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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Two Tier Slicing Resource Allocation Algorithm Based on Deep Reinforcement Learning and Joint Bidding in Wireless

Geng Chen1, Xu Zhang1, Fei Shen2

  • 1College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

Sensors (Basel, Switzerland)
|May 20, 2022
PubMed
Summary

This study introduces a two-tier network slicing resource allocation algorithm using Deep Reinforcement Learning (DRL) and bidding. The novel approach enhances base station resource utilization and user service quality in 5G networks.

Keywords:
biddingdeep reinforcement learningnetwork slicing (NS)resource allocation

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Area of Science:

  • Telecommunications Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Network slicing (NS) in 5G networks allows resource partitioning for diverse services like enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC).
  • Effective network resource allocation is crucial due to limited base station (BS) resources and varying user demands.
  • Existing methods like bidding, deep learning (DL), ant colony, and wolf colony algorithms have been explored for resource allocation challenges.

Purpose of the Study:

  • To propose a novel two-tier network slicing resource allocation algorithm for wireless access networks.
  • To optimize the allocation of sliced network resources by integrating Deep Reinforcement Learning (DRL) and joint bidding mechanisms.
  • To improve both base station resource utilization and mobile user service quality.

Main Methods:

  • A two-tier resource allocation framework is implemented, dividing mobile operators into infrastructure providers (InPs) and mobile virtual network operators (MVNOs).
  • The upper tier utilizes a combination of bidding and Deep Q Network (DQN) for MVNOs to acquire resources from the base station.
  • The lower tier employs the Dueling DQN method for MVNOs to distribute acquired resources to connected mobile users (MUs), optimizing service quality.

Main Results:

  • The proposed algorithm shows a 5.4% increase in total system utility and revenue compared to double DQN in the upper tier, and a 2.6% increase over Dueling DQN.
  • In the lower tier, the algorithm provides more stable user service quality, with system utility and Se (service experience) improvements of 0.5-2.7% over DQN and Double DQN.
  • The Dueling DQN method in the lower tier demonstrates faster convergence for optimal resource distribution.

Conclusions:

  • The proposed two-tier DRL-based resource allocation algorithm effectively enhances network slicing efficiency in wireless access networks.
  • The integration of bidding and DRL, specifically DQN and Dueling DQN, optimizes resource utilization and user-centric service quality.
  • This approach offers a promising solution for managing complex resource allocation demands in advanced 5G and future mobile networks.