In this study,a solution based on deep Q network(DQN)is proposed to address the relay selection problem in cooperative non-orthogonal multiple access(NOMA)systems.DQN is particularly effective in addressing problems w...In this study,a solution based on deep Q network(DQN)is proposed to address the relay selection problem in cooperative non-orthogonal multiple access(NOMA)systems.DQN is particularly effective in addressing problems within dynamic and complex communication environ-ments.By formulating the relay selection problem as a Markov decision process(MDP),the DQN algorithm employs deep neural networks(DNNs)to learn and make decisions through real-time interactions with the communication environment,aiming to minimize the system’s outage proba-bility.During the learning process,the DQN algorithm progressively acquires channel state infor-mation(CSI)between two nodes,thereby minimizing the system’s outage probability until a sta-ble level is reached.Simulation results show that the proposed method effectively reduces the out-age probability by 82%compared to the two-way relay selection scheme(Two-Way)when the sig-nal-to-noise ratio(SNR)is 30 dB.This study demonstrates the applicability and advantages of the DQN algorithm in cooperative NOMA systems,providing a novel approach to addressing real-time relay selection challenges in dynamic communication environments.展开更多
基金supported by the National Natural Science Foundation of China(Nos.61841107 and 62061024)Gansu Natural Sci-ence Foundation(Nos.22JR5RA274 and 23YFGA0062)Gansu Innovation Foundation(No.2022A-215).
文摘In this study,a solution based on deep Q network(DQN)is proposed to address the relay selection problem in cooperative non-orthogonal multiple access(NOMA)systems.DQN is particularly effective in addressing problems within dynamic and complex communication environ-ments.By formulating the relay selection problem as a Markov decision process(MDP),the DQN algorithm employs deep neural networks(DNNs)to learn and make decisions through real-time interactions with the communication environment,aiming to minimize the system’s outage proba-bility.During the learning process,the DQN algorithm progressively acquires channel state infor-mation(CSI)between two nodes,thereby minimizing the system’s outage probability until a sta-ble level is reached.Simulation results show that the proposed method effectively reduces the out-age probability by 82%compared to the two-way relay selection scheme(Two-Way)when the sig-nal-to-noise ratio(SNR)is 30 dB.This study demonstrates the applicability and advantages of the DQN algorithm in cooperative NOMA systems,providing a novel approach to addressing real-time relay selection challenges in dynamic communication environments.
