Non-orthogonal multiple access is a promising technique to meet the harsh requirements for the internet of things devices in cognitive radio networks.To improve the energy efficiency(EE)of the unlicensed secondary use...Non-orthogonal multiple access is a promising technique to meet the harsh requirements for the internet of things devices in cognitive radio networks.To improve the energy efficiency(EE)of the unlicensed secondary users(SU),a power allocation(PA)algorithm with polynomial complexity is investigated.We first establish the feasible range of power consumption ratio using Karush-Kuhn-Tucker optimality conditions to support each SU’s minimum quality of service and the effectiveness of successive interference cancellation.Then,we formulate the EE optimization problem considering the total transmit power requirements which leads to a non-convex fractional programming problem.To efficiently solve the problem,we divide it into an inner-layer and outer-layer optimization sub-problems.The inner-layer optimization which is formulated to maximize the sub-carrier PA coefficients can be transformed into the difference of convex programming by using the first-order Taylor expansion.Based on the solution of the inner-layer optimization sub-problem,the concave-convex fractional programming problem of the outer-layer optimization sub-problem may be converted into the Lagrangian relaxation model employing the Dinkelbach algorithm.Simulation results demonstrate that the proposed algorithm has a faster convergence speed than the simulated annealing algorithm,while the average system EE loss is only less than 2%.展开更多
基金supported in part by the Science and Technology Research Program of the National Science Foundation of China(No.61671096)Chongqing Research Program of Basic Science and Frontier Technology(No.cstc2017jcyj BX0005)+1 种基金Chongqing Municipal Education Commission(No.KJQN201800642)Doctoral Student Training Program(No.BYJS2016009)。
文摘Non-orthogonal multiple access is a promising technique to meet the harsh requirements for the internet of things devices in cognitive radio networks.To improve the energy efficiency(EE)of the unlicensed secondary users(SU),a power allocation(PA)algorithm with polynomial complexity is investigated.We first establish the feasible range of power consumption ratio using Karush-Kuhn-Tucker optimality conditions to support each SU’s minimum quality of service and the effectiveness of successive interference cancellation.Then,we formulate the EE optimization problem considering the total transmit power requirements which leads to a non-convex fractional programming problem.To efficiently solve the problem,we divide it into an inner-layer and outer-layer optimization sub-problems.The inner-layer optimization which is formulated to maximize the sub-carrier PA coefficients can be transformed into the difference of convex programming by using the first-order Taylor expansion.Based on the solution of the inner-layer optimization sub-problem,the concave-convex fractional programming problem of the outer-layer optimization sub-problem may be converted into the Lagrangian relaxation model employing the Dinkelbach algorithm.Simulation results demonstrate that the proposed algorithm has a faster convergence speed than the simulated annealing algorithm,while the average system EE loss is only less than 2%.
