Resource Allocation Based Interference Management Of Wireless Networks

With the rapid popularization of various smart devices and the booming development of social informatization,user's demand for wireless communication networks are increasing,and various application scenarios are emerging.In order to cope with the massive connections of smart devices and the explosive growth of data traffic,ultra-dense heterogeneous wireless networks,as the one of key technology of the fifth generation of mobile communication,have attracted widespread attention from international researchers.The ultra-dense heterogeneous wireless networks reduce the distance between users and base stations by densely deploying a large number of base stations with different coverage ranges in space,and substantially increase the spectrum efficiency and system capacity.However,the available subchannel resources in the network are always extremely limited compared to the users' demands,and it is impossible to allocate orthogonal sub-channels to all users in the network.The way of spectrum multiplexing will cause serious interference problems for ultra-dense heterogeneous wireless networks,which greatly decreases the system performance.Therefore,the interference management problem in dense heterogeneous wireless networks becomes particularly important.Interference alignment(IA),as an efficient interference management method,by jointly designing the precoding matrix at each transmitter and the decoding matrix at the receiver to compress the interfering signals received at each receiver into the lower dimensional subspace as much as possible,so that the subspace not occupied by interference retains a higher dimension for useful signal transmission.IA can eliminate interference between users and improve the degree of freedom(Do F)and spectrum utilization.However,IA is constrained by the feasibility conditions,and the interference in dense networks cannot be completely eliminated by using IA alone.Therefore,jointly exploiting the advantages of IA and sub-channel allocation can further improve the system performance.In order to satisfy the differentiated Do F demands and sub-channel demands of users in dense heterogeneous wireless networks,this thesis proposes a joint IA and sub-channel resource allocation strategy for interference cancellation in the network.With the objective of maximizing the number of satisfied users in the network,this thesis formulated the joint IA and sub-channel resource allocation problem in dense heterogeneous wireless networks as an optimization problem.For the users in the selected feasible IA groups,intrainterference from the IA group is eliminated by using IA,and for the other users,their interference eliminated by allocating orthogonal sub-channels.Since the original problem is NP-hard,this thesis proposes a low-complexity heuristic algorithm based on graph theory.Firstly,a conflict graph is constructed to demonstrate the interference relationships of users in the network and the different Do F and sub-channel resource demands of each user.Secondly,a transformed conflict graph is constructed to depict the influence of using IA in the feasible IA group on the interference distribution of the network as well as the Do F and sub-channel demands of the users.Then,based on the constructed conflict graph and the transformed conflict graph,a selection scheme for the feasible IA groups and a sub-channel allocation algorithm are proposed.Finally,the simulation results show that the proposed joint IA and sub-channel allocation strategy has a higher user satisfaction rate than the strategy using sub-channel allocation only.