orthogonal frequency division multiple systems were combined that facilitate the spatial multiplexing on resource blocks (RBs) based on time-frequency. This paper aims to propose a novel approach for maximizing the throughput of cell-edge users and cell-center users. Design/methodology/approach In this work, the specified multi-objective function is defined as the single objective function, which is solved by the introduction of a new improved algorithm as well. This optimization problem can be resolved by the fine-tuning of certain parameters such as assigned power for RB, cell-center user, cell-edge user and RB allocation. The fine-tuning of parameters is attained by a new improved Lion algorithm (LA), termed as Lion with new cub generation (LA-NCG) model. Finally, the betterment of the presented approach is validated over the existing models in terms of signal to interference plus noise ratio, throughput and so on. Findings On examining the outputs, the adopted LA-NCG model for 4BS was 66.67%, 66.67% and 20% superior to existing joint processing coordinated multiple point-based dual decomposition method (JC-DDM), fractional programming (FP) and LA models. In addition, the throughput of conventional JC-DDM, FP and LA models lie at a range of 10, 45 and 35, respectively, at the 100th iteration. However, the presented LA-NCG scheme accomplishes a higher throughput of 58. Similarly, the throughput of the adopted scheme observed for 8BS was 59.68%, 44.19% and 9.68% superior to existing JC-DDM, FP and LA models. Thus, the enhancement of the adopted LA-NCG model has been validated effectively from the attained outcomes. Originality/value This paper adopts the latest optimization algorithm called LA-NCG to establish a novel approach for maximizing the throughput of cell-edge users and cell-center users. This is the first that work uses LA-NCG-based optimization that assists in fine-tuning certain parameters such as assigned power for RB, cell-center user, cell-edge user and RB allocation.

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Today’s wireless systems would be incomplete without MIMO systems, which have become increasingly common in recent years due to their ability to increase both spectrum efficiency and energy efficiency at a significant rate. Prior MIMO, the most often utilised system was a single-input, single-output system, which had limited capability and could not reliably support a huge number of users. Throughout this study, an artificial intelligence machine training MIMO channel is developed to handle the huge customer demand.. However, the ever-increasing needs are not met by this new technology. AI techniques becoming more prominent in determining the angle of elevation and angle of arrival for 3D MIMO in 5G system. These days, more and more people are using wireless devices, and these devices generate an enormous volume of information that must be processed quickly and reliably. AI based machine learning solve the issue and provide more throughput and energy efficient 5G system.

In recent decades, Multiple Input Multiple Output beamforming is deliberated as the vital technology enablers for 5G mobile radio services. Since, it provides noticeable improvement regarding throughput and coverage measures in 5G networks. Primarily, executed 3D MIMO beamforming using the modified Support Vector Machine algorithm which forms beam effectually to the users. The interference is mitigated in two stages that are small cell interference and macro cell interference by measuring the interference power from the cells. To provide better security to the data transmitted over Device-to-Device communication, Advanced Encryption Standard algorithm is used. The results attained from the simulations are auspicious in terms of metrics including throughput, Signal to Interference plus Noise Ratio (SINR) and Signal to Noise Ratio (SNR). From the simulation results, we prove that our ML-3DIM method increases throughput, SINR, SNR by up to 20%, 30% and 35% respectively compared to the existing methods including PABM, ULABM, and NOMA.