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)

Duration:20min

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

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Abstract
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.
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Speaker
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
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    Jun 08

    2020

    to

    Jun 11

    2020

  • Jan 12 2020

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  • Apr 15 2020

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