Optimizing Energy-Efficient 5G Resource Allocation for Machine-Type Communication through Reinforcement Learning
Keywords:
Machine-to-Machine, Energy Efficiency, Re- source Allocation, Reinforcement learning, 5GAbstract
With the proliferation of machine-type communication (MTC) devices in 5G networks, there is a growing need to optimize resource allocation to ensure energy efficiency and network performance. The advent of 5G networks has opened up opportunities for massive connectivity of MTC devices in various application domains, such as Internet of Things (IoT) sensors and devices. However, the resource allocation for MTC poses significant challenges due to the dynamic and diverse traffic patterns generated by a large number of devices. Traditional resource allocation methods may not be suitable for handling MTC’s unique energy efficiency and scalability requirements. This re- search paper proposes a novel approach using reinforcement learning to optimize energy-efficient resource allocation for MTC in 5G networks. This paper presents a reinforcement learning- based approach to optimize resource allocation in 5G networks for MTC. The proposed approach utilizes reinforcement learning algorithms to learn and adapt resource allocation policies based on real-time network conditions and device demands. By employing a reward-based mechanism, the system maximizes energy efficiency while meeting the quality of service (QoS) requirements of MTC devices. The experimental evaluations demonstrate the effectiveness of the proposed approach in optimizing energy- efficient resource allocation for MTC in 5G networks. The results show significant improvements in energy consumption and network performance compared to traditional resource allocation methods. The reinforcement learning-based approach adapts to varying traffic conditions, effectively balancing resource allocation and minimizing energy waste. The contributions of this research include the development of a novel framework for energy-efficient resource allocation in 5G networks, specifically tailored for MTC. The proposed approach enables intelligent decision-making and adaptation based on real-time network dynamics by leveraging reinforcement learning techniques. The optimization of resource allocation for MTC devices in 5G networks enhances energy efficiency, scalability, and overall network performance.
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