Virtual Cell Clustering with Optimal Resource Allocation to Maximize Capacity

The deployment of small cells in cellular networks increases their overall capacity, however, the proximity of small cells to one another also causes significant interference. To reduce interference and increase capacity, this work proposes a new resource allocation optimization and network management framework for wireless networks using neighborhood-based optimization rather than fully centralized or fully decentralized methods. We first utilize hierarchical clustering with a minimax linkage criterion for forming the virtual cells. Once the virtual cells are formed we consider two cooperation models: the interference coordination model and the coordinated multi-point decoding model. In the first model, base stations in a virtual cell decode their signals independently but allocate the communication resources cooperatively. In the second model, base stations in the same virtual cell allocate the communication resources and decode their signals cooperatively. We address the resource allocation problem for each of these cooperation models. Our numerical results indicate that the proper design of the neighborhood-based optimization leads to significant gains in sum rate over fully decentralized optimization. Nonetheless, they may have a significant sum rate penalty compared to fully centralized optimization. In particular, neighborhood-based optimization has a significant sum rate penalty compared to fully centralized optimization in the coordinated multi-point model, but not in the interference coordination model.