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QoE-Driven Antenna Tuning in Cellular Networks With Cooperative Multi-Agent Reinforcement Learning

文献类型：期刊论文

作者Liu, Xuewen5; Chuai, Gang4; Wang, Xin3; Xu, Zhiwei2; Gao, Weidong4; Zhang, Kaisa4; Liu, Qian1; Maimaiti, Saidiwaerdi4; Zuo, Peiliang5
刊名IEEE TRANSACTIONS ON MOBILE COMPUTING
出版日期2024-02-01
卷号23期号:2页码:1186-1199
关键词Antenna tuning multi-goal MARL QoE/QoS mapping self-optimization
ISSN号1536-1233
DOI10.1109/TMC.2022.3230711
英文摘要Antenna tuning plays an essential role in ensuring high quality wireless communications. Targeting for higher Quality of Service (QoS), many existing network antenna tuning schemes are based on expert knowledge, rule-based policies or conventional optimization theory. However, maximizing the traffic-related QoS does not guarantee that all customers experience good services. In addition, existing schemes are often limited to some handcrafted rules or heuristics and lack of adaptability especially in a time-varying environment. Quality of Experience (QoE), a user-centric metric, can better measure users' satisfaction for services in wireless networks. This article proposes the cooperative tuning of antennas based on QoE, a paradigm shift from network-centric QoS to user-centric QoE domain. In a normal cellular network, besides the need of improving the overall QoE, it requires handling faults from different cells. As Multi-agent Reinforcement Learning (MARL) has the capability of self-learning the dynamics of environment, we propose an antenna configuration algorithm based on multi-goal MARL. In our framework, agents from different cells not only need to cooperate with each other to achieve the global goal of increasing the overall QoE of the wireless network but also complete some personal goals by combating the faults encountered in their own cells. To accelerate the training efficiency, we introduce a novel two-stage curriculum learning. To reduce the collection time of each QoE sample, we develop an accurate and timely QoE/QoS mapping model with the cascading of a Random Forest Classifier (RFC) and a Deep Neural Network (DNN) (abbreviated as RFC-DNN), which can help us obtain QoE by collecting QoS measurements and perform QoE-based antenna configurations with smaller time granularity. Our proposed RFC-DNN model can reduce the time by 70% when predicting the QoE of a single sample. A huge amount of time will be saved in MARL when tens of thousands of transitions/samples need to be collected. The performance results show that our proposed antenna tuning schemes can not only address specific faults in each cell, but also significantly improve the global average QoE with a faster and more stable convergence speed.
资助项目National Key Research and Development Project of China
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:001140706700043
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/38385]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Kaisa; Zuo, Peiliang
作者单位1.Chongqing Univ Posts & Telecommun, Sch Commun & Informat Engn, Chongqing 400065, Peoples R China
2.Chinese Acad Sci, Inst Comp, Beijing 100080, Peoples R China
3.SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY 11794 USA
4.Beijing Univ Posts & Telecommun, Dept Informat & Commun Engn, Beijing 100876, Peoples R China
5.Beijing Elect Sci & Technol Inst, Dept Elect & Commun Engn, Beijing 100070, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Xuewen,Chuai, Gang,Wang, Xin,et al. QoE-Driven Antenna Tuning in Cellular Networks With Cooperative Multi-Agent Reinforcement Learning[J]. IEEE TRANSACTIONS ON MOBILE COMPUTING,2024,23(2):1186-1199.
APA Liu, Xuewen.,Chuai, Gang.,Wang, Xin.,Xu, Zhiwei.,Gao, Weidong.,...&Zuo, Peiliang.(2024).QoE-Driven Antenna Tuning in Cellular Networks With Cooperative Multi-Agent Reinforcement Learning.IEEE TRANSACTIONS ON MOBILE COMPUTING,23(2),1186-1199.
MLA Liu, Xuewen,et al."QoE-Driven Antenna Tuning in Cellular Networks With Cooperative Multi-Agent Reinforcement Learning".IEEE TRANSACTIONS ON MOBILE COMPUTING 23.2(2024):1186-1199.

入库方式： OAI收割

来源：计算技术研究所

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