Deep Reinforcement Learning-Based Load Balancing Algorithm for Sliced Ultra-Dense Network - Details

Author：

Sun, Ao (Sun, Ao.) | Yang, Feng (Yang, Feng.) | Wu, Wenjun (Wu, Wenjun.) | Wang, Tengda (Wang, Tengda.) | Sun, Yang (Sun, Yang.)

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Abstract：

With　the　deployment　of　end-to-end　network　slicing　(NS),　the　flexibility　of　ultra-dense　networks　(UDN)　can　be　enhanced　to　meet　diverse　requirements　of　various　services.　In　sliced　UDN,　load　balancing　is　an　important　factor　affecting　network　performance　and　service　quality.　Especially,　handoff　strategies　have　a　great　influence　on　load　balancing　performance.　In　this　paper,　the　handoff　problem　considering　load　balancing　issue　is　modeled　as　a　Markov　decision　process　(MDP),　which　takes　into　account　the　load　of　each　access　point,　service　profit,　outage　penalty,　and　handoff　cost.　A　deep　reinforcement　learning　(DRL)　based　load　balancing　handoff　algorithm　is　proposed　and　the　double　deep　Q　network　(DDQN)　is　trained　to　maximize　the　cumulative　reward.　The　proposed　algorithm　is　proved　to　be　converged　by　the　numerical　results　and　the　form　of　state　we　set　are　convinced　to　improve　convergence　performance.　The　proposed　algorithm　can　achieve　better　load　balancing　performance　compared　with　traditional　algorithms.　©　2023　IEEE.

Keyword：

Markov processes Reinforcement learning Learning algorithms Deep learning

Author Community：

[ 1 ] [Sun, Ao]Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 2 ] [Yang, Feng]Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 3 ] [Wu, Wenjun]Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 4 ] [Wang, Tengda]Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 5 ] [Sun, Yang]Beijing University of Technology, Faculty of Information Technology, Beijing, China

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Year： 2023

Page： 27-32

Language： English

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ESI Highly Cited Papers on the List： 0 Unfold All

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30 Days PV： 4

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