Multi-Agent Q-Learning for Power Allocation in Interference Channel

被引：3

作者：

Wongphatcharatham, Tanutsorn ^{[1
]}

Phakphisut, Watid ^{[1
]}

Wijitpornchai, Thongchai ^{[2
]}

Areeprayoonkij, Poonlarp ^{[2
]}

Jaruvitayakovit, Tanun ^{[2
]}

Hannanta-Anan, Pimkhuan ^{[1
]}

机构：

[1] King Mongkuts Inst Technol Ladkrabang, Sch Engn, Bangkok, Thailand

[2] Adv Wireless Network Co Ltd, Bangkok, Thailand

来源：

2022 37TH INTERNATIONAL TECHNICAL CONFERENCE ON CIRCUITS/SYSTEMS, COMPUTERS AND COMMUNICATIONS (ITC-CSCC 2022) | 2022年

关键词：

Multi-Agent; Reinforcement Learning; Power Allocation; Wireless Networks;

D O I：

10.1109/ITC-CSCC55581.2022.9894852

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Signal transmission in wireless networks suffers from unwanted interference. To maximize signal to interference plus noise ratio, transmit power of each transmitter needs to be optimally allocated. Here, we propose to use multi-agent Q-learning to optimize such transmit power within interference channel. Our simulation indicated that multi-agent Q-learning resulted in better sum-rate than the traditional methods such as the maximum power allocation and the random power allocation. Our work offers a novel and practical computational approach to optimizing signal transmission in wireless networks.

引用

页码：876 / 879

页数：4

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