.png){kind=icon}
返回
Multi-Agent Reinforcement Learning Based Distributed Transmission in Collaborative Cloud-Edge Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2021.3055511.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Millimeter-wave (mmWave) cloud-edge collaboration has emerged as an effective solution for low-latency transmission by harnessing the potentials of cloud and edge nodes (eNodes) as well as abundant bandwidth. In a mmWave dense network, centralized processing and coordinated multi-points processing require large amounts of signaling exchange overhead, which in turn increase the latency. As such, distributed mechanism is demanded. In this paper, we investigate joint user scheduling and beam selection strategies for eNodes and formulate the problem as a constrained Markov decision process. The objective and constraints are long-term average network delay cost minimization and instantaneous quality of service (QoS) guarantee for each user equipment. To update the distributed strategies, we apply the multi-agent reinforcement learning technique and propose a hybrid learning framework by extending the actor-critic model, where centralized training and decentralized execution are implemented. We utilize the Lagrange technique to design the reward functions. Additionally, a novel actor network architecture and an exploration scheme are proposed. Simulation results validate the effectiveness of the proposed intelligent distributed algorithm with a high degree of scalability, and show superior performance in terms of long-term average network delay cost and QoS satisfaction rates compared with other methods.
Keyword:
Collaboration
Cloud computing
Interference
Array signal processing
Quality of service
Delays
Wireless communication
Cloud-edge collaboration
user scheduling
beam selection
distributed algorithm
multi-agent reinforcement learning (MARL)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
引用论文
Multicell Coordination via Joint Scheduling, Beamforming, and Power Spectrum Adaptation通过联合调度，波束成形和功率谱自适应进行多小区协调
OAAI
OAAI分享收藏On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming关于使用迫零波束成形的多天线广播调度的最优性
PREAI
分享收藏Distributed Interference-Aware Power Control in Ultra-Dense Small Cell Networks: A Robust Mean Field Game.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容