Non-orthogonal multiple access(NOMA)is one of the key 5G technology which can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner.NOMA allows m...Non-orthogonal multiple access(NOMA)is one of the key 5G technology which can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner.NOMA allows multiple terminals to share the same resource unit at the same time.The receiver usually needs to configure successive interference cancellation(SIC).The receiver eliminates co-channel interference(CCI)between users and it can significantly improve the system throughput.In order to meet the demands of users and improve fairness among them,this paper proposes a new power allocation scheme.The objective is to maximize user fairness by deploying the least fairness in multiplexed users.However,the objective function obtained is non-convex which is converted into convex form by utilizing the optimal Karush-Kuhn-Tucker(KKT)constraints.Simulation results show that the proposed power allocation scheme gives better performance than the existing schemes which indicates the effectiveness of the proposed scheme.展开更多
In order to improve the energy efficiency(EE)in the underlay cognitive radio(CR)networks,a power allocation strategy based on an actor-critic reinforcement learning is proposed,where a cluster of cognitive users(CUs)c...In order to improve the energy efficiency(EE)in the underlay cognitive radio(CR)networks,a power allocation strategy based on an actor-critic reinforcement learning is proposed,where a cluster of cognitive users(CUs)can simultaneously access to the same primary spectrum band under the interference constraints of the primary user(PU),by employing the non-orthogonal multiple access(NOMA)technique.In the proposed scheme,the optimization of the power allocation is formulated as a non-convex optimization problem.Additionally,the power allocation for different CUs is based on the actor-critic reinforcement learning model,in which the weighted data rate is set as the reward function,and the generated action strategy(i.e.the power allocation)is iteratively criticized and updated.Both the CU’s spectral efficiency and the PU’s interference constrains are considered in the training of the actor-critic reinforcement learning.Furthermore,the first order Taylor approximation as well as other manipulations are adopted to solve the power allocation optimization problem for the sake of considering the conventional channel conditions.According to the simulation results,we find that our scheme could achieve a higher spectral efficiency for the CUs compared to a benchmark scheme without learning process as well as the existing Q-learning based method,while the resultant interference affecting the PU transmission can be maintained at a given tolerated limit.展开更多
文摘Non-orthogonal multiple access(NOMA)is one of the key 5G technology which can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner.NOMA allows multiple terminals to share the same resource unit at the same time.The receiver usually needs to configure successive interference cancellation(SIC).The receiver eliminates co-channel interference(CCI)between users and it can significantly improve the system throughput.In order to meet the demands of users and improve fairness among them,this paper proposes a new power allocation scheme.The objective is to maximize user fairness by deploying the least fairness in multiplexed users.However,the objective function obtained is non-convex which is converted into convex form by utilizing the optimal Karush-Kuhn-Tucker(KKT)constraints.Simulation results show that the proposed power allocation scheme gives better performance than the existing schemes which indicates the effectiveness of the proposed scheme.
基金The work was supported by the Fundamental Research Funds for the Central Universities Grant3102018QD096in part by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2019JQ-075 and Grant 2019JQ-253,and in part by the National Natural Science Foundation of China under Grant 61901379,Grant 61901327,Grant 61825104 and Grant 61631015.
文摘In order to improve the energy efficiency(EE)in the underlay cognitive radio(CR)networks,a power allocation strategy based on an actor-critic reinforcement learning is proposed,where a cluster of cognitive users(CUs)can simultaneously access to the same primary spectrum band under the interference constraints of the primary user(PU),by employing the non-orthogonal multiple access(NOMA)technique.In the proposed scheme,the optimization of the power allocation is formulated as a non-convex optimization problem.Additionally,the power allocation for different CUs is based on the actor-critic reinforcement learning model,in which the weighted data rate is set as the reward function,and the generated action strategy(i.e.the power allocation)is iteratively criticized and updated.Both the CU’s spectral efficiency and the PU’s interference constrains are considered in the training of the actor-critic reinforcement learning.Furthermore,the first order Taylor approximation as well as other manipulations are adopted to solve the power allocation optimization problem for the sake of considering the conventional channel conditions.According to the simulation results,we find that our scheme could achieve a higher spectral efficiency for the CUs compared to a benchmark scheme without learning process as well as the existing Q-learning based method,while the resultant interference affecting the PU transmission can be maintained at a given tolerated limit.
