An orderly competition mechanism is used to change unexpected competition into predictable competition so as to reduce access collision during access process.The scheme is realized by learning,queuing,and accessing.Qu...An orderly competition mechanism is used to change unexpected competition into predictable competition so as to reduce access collision during access process.The scheme is realized by learning,queuing,and accessing.Queuing is the key step to reduce random and realize orderly competition.Related parameters leading to access random including the arrival rate,the delay requirements,the number of devices,and so on,are defined as queue factors in this paper.The queue factors are obtained from the improved double deep Q network(DDQN)algorithm which is proposed here by setting asynchronous weights of two target networks.By learning,the queue factors will guide the devices with diverse delay requirements to queue.Then the queued devices start the access process according to their learning optimal access slot and preamble.Different from traditional competition solutions,markov decision process of the orderly competition mechanism has only two states,which remarkably cuts down the back-off rate and reduces the access delay.The simulation results show that the access success rate of this method can be close to 100%before the system capacity approaches the maximum value.展开更多
基金supported by Key Technologies R&D Program of Jiangsu(Prospective and Key Technologies for Industry)(BE2022067,BE2022067-1 and BE2022067-2)National Natural Science Foundation of China(No.61771255)Provincial and Ministerial Key Laboratory Open Project under Grant No.20190904.
文摘An orderly competition mechanism is used to change unexpected competition into predictable competition so as to reduce access collision during access process.The scheme is realized by learning,queuing,and accessing.Queuing is the key step to reduce random and realize orderly competition.Related parameters leading to access random including the arrival rate,the delay requirements,the number of devices,and so on,are defined as queue factors in this paper.The queue factors are obtained from the improved double deep Q network(DDQN)algorithm which is proposed here by setting asynchronous weights of two target networks.By learning,the queue factors will guide the devices with diverse delay requirements to queue.Then the queued devices start the access process according to their learning optimal access slot and preamble.Different from traditional competition solutions,markov decision process of the orderly competition mechanism has only two states,which remarkably cuts down the back-off rate and reduces the access delay.The simulation results show that the access success rate of this method can be close to 100%before the system capacity approaches the maximum value.
