Research On The Technology Of Interference Alignment Based On Clustering

These years a new way of interference management appears and we can call it as interference alignment. The core idea of this technology is to align the interference from different transmitters into a smaller space to save more room for the desired signal. And in this way we can improve the sum rate of the system. On one hand the appearance of IA improved the wireless interference channels’ performance greatly which generalizes the eyesight of researchers on sum rate of interference channels; on the other hand IA introduces the concept of degrees of freedom which boosts the development of network information theory.However, being faced with so many questions, IA has a long distance to go before it can be used practically. For example: the performance of IA is effected by the number of antennas and users; the existing methods that are general for IA is so complicated when the number of user is large. For these reasons, this paper aims at improving the IA methods to make it more practical. The following are the main work of this paper:In this dissertation, we propose the idea of generalized IA and explore different practical IA algorithms to address the above issues the K user constant multiple-input multiple-output interference channel( K-user MIMO IFC). The specific works of this dissertation include:1. We have researched the effect of users’ number on the computation complexity in conventional IA solutions and the application of clustering in IA. As for the IA in each cluster, we propose a new kind of non-iteration IA based on clustering and compare it with iterative max-SINR IA. The simulation result shows that clustering IA not only reduces the computation complexity of IA but also improve the performance of system in particular environment.2. Applying IA into the clustering IA simply can introduce the inter-cluster interference which may harm the performance of system, so we analysis the interference of receivers and propose two kinds of methods dealing with the inter-cluster interference according to the way in which they use the channel state information of different cluster. The simulation result shows that they can both reduce the computation complexity and cancel the inter-cluster interference effectively.3. As for the condition that different interference link have different level of power in practical communication system, we dig up this feature and analysis a clustering method based on receivers’ interference. After that we define the links’ weight according to the links’ power and the expression of sum rate. Besides that we propose two methods of clustering. The simulation result shows that all of them can reduce the computation complexity greatly.It can be expected that our studies have a significant potential to address those issues hindering IA practical applications.