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An Intelligent Resnets Resource Allocation Framework for 5G Networks


Affiliations
1 Department of Computer Science and Engineering, Hindusthan Institute of Technology, India., India
2 Department of Information Technology, Karpagam Institute of Technology, India., India
     

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This paper presents a resource allocation technique for industrial applications for 6G networks, which are characterised by the presence of many heterogeneous parameters that have an effect on the quality of data transmission. The purpose of the project is to achieve the greatest possible efficiency in the application of the resources that are presently while achieving a higher level of control over a diverse collection of sensing nodes operating within a hybrid network. The system model that has been proposed is a workable option for efficient resource allocation. The performance of the proposed method, in addition to similarities to the performance of other methods has been analysed. The proposed methods offer performance that is comparable to or better than the baseline, while simultaneously significantly reducing the SI exchange overhead and improving the system resilience to sensing intervals, some of which may be unavoidable in practise.

Keywords

ResNets, Resource Allocation, 6G, IoT.
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  • V. Saravanan, D. Saravanan and H.P. Sultana, “Design of Deep Learning Model for Radio Resource Allocation in 5G for Massive IoT Device”, Sustainable Energy Technologies and Assessments, Vol. 56, pp. 103054-103064, 2023.
  • M. Sheng and J. Li, “Coverage Enhancement for 6G Satellite-Terrestrial Integrated Networks: Performance Metrics, Constellation Configuration and Resource Allocation”, Science China Information Sciences, Vol. 66, No. 3, pp. 1-20, 2023.
  • J. Singh, J. Deepika and J. Sathyendra Bhat, “EnergyEfficient Clustering and Routing Algorithm Using Hybrid Fuzzy with Grey Wolf Optimization in Wireless Sensor Networks”, Security and Communication Networks, Vol. 2022, pp. 1-12, 2022.
  • J. Huan and K. Yu, “Opportunistic Capacity based Resource Allocation for 6G Wireless Systems with Network Slicing”, Future Generation Computer Systems, Vol. 140, pp. 390- 401, 2023.
  • Y. Robinson, E.G. Julie and P.E. Darney, “Enhanced Energy Proficient Encoding Algorithm for Reducing Medium Time in Wireless Networks”, Wireless Personal Communications, Vol. 131, pp. 3569-3588, 2021.
  • R. Indhumathi and A. Pandey, “Design of Task Scheduling and Fault Tolerance Mechanism Based on GWO Algorithm for Attaining Better QoS in Cloud System”, Wireless Personal Communications, Vol. 95, pp. 1-19, 2022.
  • P. Qin and S. Geng, “Content Service Oriented Resource Allocation for Space-Air-Ground Integrated 6G Networks: A Three-Sided Cyclic Matching Approach”, IEEE Internet of Things Journal, Vol. 10, No. 1, pp. 828-839, 2022.
  • S.U. Jamil, “Resource Allocation and Task Off-Loading for 6G Enabled Smart Edge Environments”, IEEE Access, Vol. 10, pp. 93542-93563, 2022.
  • T. Karthikeyan, K. Praghash and K.H. Reddy, “Binary Flower Pollination (BFP) Approach to Handle the Dynamic Networking Conditions to Deliver Uninterrupted Connectivity”, Wireless Personal Communications, Vol. 121, No. 4, pp. 3383-3402, 2021.
  • T.Q. Duong and H. Shin, “Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions”, IEEE Open Journal of Vehicular Technology, Vol. 3, pp. 375-387, 2022.
  • F.D.O. Torres, D.L. Cardoso and R.C. Oliveira, “Radio Resource Allocation in a 6G D-OMA Network with Imperfect SIC: A Framework Aided by a Bi-Objective Hyper-Heuristic”, Engineering Applications of Artificial Intelligence, Vol. 119, pp. 105830-105843, 2023.
  • D.H. Tran and B. Ottersten, “Satellite-and Cache-Assisted UAV: A Joint Cache Placement, Resource Allocation, and Trajectory Optimization for 6G Aerial Networks”, IEEE Open Journal of Vehicular Technology, Vol. 3, pp. 40-54, 2022.
  • H.B. Salameh and A. Al-Ajlouni, “Energy-Efficient Power-Controlled Resource Allocation for MIMO-based Cognitive-enaBled B5G/6G Indoor-Flying Networks”, IEEE Access, Vol. 10, pp. 106828-106840, 2022.
  • T.K. Rodrigues and N. Kato, “Network Slicing with Centralized and Distributed Reinforcement Learning for Combined Satellite/Ground Networks in a 6G Environment”, IEEE Wireless Communications, Vol. 29, No. 1, pp. 104-110, 2022.

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  • An Intelligent Resnets Resource Allocation Framework for 5G Networks

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Authors

S. Ramasamy
Department of Computer Science and Engineering, Hindusthan Institute of Technology, India., India
P.N. Periyasamy
Department of Computer Science and Engineering, Hindusthan Institute of Technology, India., India
M. Sathiya
Department of Information Technology, Karpagam Institute of Technology, India., India

Abstract


This paper presents a resource allocation technique for industrial applications for 6G networks, which are characterised by the presence of many heterogeneous parameters that have an effect on the quality of data transmission. The purpose of the project is to achieve the greatest possible efficiency in the application of the resources that are presently while achieving a higher level of control over a diverse collection of sensing nodes operating within a hybrid network. The system model that has been proposed is a workable option for efficient resource allocation. The performance of the proposed method, in addition to similarities to the performance of other methods has been analysed. The proposed methods offer performance that is comparable to or better than the baseline, while simultaneously significantly reducing the SI exchange overhead and improving the system resilience to sensing intervals, some of which may be unavoidable in practise.

Keywords


ResNets, Resource Allocation, 6G, IoT.

References