An MDP-Based Vertical Handoff Decision Algorithm for Heterogeneous Wireless Networks

By Enrique Stevens-Navarro, Yuxia Lin, Vincent W. S. Wong. IEEE Transactions on Vehicular Technology, 2008

465 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/a962e0f59763de9832a4045a2e78d17e5807b450

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions (2019, methodology): “Because RL-based algorithms can adapt to a changing environment by learning through their interactions and from the environment, RL has been studied for many algorithms [110] relevant to load balancing [70]–[72], [88], mobility management [67], [73]–[75], [87], user association [76], [77], [99], and”
An overview of load balancing in hetnets: old myths and open problems (2013, background): “In the context of HetNets, MDPs have been used to study handoff between different radio access technologies (RATs), for example, cellular to WiFi offloading [4].”
A survey of vertical handover decision algorithms in Fourth Generation heterogeneous wireless networks (2010, background): “Many studies have been done in this area [3,8,56,9,23,18,43,40,24].”
Offloading in Heterogeneous Networks: Modeling, Analysis, and Design Insights (2012, background): “Fully centralized schemes, such as in [8]– [10], try to maximize a network wide utility as a solution to the association optimization problem.”
Mathematical Modeling for Network Selection in Heterogeneous Wireless Networks — A Tutorial (2013, background): “[51], [52] provides an idea for modeling the network selection problem into an MDP.”
Mobile Edge Computing (2021, background): “This can be achieved by disregarding opportunities for stronger connection with nearer base stations while the device’s weaker connection with its present base station remains useful [40][3], and by delaying the migration of VMs even when the device connects with a new base station [43].”
Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks (2018, background): “But unlike many decision making problems, such as vertical handoff [6] and power allocation [7], that can”