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DQN-Based Adaptive Modulation Scheme Over Wireless Communication Channels
- Authors
- Lee, Donggu; Sun, Young Ghyu; Kim, Soo Hyun; Sim, Isaac; Hwang, Yu Min; Shin, Yoan; Kim, Dong In; Kim, Jin Young
- Issue Date
- Jun-2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- 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
- Citation
- IEEE COMMUNICATIONS LETTERS, v.24, no.6, pp.1289 - 1293
- Journal Title
- IEEE COMMUNICATIONS LETTERS
- Volume
- 24
- Number
- 6
- Start Page
- 1289
- End Page
- 1293
- ISSN
- 1089-7798
- Abstract
- 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.
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Related Researcher
- Shin, Yo an
- College of Information Technology (Department of IT Convergence)