As the rapid development of wireless communication networks has resulted in better user experiences,the spectrum resources occupied and energy consumption have increased considerably and resulted in great costs.To add...As the rapid development of wireless communication networks has resulted in better user experiences,the spectrum resources occupied and energy consumption have increased considerably and resulted in great costs.To address the energy consumption and cost problems of spectrum sharing in cognitive radio networks,a hybrid spectrum sharing model combining the free spectrum of authorized users and the leased spectrum of mobile network operators is given.Based on the hybrid model,a function of throughput and costs,including energy consumption and transaction costs,is constructed,and a joint utility optimization problem is analyzed.The transactions between secondary users and primary users are performed on the consortium blockchain on which users can directly trade spectrum and the transaction information is recorded.In order to improve the joint utility,the Lagrange multiplier method is used to achieve the optimal solution for the sensing time,the number of secondary users involved in sensing,and the transmission power.The simulation results show that the joint utility optimization algorithm proposed in this paper can achieve higher joint utility under the constraints of the minimum throughput requirement and maximum transmission power.展开更多
It is essential to maximize capacity while satisfying the transmission time delay of unmanned aerial vehicle(UAV)swarm communication system.In order to address this challenge,a dynamic decentralized optimization mecha...It is essential to maximize capacity while satisfying the transmission time delay of unmanned aerial vehicle(UAV)swarm communication system.In order to address this challenge,a dynamic decentralized optimization mechanism is presented for the realization of joint spectrum and power(JSAP)resource allocation based on deep Q-learning networks(DQNs).Each UAV to UAV(U2U)link is regarded as an agent that is capable of identifying the optimal spectrum and power to communicate with one another.The convolutional neural network,target network,and experience replay are adopted while training.The findings of the simulation indicate that the proposed method has the potential to improve both communication capacity and probability of successful data transmission when compared with random centralized assignment and multichannel access methods.展开更多
基金Supported by the National Natural Science Foundation of China(No.62071002)。
文摘As the rapid development of wireless communication networks has resulted in better user experiences,the spectrum resources occupied and energy consumption have increased considerably and resulted in great costs.To address the energy consumption and cost problems of spectrum sharing in cognitive radio networks,a hybrid spectrum sharing model combining the free spectrum of authorized users and the leased spectrum of mobile network operators is given.Based on the hybrid model,a function of throughput and costs,including energy consumption and transaction costs,is constructed,and a joint utility optimization problem is analyzed.The transactions between secondary users and primary users are performed on the consortium blockchain on which users can directly trade spectrum and the transaction information is recorded.In order to improve the joint utility,the Lagrange multiplier method is used to achieve the optimal solution for the sensing time,the number of secondary users involved in sensing,and the transmission power.The simulation results show that the joint utility optimization algorithm proposed in this paper can achieve higher joint utility under the constraints of the minimum throughput requirement and maximum transmission power.
基金supported by the National Natural Science Foundation of China(62031017,61971221).
文摘It is essential to maximize capacity while satisfying the transmission time delay of unmanned aerial vehicle(UAV)swarm communication system.In order to address this challenge,a dynamic decentralized optimization mechanism is presented for the realization of joint spectrum and power(JSAP)resource allocation based on deep Q-learning networks(DQNs).Each UAV to UAV(U2U)link is regarded as an agent that is capable of identifying the optimal spectrum and power to communicate with one another.The convolutional neural network,target network,and experience replay are adopted while training.The findings of the simulation indicate that the proposed method has the potential to improve both communication capacity and probability of successful data transmission when compared with random centralized assignment and multichannel access methods.
