Energy-efficient Networking,User Association And Resource Allocation Algorithms In Dense Wireless Networks

Wireless communication networks are gradually becoming denser by deploying various kinds of small base stations or antennas;meanwhile,high-energy efficiency(EE)is a foundamental requirement of this development.This thesis investigates from three aspects,i.e.,wireless networking,user association and resource allocation,on how to improve the system EE.Three questions will be answered,1)how to configure the base station(BS)density;2)how to associate users and BSs;3)How to allocate resources based on energy harvesting technology.The BS density configuration is a key factor to improve energy efficiency(EE)in cellular networks,especially when sleeping strategies are adopted to reduce energy consumption.In this thesis,the optimization of BS density for enhancing EE through traffic-aware sleeping strategies in both one-tier and two-tier cellular networks is researched,where BS locations are modeled as a Poisson point process.In the one-tier scenario,the EE optimization objective function is proved to be quasi-concave,and the optimal BS density solution is derived,where the maximum achievable EE is demonstrated to be independent of the user density.In the two-tier scenario,both the activation probability of BSs and the coverage probability are analyzed,where the EE optimization objective function is proved to be not necessarily quasi-concave.To handle this non-convex issue,an equivalent optimization problem that jointly optimizes the ratio and weighted sum of BS densities is proposed,and solved by a dynamic gradient iterative algorithm.Simulation results verify the relevant derivations,and reveal that the sleeping strategy can not only save energy,but also improve the quality of radio links.Redesigning user association strategies to improve energy efficiency(EE)has been viewed as one of promising shifting paradigms for the dense wireless cellular networks.In this thesis,we investigate how to optimize users’ association to enhance EE for hyper-dense heterogeneous networking in the 5G cellular networks,where the low-power node(LPN)(e.g.,small cell base station)much outnumbers the high-power node(HPN)(e.g.,macro base station).To characterize that densely deployed LPNs would undertake a majority of high-rate services,while HPNs mainly support coverage,the EE metric is defined as average weighted EE of access nodes with the unit of bit per joule.Then,the EE optimization objective function is formulated to optimize the association,which is proved to be neither convex nor quasiconvex.To solve the non-convex EE optimization problem,two mathematical transformation techniques are presented.In the first case,the original problem is reformulated as an equivalent problem involving the maximization of a biconcave function.In the second case,the non-convex problem is equivalent to a concave minimization problem.Furthermore,the relationship between two reformulated problems is presented.We focus on the solution of the biconcave framework,and by exploiting the biconcave structure,a novel iterative algorithm based on dual theory is proposed,where a partially optimal solution can be achieved.Simulation results have verified the effectiveness of the above algorithm.Energy harvesting is necessary to make the wireless network self-sustaining and self-organizing regardless of the traditional power grid.How to allocate the limited radio resources in the energy harvesting wireless network is a challenging work.This thesis focuses on optimizing the time and power related resources in the multi-cell scenario to maximize the throughput constraining of the changeable energy in the base station.The optimal off-line resource allocation strategy is put forward,based on analysis of structural properties of the optimal total power sequences.To decrease the complexity of the optimal solution,a low complexity suboptimal off-line algorithm is presented based on the nature of the concave function.Furthermore,inspired by the off-line algorithm,an on-line resource allocation algorithm is proposed as well.Simulation results show the suboptimal offline algorithm closely tracks the performance of the optimal solution,and the on-line algorithm also has brilliant performance compared with several kinds of algorithms under different system settings.