An approach to energy efficient vertical handover technique for heterogeneous networks

Baghla, Silki; Bansal, Sandeep

doi:10.1007/s41870-018-0115-2

Cited by 7 publications

(1 citation statement)

References 15 publications

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“…With the rapid development of wireless communication technology and the large-scale popularity of mobile intelligent terminals, a single network cannot meet the needs of users. The future wireless network will be a heterogeneous network composed of different technologies and types [1]- [3]. As one of the key technologies of 5G communication, heterogeneous ultra dense network technology was explicitly included in the "5G Concept White Paper" released by IMT-2020 [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Research on 5G Heterogeneous Ultra Dense network selection algorithm based on Dueling- DDQN

Xie

Zhu

2023

Preprint

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In the 5G Heterogeneous Ultra Dense Network environment, due to the dense deployment of the network and the diversity of user service types, a more flexible network selection algorithm is required to reduce the network blocking rate and improve the user’s quality of service (QoS). Considering the QoS requirements and preferences of the users, a network selection algorithm based on Dueling-DDQN is proposed by using deep reinforcement learning, which defines the state, action space and reward function of the different services to maximize the cumulative reward value of the network selection algorithm. The simulation results show that compared with other algorithms, the proposed algorithm can effectively reduce the network blocking rate while reducing the switching times.

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