 decision based on the CSIs of its own UEs, metrics maintained by its own scheduler and the metrics received from adjacent eNodeBs. The amount of X2 signalling can be minimized by exchanging only cell-level metrics instead of UE-specific information as shown in Figure 7.26. The centralized scheduler benefits if it can obtain UE-specific CSI information since it does not have direct access to any local scheduler information. UE-specific information exchange may increase the amount of signalling. The scalability of the multi-cell coordination in the large networks needs to be consid- ered. Since the interference caused by a cell typically affects closest neighbouring cells, the coordination to overcome interference typically requires only local coordination instead of coordination over large areas. The most relevant interfering neighbouring cells can change depending on the UE locations, antenna tilts and network expansions. The preferred solution is a flexible cluster configuration instead of predefined coordination clusters. The flexible cluster means that each cell can have dynamically defined neighbour cells in the multi-cell coordi- nation. This concept is called liquid clusters and is shown in Figure 7.27. The coordination capability with fixed and liquid clusters in system simulations is illustrated in Figure 7.28. If we have three-cell fixed cluster, only 60% of UEs have its strongest interferer in the coordina- tion cluster. If the fixed cluster size is increased to nine cells, the probability grows to 72%. If Cell-level metrics exchange Cell A Cell B Cell C Scheduler Scheduler Scheduler Figure 7.26 Scheduling metrics exchange in distributed multi-cell scheduling LTE Small Cell Optimization Coordination Coordination cluster for Cell A cluster for Cell B Figure 7.27 Liquid cluster concept in multi-cell scheduling seven-cell cluster is used with liquid cluster selection, the probability is increased even above 99% SO that the strongest interferer is in the coordination cluster. That gives a good starting poi