Slicing Framework for Service Level Agreement Guarantee in Heterogeneous Networks - A Deep Reinforcement Learning Approach

Heng Zhang, Shugong Xu*, Shunqing Zhang, Zhiyuan Jiang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

In 5G scenarios, network slicing and multi-tier heterogeneous networks are critical to guarantee the service level agreement (SLA) of various services. In this letter, a dynamic radio resource slicing framework considering joint bandwidth slicing ratios and base station (BS)-user association is presented for a two-tier heterogeneous wireless network. This framework maximizes the spectrum reuse ratio through a two-step deep reinforcement learning (DRL) method, and guarantees the SLA of network slices simultaneously. Specially, a distributed agent (D-Agent) is deployed at each BS for acquiring the slicing resource in a single BS level. Meanwhile, a centralized agent (C-Agent) manages radio resource allocation and user association among heterogeneous BSs to guarantee the SLA.

Original languageEnglish
Pages (from-to)193-197
Number of pages5
JournalIEEE Wireless Communications Letters
Volume11
Issue number1
DOIs
Publication statusPublished - 1 Jan 2022
Externally publishedYes

Keywords

  • deep reinforcement learning
  • heterogeneous networks
  • Network slices
  • service level agreements

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