Multicast Beamforming In Ultra-dense Networks With Wireless Backhaul

With the rapid development of mobile internet,a large amount of always on devices are accessing to current mobile network,which demand much more traffic in the wireless network.In order to support a 1000-fold increase in traffic demand,researchers working on the emerging fifth generation?5G?wireless system proposed the ultra-dense networks?UDNs?concept to be the key enabling technologies.By deploying a large number of lower power and low-cost ac-cess points?APs?across the networking area,which are connected to the base station?BS?via wired/wireless backhaul links,UDNs offer a cost-effective alternative to cell splitting in conven-tional cellular networks.The density of APs in the UDN will potentially reach,or even exceed,the density of users,which greatly reduces the inter-cell distances and cause severe inter-cell in-terference.By adopting advanced interference cancellation and avoidance techniques,inter-cell interference can be controlled under a very low level.Our research work base on the UDN net-work,and consideres the inter-cell interference cancellation and beamformer design in access links and resource allocation in backhaul links.Firstly,we formulate a joint optimization prob-lem of multicast beamformer design in access links and resource allocation in backhaul links,which is a non-convex mixed integer programming problem.This problem is transformed to a tractable form using the successive convex approximation and re-weighted l₁-norm techniques,which is solved using an iterative algorithm.To futher explore the efficiency of this network,we conduct the study on deploying cache on the AP side to improve the efficiency of the network system.In this situation,we formulate the system model as minimizing the serving time,and decompose such problem into two subproblems corresponding to the access link and the back-haul link.The problem on the backhaul link can be solved through linear programming and problem in access link can be solved through convex approximation and re-weighted l₁-norm techniques.Finally,we raise an two-tier iterative algorithm on solving this problem and test the algorithm in comprehensive network settings.