The resource allocation for device-to-device(D2D)multicast communications is investigated.To achieve fair energy efficiency(EE)among different multicast groups,the max-min fairness criterion is used as the optimizatio...The resource allocation for device-to-device(D2D)multicast communications is investigated.To achieve fair energy efficiency(EE)among different multicast groups,the max-min fairness criterion is used as the optimization criterion and the EE of D2D multicast groups are taken as the optimization objective function.The aim is to maximize the minimum EE for different D2D multicast groups under the constraints of the maximum transmit power and minimum transmit rate,which is modeled as a non-convex and mixed-integer fractional programming problem.Here,suboptimal resource allocation algorithms are proposed to solve this problem.First,channel assignment scheme is performed to assign channel to D2D multicast groups.Second,for a given channel assignment,iterative power allocation schemes with and without loss of cellular users’rate are completed,respectively.Simulation results corroborate the convergence performance of the proposed algorithms.In addition,compared with the traditional throughput maximization algorithm,the proposed algorithms can improve the energy efficiency of the system and the fairness achieved among different multicast groups.展开更多
The energy efficiency（EE） for the full-duplex massive multi-input multi-output（MIMO） system is investigated. Given the transmit powers of both the uplink and the downlink, the closed-form solutions of the optimal ...The energy efficiency（EE） for the full-duplex massive multi-input multi-output（MIMO） system is investigated. Given the transmit powers of both the uplink and the downlink, the closed-form solutions of the optimal number of antennas and the maximum EE are achieved in the high regime of the signal-to-noise ratio（SNR）. It is shown that the optimal number of antennas and the maximum EE gets larger with the increase in user numbers. To further improve the EE, an optimization algorithm with low complexity is proposed to jointly determine the number of antennas and the transmit powers of both the uplink and the downlink. It is shown that, the proposed algorithm can achieve the system performance very close to the exhaustive search.展开更多
To reduce carbon intensity, an improved management method balancing the reduction in costs and greenhouse gas(GHG)emissions is required for Tianjin's waste management system. Firstly, six objective functions, name...To reduce carbon intensity, an improved management method balancing the reduction in costs and greenhouse gas(GHG)emissions is required for Tianjin's waste management system. Firstly, six objective functions, namely, cost minimization, GHG minimization, eco-efficiency minimization, cost maximization, GHG maximization and eco-efficiency maximization, are built and subjected to the same constraints with each objective function corresponding to one scenario. Secondly, GHG emissions and costs are derived from the waste flow of each scenario. Thirdly, the range of GHG emissions and costs of other potential scenarios are obtained and plotted through adjusting waste flow with infinitely possible step sizes according to the correlation among the above six scenarios. And the optimal scenario is determined based on this range. The results suggest the following conclusions. 1) The scenarios located on the border between scenario cost minimization and GHG minimization create an optimum curve, and scenario GHG minimization has the smallest eco-efficiency on the curve; 2) Simple pursuit of eco-efficiency minimization using fractional programming may be unreasonable; 3) Balancing GHG emissions from incineration and landfills benefits Tianjin's waste management system as it reduces GHG emissions and costs.展开更多
基金Projects(61801237,61701255)supported by the National Natural Science Foundation of ChinaProject(SBH17024)supported by the Postdoctoral Science Foundation of Jiangsu Province,China+2 种基金Project(15KJB510026)supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions,ChinaProject(BK20150866)supported by the Natural Science Foundation of Jiangsu Province,ChinaProjects(NY215046,NY217056)supported by the Introduction of Talent Fund of Nanjing University of Posts and Telecommunications,China
文摘The resource allocation for device-to-device(D2D)multicast communications is investigated.To achieve fair energy efficiency(EE)among different multicast groups,the max-min fairness criterion is used as the optimization criterion and the EE of D2D multicast groups are taken as the optimization objective function.The aim is to maximize the minimum EE for different D2D multicast groups under the constraints of the maximum transmit power and minimum transmit rate,which is modeled as a non-convex and mixed-integer fractional programming problem.Here,suboptimal resource allocation algorithms are proposed to solve this problem.First,channel assignment scheme is performed to assign channel to D2D multicast groups.Second,for a given channel assignment,iterative power allocation schemes with and without loss of cellular users’rate are completed,respectively.Simulation results corroborate the convergence performance of the proposed algorithms.In addition,compared with the traditional throughput maximization algorithm,the proposed algorithms can improve the energy efficiency of the system and the fairness achieved among different multicast groups.
基金supported by the National Natural Science Foundation of China(61371188)the Research Fund for the Doctoral Program of Higher Education(20130131110029)+2 种基金the Open Fund of State Key Laboratory of Integrated Services Networks(ISN14-03)the China Postdoctoral Science Foundation(2014M560553)the Special Funds for Postdoctoral Innovative Projects of Shandong Province(201401013)
文摘The energy efficiency（EE） for the full-duplex massive multi-input multi-output（MIMO） system is investigated. Given the transmit powers of both the uplink and the downlink, the closed-form solutions of the optimal number of antennas and the maximum EE are achieved in the high regime of the signal-to-noise ratio（SNR）. It is shown that the optimal number of antennas and the maximum EE gets larger with the increase in user numbers. To further improve the EE, an optimization algorithm with low complexity is proposed to jointly determine the number of antennas and the transmit powers of both the uplink and the downlink. It is shown that, the proposed algorithm can achieve the system performance very close to the exhaustive search.
基金Project(51406133) supported by the National Natural Science Foundation of ChinaProject supported by the Scientific Research Foundation for the Returned Overseas,ChinaProject supported by Independent Innovation Fund of Tianjin University,China
文摘To reduce carbon intensity, an improved management method balancing the reduction in costs and greenhouse gas(GHG)emissions is required for Tianjin's waste management system. Firstly, six objective functions, namely, cost minimization, GHG minimization, eco-efficiency minimization, cost maximization, GHG maximization and eco-efficiency maximization, are built and subjected to the same constraints with each objective function corresponding to one scenario. Secondly, GHG emissions and costs are derived from the waste flow of each scenario. Thirdly, the range of GHG emissions and costs of other potential scenarios are obtained and plotted through adjusting waste flow with infinitely possible step sizes according to the correlation among the above six scenarios. And the optimal scenario is determined based on this range. The results suggest the following conclusions. 1) The scenarios located on the border between scenario cost minimization and GHG minimization create an optimum curve, and scenario GHG minimization has the smallest eco-efficiency on the curve; 2) Simple pursuit of eco-efficiency minimization using fractional programming may be unreasonable; 3) Balancing GHG emissions from incineration and landfills benefits Tianjin's waste management system as it reduces GHG emissions and costs.
