DQN-Based Adaptive Modulation Scheme Over Wireless Communication Channels
Donggu Lee, Young Ghyu Sun, Soo Hyun Kim, Isaac Sim, Yu Min Hwang, Yoan Shin, Dong In Kim, Jin Young Kim
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.
Topics & Concepts
Computer scienceLink adaptationReinforcement learningSignal-to-noise ratio (imaging)Spectral efficiencyWirelessModulation (music)Markov decision processMarkov processWireless networkAlgorithmChannel (broadcasting)Computer networkTelecommunicationsArtificial intelligenceMathematicsFadingStatisticsAestheticsPhilosophyAdvanced Wireless Network OptimizationAdvanced MIMO Systems OptimizationAdvanced Wireless Communication Techniques