Virtual Cell Clustering with Optimal Resource Allocation to Maximize Capacity

TL;DR

This paper introduces a hierarchical clustering-based resource allocation framework for wireless networks, balancing between centralized and decentralized approaches to improve network capacity.

Contribution

It proposes a novel neighborhood-based optimization method with hierarchical clustering for virtual cell formation and analyzes two cooperation models for resource allocation.

Findings

Significant sum rate gains over decentralized optimization.

Trade-offs between centralized and neighborhood-based approaches.

Different impacts on sum rate depending on cooperation model.

Abstract

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 propose hierarchical clustering with a minimax linkage criterion for the formation of 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. For the interference coordination model this problem is an NP-hard mixed-integer optimization problem whereas for the coordinated multi-point decoding model it is convex. Our numerical results indicate that proper design of the neighborhood-based optimization leads to significant gains in sum rate over fully decentralized optimization, yet may also 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 the interference coordination model.