AI-Driven Resource Allocation in 6G Wireless Ad Hoc Networks: Optimizing Cloud-Edge Interoperability
DOI:
https://doi.org/10.54228/mjaret0624006Keywords:
AI-Driven Resource Allocation; 6G Networks; Cloud-Edge Interoperability; Deep Reinforcement Learning (DRL); Network Softwarization; Virtualization; Task Execution EfficiencyAbstract
In this paper, we introduce an AI-driven resource allocation scheme for 6G wireless ad hoc networks, in which the interoperability between cloud and edge computing in network execution is optimized with the main objectives of improving task execution efficiency and reducing network latency. Specifically, a system is designed to address the resource utilization challenge in different network conditions with dynamic and scalable processing requirements. The proposed framework integrates the Deep Reinforcement Learning (DRL) with Network Softwarization and Virtualization to enable dynamic call admission control and adaptation to real-time network traffic demands based on the AI-based algorithms. As demonstrated by computer simulations, in comparison to the traditional methods, the proposed framework can improve resource utilization by 35%, reduce task completion latency by 20%, and achieve a 25% improvement in task completion efficiency. We can conclude that this work presents a promising solution for 6G-enabled wireless ad hoc networks with large-scale distributed devices.
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