A Selection Mechanism Of Interference Coordination Algorithm In Heterogeneous Networks

As the capacity of macro cell in the traditional cellular networks approaches its theoretical limits, heterogeneous networks are fully studied to provide high data rate services to the users. But there will be kinds of downlink interference in heterogeneous network when macrocell and femtocell share the same licensed frequency, and the interference seriously decreases the system capacity. In order to resolve the interference problem, interference coordination algorithms have been widely researched on several domains, such as frequency, power control or load balancing. But no interference coordination algorithm can always get a good system performance in any scenery of heterogeneous network, which has complicated interference state. So there is a need to decide which interference coordination algorithm should be used in a specific heterogeneous scenery.This article designs an interference coordination algorithm selection mechanism to alleviate the downlink interference problem. Firstly, the heterogeneous network technology is introduced, and we analyze the possible kinds of interference in heterogeneous network and why does the interference exist. Secondly, the development of interference coordination algorithm is discussed. By comparing the performance of several coordination algorithms in heterogeneous network where the positions of femto, femto users and macro users are deployed randomly, we find each algorithm’s performance is related to the specific network deployment, so femto in different network deployment needs to select its own algorithm to get the best system performance. Thirdly, in order to identify different network scenery, we design a set of parameters to represent the network scenery. And on this basis the selection mechanism using Q-Learning technology is introduced. The simulation result shows it can select the right interference coordination algorithm for femto in different heterogeneous sceneries, both making sure the macro user’s Qo S and keeping the system performance max. At last, a resource management platform is constructed. On this platform, you can see the optimization process of the interference coordination algorithm.