Research On Resource Management Of Green Heterogeneous Cellular Networks

Given the explosive growth of mobile devices and the popularity of mobile applications,heterogeneous cellular networks(HCNs),in which the small base stations(SBSs)densely underlay a macro cell base station,hold the promise of coping with the huge mobile traffic pressure.However,developing HCNs means deploying base stations on a large scale,which leads to colossal energy consumption.Further,colossal energy consumption results in massive carbon emissions and puts mobile communication operators in the face of enormous energy consumption cost.In the context of high energy prices and the promotion of the "carbon peaking and carbon neutrality" strategy,power supply via green energy and resource management have been widely concerned as effective solutions to the energy supply side and communication demand side of HCNs,respectively.Hence,it is of great significance how to implement both solutions to improve energy efficiency further or reduce energy consumption.For energy conservation and emission reduction,this dissertation focuses on resource management problems in green HCNs.Specifically,this dissertation studies multi-objective resource management in HCNs powered by hybrid energy,online resource management in HCNs powered by a grid-connected microgrid,and multi-timescale dynamic resource management in HCNs powered by a smart grid,respectively.The main research work is as follows:(1)Focusing on the situation where multiple conflicting indicators are required to be optimized simultaneously,this dissertation studies multi-objective resource management in HCNs powered by hybrid energy.Firstly,a joint optimization problem of user association and power allocation is established with the goal of optimizing the traffic load balance among SBSs and the grid-energy efficiency simultaneously.Then,however,different situations call for different requirements for objectives,and it is difficult to know the exact weights of different objectives in advance.Three multi-objective optimization algorithms based on the gravitational search algorithm(GSA)are proposed to find a series of Pareto optimal solutions.The proposed Multi-Objective GSA considering Lévy flight and recording Global optimality(MOGSA-LG)comprehensively considers the optimization iteration of multiple objectives,the processing mode of state variable constraints,the mixed-integer nature of control variables,and the diversification and intensification of meta-heuristic algorithms.Finally,simulation experiments and performance evaluations prove that the MOGSA-LG can obtain the Pareto optimal front with better convergence,diversity,and maximum distribution boundary.(2)Given that different energy sources have different costs and the loss caused by frequent long-term energy sharing operations cannot be neglected,this dissertation studies the online resource management in HCNs powered by a grid-connected microgrid.Firstly,the joint optimization problem of admission control,power allocation,and energy sharing is established to minimize the long-term average total energy consumption cost,including the traditional grid energy cost and the renewable energy cost.Besides,the designed distance-related energy sharing loss rate model and the price factor to study the influence of renewable energy cost are applied to the studied problem.Then,the Lyapunov optimization algorithm is applied to transform the long-term average optimization problem into a real-time online one,and a cost-aware online resource management algorithm is proposed.The proposed algorithm decouples the transformed real-time online problem into two sub-problems,which are solved by linear programming and successive convex approximation.Finally,the asymptotic optimality of the proposed algorithm and the tradeoff between performance and delay are theoretically proved.The simulation results verify the theoretical analysis and effectiveness of the designed distance-related energy sharing loss rate model and reveal that the proposed algorithm can make appropriate decisions according to the unit price of renewable energy.(3)Focusing on the different time scales of renewable energy arrival and wireless channel change,as well as the varying energy price over time,this dissertation studies the multi-timescale dynamic resource management in HCNs powered by a smart grid.Firstly,a joint optimization problem of power allocation,admission control,energy sharing,and two-way energy trading is established for minimizing the long-term average energy transaction cost,where the advanced energy trading decision and the real-time joint optimization of power allocation,admission control,and energy sharing are executed in different time scales.Then,a two-timescale dynamic optimization algorithm is proposed to solve the studied problem based on a two-layer Lyapunov optimization architecture.In the proposed algorithm,the real-time joint optimization problem in small time scales can be decoupled into two sub-problems and then solved by linear programming and successive convex approximation.However,the advanced energy trading decision in large time scales requires prior information on relevant random events,which is difficult to obtain in practice.Therefore,a sub-optimal solution for advanced energy trading is obtained via a stochastic subgradient algorithm.Finally,it is theoretically proved that adjusting the control parameter can make the proposed algorithm asymptotically optimal,and there is a tradeoff between performance and delay.Simulation results verify the theoretical analysis and demonstrate that advanced energy planning can reduce energy trading cost effectively.