Research On Clustered Interference Alignment Under MIMO Interference Channel

With the development of the wireless communication technologies such as Massive Multiple-Input Multiple-Output(MIMO),the rate of the data transmission is getting faster and faster.The interference is an important factor limiting system performance in the traditional wireless communication systems.And the interference alignment(IA)is widely used as an interference management strategy to eliminate the interference effects,which can approach the capacity in case of satisfying the feasibility.However,the situation that the number of users and the demand for information transmission rates are explosively increasing can no longer be dealt with only by IA.Clustered interference alignment provides an effective scheme to solve the problem of the interference management in cell densification system,that is,by aligning strong interference within clusters and ignoring the weak inter-cluster interference to achieve higher system performance.This paper mainly studies the clustered IA for the cell densification system under the limited backhaul link capacity,and the base stations are cacheable.First,two kinds of clustering IA algorithms based on graph partitioning and coalition formation are introduced,and their advantages and disadvantages are analyzed.The graph partitioning algorithm has lower complexity under appropriate constraints,but does not consider Channel State Information(CSI)overhead.The clustering algorithm of the coalition formation has a better clustering effect and considers the CSI overhead,but has a high complexity.Second,the backhaul link and the exponential distribution based cache model are introduced.Under this model,an opportunistic IA user selection scheme based on deep reinforcement learning under the finite state Markov channel is introduced.Finally,aiming at the advantages and disadvantages of the above clustering algorithms,a clustering algorithm with cluster size balance is proposed.By redefining the edge weights in the graph and designing the system performance indicators in the clustering process,a new graph partitioning model is constructed.The proposed algorithm uses the clustering algorithm with cluster size soft constraints as the heuristic algorithm,and balances the pre-clustering scheme with the greedy idea,so that the feasible conditions of the IA are more easily satisfied in the cluster.The simulation results and numerical statistics demonstrate the effectiveness and advantages of the proposed algorithm.Moreover,according to the clustering result,a CSI overhead quantization method is designed.The CSI overhead is combined with the average throughput as the long-term throughput to measure the system performance.The simulation results show the impact of backhaul link capacity parameters and base station cache parameters on long-term throughput.By compressing the CSI shared capacity in the backhaul link,the performance of the system under the high signal to noise ratio is further improved.Aiming at the impact of clustering on the system,a method for optimizing the backhaul link capacity allocation based on clustering results is proposed.The simulation results confirm the effectiveness of using this optimization method under different clustering algorithms,and once again prove the advantages of the proposed algorithm.