The existing literature on device-to-device(D2D)architecture suffers from a dearth of analysis under imperfect channel conditions.There is a need for rigorous analyses on the policy improvement and evaluation of netwo...The existing literature on device-to-device(D2D)architecture suffers from a dearth of analysis under imperfect channel conditions.There is a need for rigorous analyses on the policy improvement and evaluation of network performance.Accordingly,a two-stage transmit power control approach(named QSPCA)is proposed:First,a reinforcement Q-learning based power control technique and;second,a supervised learning based support vector machine(SVM)model.This model replaces the unified communication model of the conventional D2D setup with a distributed one,thereby requiring lower resources,such as D2D throughput,transmit power,and signal-to-interference-plus-noise ratio as compared to existing algorithms.Results confirm that the QSPCA technique is better than existing models by at least 15.31%and 19.5%in terms of throughput as compared to SVM and Q-learning techniques,respectively.The customizability of the QSPCA technique opens up multiple avenues and industrial communication technologies in 5G networks,such as factory automation.展开更多
文摘The existing literature on device-to-device(D2D)architecture suffers from a dearth of analysis under imperfect channel conditions.There is a need for rigorous analyses on the policy improvement and evaluation of network performance.Accordingly,a two-stage transmit power control approach(named QSPCA)is proposed:First,a reinforcement Q-learning based power control technique and;second,a supervised learning based support vector machine(SVM)model.This model replaces the unified communication model of the conventional D2D setup with a distributed one,thereby requiring lower resources,such as D2D throughput,transmit power,and signal-to-interference-plus-noise ratio as compared to existing algorithms.Results confirm that the QSPCA technique is better than existing models by at least 15.31%and 19.5%in terms of throughput as compared to SVM and Q-learning techniques,respectively.The customizability of the QSPCA technique opens up multiple avenues and industrial communication technologies in 5G networks,such as factory automation.
