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The beamforming approach has been emerging as a very important concept for next generation networks. In addition to the improved channel capacity, spectral efficiency, energy efficiency, secrecy rate and secrecy outage probability, the upcoming fifth generation network mainly aims at enhancing the parameters of the channel for secure communication. In this paper, we have implied the allocation of resource blocks adaptively using HMM with a beamforming approach in an intruded network. A system model for secure communication in an intruded network has been discussed using a beamforming approach with the main motive being to provide a security scenario to the data which is transmitted over an unsecured channel in a network. In addition to this we have used the approach of HMM for allocating the resource blocks to the users which have been demanded and applied in order to avoid the intrusion and wastage of resource blocks.
Academics and businesses alike are presently fixated on the Sixth Generation (6G) network, which is seen as the telecom industry’s next major game-changer. The 6G architecture has not been finalized and is not yet being used in commercial settings. Research and development for 6G is still in its early stages. Several important features and technologies have surfaced as possible 6G system underpinnings, while development is still in its early phases. To facilitate the next generation of 3D modeling applications, this article suggests a new 6G cellular communication architecture. Aware of 6G’s revolutionary potential, we investigate its fundamental features to build a collaborative, real-time 3D modeling environment with unmatched capabilities. The goal of this project is to design the architecture for the next generation of communications systems. This will incorporate elements from two existing designs: the decoupled RAN design, which improves security and smooth data sorting, and the high-level design, which incorporates numerous protocols inside the antenna for stringent protection. Lastly, we delve into the possible sector-specific disruptions caused by this design and examine its wider implications for the future of 3D modeling. We hope that by introducing this new 6G architecture, we may inspire more study and development towards a day when 6G technology completely changes the game when it comes to 3D modeling.
Event-triggered reinforcement learning-based internet data bandwidth allocation technique as a metric for balanced QoS and QoE
Article ID: 3135
Vol 2, Issue 4, 2024
DOI: https://doi.org/10.54517/cte3135
Vol 2, Issue 4, 2024
Received: 16 November 2024; Accepted: 20 December 2024; Available online: 29 December 2024; Issue release: 31 December 2024
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Abstract
This research work studies the performance and management of the internet services of institutions of higher learning in Nigeria. Data were collated from a federal, state, and private university designated as FEDERAL1, STATE1, and PRIVATE1, respectively, in this research study. The reinforcement learning-based internet data bandwidth allocation model was developed for bandwidth allocation and prediction to enhance balanced quality of service (QoS) and quality of experience (QoE) of the users. The linear Lagrange’s method of interpolation, the LILARINT model, was developed and implemented to predict and allocate effective internet data bandwidth for the significantly increasing number of internet users in each of the institutions. The problem of inability to predict and allocate acceptable internet data bandwidth with the corresponding number of internet users was solved by the LILARINT model. The Allen’s PRESS regression, R2 of the LILARINT models, was very close to unity, which is an indication that the models developed stood at the very best fit. In this research work, it is clear that PRESS regressions, R2 for the selected institutions, were better than the regression, R2 obtained from Nielsen’s institution. Using the measured and simulated results, we found out that PRESS regression, R2 has significantly performed best in the LILARINT model developed. In the overall comparative analysis, the FEDERAL1 LILARINT model emerged as the most reliable model developed and implemented. The model has a regression, R2 of 0.9999, mean squared error (MSE) of 1.455, mean absolute deviation (MAD) of 122.6920, Standard Deviation (σ) of 8.2975, and mean absolute percentage error (MAPE) of 0.6274%.
Keywords
reinforcement learning-based; quality of service (QoS); quality of experience (QoE); internet data bandwidth; allocation; prediction; linear Lagrange’s interpolation model
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Copyright (c) 2024 Michael F. Adaramola, Oluwagbemiga O. Shoewu, Mary A. Adedoyin, Emmanuel B. Balogun
License URL: https://creativecommons.org/licenses/by/4.0/
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Prof. Maode Ma
Qatar University, Qatar
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