Deep Reinforcement Learning-based resource allocation strategy for Energy Harvesting-Powered Cognitive Machine-to-Machine Networks

Abstract

Machine-to-Machine (M2M) communication is a promising technology that may realize the Internet of Things (IoTs) in future networks. However, due to the features of massive devices and concurrent access requirement, it will cause performance degradation and enormous energy consumption. Energy Harvesting-Powered Cognitive M2M Networks (EH-CMNs) as an attractive solution is capable of alleviating the escalating spectrum deficient to guarantee the Quality of Service (QoS) meanwhile decreasing the energy consumption to achieve Green Communication (GC) became an important research topic. In this paper, we investigate the resource allocation problem for EH-CMNs underlaying cellular uplinks. We aim to maximize the energy efficiency of EH-CMNs with consideration of the QoS of Human-to-Human (H2H) networks and the available energy in EHdevices. In view of the characteristic of EH-CMNs, we formulate the problem to be a decentralized Discrete-time and Finite-state Markov Decision Process (DFMDP), in which each device acts as agent and effectively learns from the environment to make allocation decision without the complete and global network information. Owing to the complexity of the problem, we propose a Deep Reinforcement Learning (DRL)based algorithm to solve the problem. Numerical results validate that the proposed scheme outperforms other schemes in terms of average energy efficiency with an acceptable convergence speed.

Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

“…Another resource allocation strategy for EH-CM2M networks is proposed in [136] that uses the deep reinforcement learning approach to enhance energy efficiency. An alternative solution to the M2M energy issue is to shift traffic to the device to device communication.…”

FPIRST: Fatigue Driving Recognition Method Based on Feature Parameter Images and a Residual Swin Transformer

“…Image classification has always been a hot research direction, and the emergence of deep learning has promoted the development of this field. At present, image classification methods include the feature extraction-based method [ 13 , 14 , 15 ] and the deep learning (DL) method [ 16 , 17 ], in which the DL method mainly include convolution neural networks (CNNs) and Transformer.…”

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

“…Energy-harvesting-powered cognitive machine-to-machine networks can mitigate the intensifying deficient spectrum, as a result of large-scale smart devices and simultaneous access demand that bring about operational deterioration and massive energy use [ 92 ], by ensuring the quality of service and leading to green communication through deep-reinforcement-learning-based algorithms in terms of energy efficiency optimization. The end-to-end throughput can be assessed and enhanced in wireless-powered cognitive IoT networks through the use of a well-organized deep-neural-network-based relay selection scheme [ 93 ]: multiple energy-harvesting relays are harnessed unselectively to enable data sharing to multiple users from a source node across energy-harvesting circuit practical nonlinearity, decreasing computational complexity significantly.…”