Machine Learning Based Network Slicing and Resource Allocation for Electric Vehicles (EVs)

Gupta, Rajen K.; Choubey, Anurag; Jain, Shlok; Greeshma, R. R.; Misra, Rajiv

doi:10.1007/978-3-030-76736-5_31

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…Ref. [48] also studied vehicle use case in 5G networks. In particular, they focused on electrical vehicles by trying to assign them to charging stations with minimum collision and maximum usage of the network.…”

Section: Machine Learning Applied To Network Slicingmentioning

confidence: 99%

Integration of Network Slicing and Machine Learning into Edge Networks for Low-Latency Services in 5G and beyond Systems

2022

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Fifth-generation (5G) and beyond networks are envisioned to serve multiple emerging applications having diverse and strict quality of service (QoS) requirements. To meet ultra-reliable and low latency communication, real-time data processing and massive device connectivity demands of the new services, network slicing and edge computing, are envisioned as key enabling technologies. Network slicing will prioritize virtualized and dedicated logical networks over common physical infrastructure and encourage flexible and scalable networks. On the other hand, edge computing offers storage and computational resources at the edge of networks, hence providing real-time, high-bandwidth, low-latency access to radio network resources. As the integration of two technologies delivers network capabilities more efficiently and effectively, this paper provides a comprehensive study on edge-enabled network slicing frameworks and potential solutions with example use cases. In addition, this article further elaborated on the application of machine learning in edge-sliced networks and discussed some recent works as well as example deployment scenarios. Furthermore, to reveal the benefits of these systems further, a novel framework based on reinforcement learning for controller synchronization in distributed edge sliced networks is proposed.

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Section: Machine Learning Applied To Network Slicingmentioning

confidence: 99%

Integration of Network Slicing and Machine Learning into Edge Networks for Low-Latency Services in 5G and beyond Systems

2022

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“…In addition, considering intelligence as an main characteristic for the future wireless communication, many work have been investigated recently. The UAVenabled networks in which flying object is uses as access point of given flight period, seek to maximise the common throughput across the ground users [34][35][36]. In the paper [37], to address the massive connected devices UAV has used as air base station which aim to increase the performance by improving placement and power allocation.…”

Section: Related Workmentioning

confidence: 99%

Resource allocation for UAV-assisted 5G mMTC slicing networks using deep reinforcement learning

2022

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The Internet of Things (IoT) application scenarios is becoming extensive due to the quick evolution of smart devices with fifth-generation (5G) network slicing technologies and hence IoT becoming significantly important beyond fifth-generation (B5G) networks. However, communication with IoT devices is more sensitive in disasters because the network depends on the main power supply and devices are fragile. In this paper, we consider Unmanned Aerial Vehicles (UAV) as a flying base station (BS) for the emergency communication system with 5G mMTC Network Slicing to improve the quality of service. The UAV-assisted mMTC creates a base station selection method with the aim of maximizing the system energy efficiency. Then, the system model is reduced into the stochastic optimization based problem using Markov Decision Process (MDP) theory. We propose a Dueling-Deep-Q-Networks (DDQN) based approach based on Reinforcement Learning (RL) technique for maximization of energy efficiency to solve the resource allocation problem. We compare the proposed model with DQN and Q-Learning models and found that the proposed DDQN based model performs better for resource allocation in terms of low transmission power and maximum energy efficiency.

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Machine Learning Based Adaptive Auto-scaling Policy for Resource Orchestration in Kubernetes Clusters

Dixit

Gupta

Dubey

et al. 2022

Internet of Things and Connected Technologies

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Machine Learning Based Network Slicing and Resource Allocation for Electric Vehicles (EVs)

Cited by 8 publications

References 11 publications

Integration of Network Slicing and Machine Learning into Edge Networks for Low-Latency Services in 5G and beyond Systems

Integration of Network Slicing and Machine Learning into Edge Networks for Low-Latency Services in 5G and beyond Systems

Resource allocation for UAV-assisted 5G mMTC slicing networks using deep reinforcement learning

Machine Learning Based Adaptive Auto-scaling Policy for Resource Orchestration in Kubernetes Clusters

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