DQN-Based Adaptive Modulation Scheme Over Wireless Communication Channels

被引：21

作者：

Lee, Donggu ^{[1
]}

Sun, Young Ghyu ^{[1
]}

Kim, Soo Hyun ^{[1
]}

Sim, Isaac ^{[1
]}

Hwang, Yu Min ^{[2
]}

Shin, Yoan ^{[3
]}

Kim, Dong In ^{[4
]}

Kim, Jin Young ^{[1
]}

机构：

[1] Kwangwoon Univ, Dept Elect Convergence Engn, Seoul 01897, South Korea

[2] Western Univ, Dept Elect & Comp Engn, London, ON N6A 5B9, Canada

[3] Soongsil Univ, Sch Elect Engn, Seoul 06978, South Korea

[4] Sungkyunkwan Univ, Dept Elect & Comp Engn, Suwon 16419, South Korea

来源：

IEEE COMMUNICATIONS LETTERS | 2020年 / 24卷 / 06期

基金：

新加坡国家研究基金会;

关键词：

Modulation; Signal to noise ratio; Learning (artificial intelligence); Adaptation models; Wireless communication; Adaptive systems; Neural networks; Adaptive modulation; deep learning; deep Q network; reinforcement learning;

D O I：

10.1109/LCOMM.2020.2978390

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

In this letter, to improve data rate over wireless communication channels, we propose a deep Q network (DQN)-based adaptive modulation scheme by using Markov decision process (MDP) model. The proposed algorithm makes the reinforcement learning agent to select rate region boundaries as the states, which divide signal-to-noise ratio (SNR) range into rate regions. The simulation results show that spectral efficiency can be improved on the average by 0.5395 bps/Hz in wide SNR range.

引用

页码：1289 / 1293

页数：5

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