Multi-Agent Deep Reinforcement Learning Joint Beamforming for Slicing Resource Allocation

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In 5G Radio Access Networks (RAN), network slicing is a crucial technology for offering a variety of services. Inter-slice resource allocation is important for dynamic service requirements. In order to implement inter-slice bandwidth resource allocation at a large time scale, we used the Multi-Agent deep reinforcement learning (DRL) Asynchronous Advantage Actor Critic (A3C) algorithm with a focus on maximizing the utility function of slices. In addition, we used the K-means algorithm to categorize users for beam learning. We used the proportional fair (PF) scheduling technique to allocate physical resource blocks (PRBs) within slices at a small time scale. The results show that the A3C algorithm has a very fast convergence speed for utility function and packet drop rate. It is superior to alternative approaches, and simulation results support the proposed approach.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Wireless Communications Letters
Publication statusAccepted/In press - 2024


  • Array signal processing
  • Asynchronous Advantage Actor Critic (A3C)
  • Beamforming
  • Deep learning
  • Indexes
  • K-means
  • Network Slicing
  • Quality of experience
  • Radio Access Networks (RAN)
  • Reinforcement learning
  • Resource allocation
  • Resource management
  • Ultra reliable low latency communication


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