Optimal Cell Assignment Algorithms for Pico-Cell Networks

Abstract

The proliferation of smartphones has unleashed a tremendous growth in wireless traffic, which is projected to continue for the foreseeable future, and put yet greater strain on the capacity of cellular networks. Deployment of pico base stations, reducing cell sizes and allowing more efficient reuse of limited radio spectrum, provides a powerful approach to cope with traffic hot spots and bring capacity relief. This network densification raises a critical need for more intelligent cell selection algorithms, which not only take signal strength values into account, but also load conditions in order to harness the full potential of the pico cells. We describe how the problem of optimally balancing traffic loads among pico cells may be formulated as a linear program (LP), and show how the dual version of the LP provides insight in the structure of the optimal user association. Since the LP formulation involves several system parameters that tend to be time-varying and hard to estimate in practice, the optimal user association can not be calculated in a direct way. Hence, we develop various online cell selection algorithms that solve the LP and determine the optimal user association in a measurement-based manner, without requiring explicit knowledge of the system parameters. Extensive simulation experiments confirm that the algorithms are quite effective in optimally balancing the traffic loads, and further demonstrate that they substantially outperform conventional approaches in terms of user-perceived throughput performance.