Joint User Scheduling and Beam Selection in mmWave Networks Based on Multi-Agent Reinforcement Learning
ID:171 View Protection:ATTENDEE Updated Time:2020-08-05 10:17:28 Hits:483 Oral Presentation

Start Time:2020-06-08 14:00(Asia/Shanghai)


Session:S Special Session » SS08Intelligent Antenna Arrays And Surfaces For Future Communications

Video No Permission

Tips: Only the registered participant can access the file. Please sign in first.

In this paper, we consider a multi-cell downlink mmWave communication network, where the base stations (BS) are assumed to be incapable of synchronously accommodating service requests from all users. The objective is to develop the joint user scheduling and beam selection strategy that minimizes the long-term average delay cost while satisfying the instantaneous quality of service constraint of each user. To achieve the long-term performance, we propose a distributed algorithm to develop the joint strategy based on multi-agent reinforcement learning. Simulation results show that the proposed intelligent distributed algorithm can learn from the dynamic environment and enhance the long-term network performance.
Chunmei Xu
Southeast University, China

Submission Author
Chunmei Xu Southeast University, China
Shengheng Liu Southeast University & Purple Mountain Laboratories, China
Cheng Zhang Southeast University, China
Yongming Huang Southeast University, China
Luxi Yang Southeast University, China
Submit Comment
Verify Code Change Another
All Comments
Important Date
  • Conference Date

    Jun 08



    Jun 11


  • Jan 12 2020

    Draft paper submission deadline

  • Apr 15 2020

    Early Bird Registration

  • Dec 31 2020

    Registration deadline

Sponsored By
IEEE Signal Processing Society
Contact Information