Comparison between Static and Dynamic Modeling Approaches for Heterogeneous Cellular Networks

In order to accommodate growing traffic demands, next generation cellular networks must become highly heterogeneous to achieve capacity gains. Heterogeneous cellular networks composed of macro base stations and low-power base stations of different types are able to improve spectral efficiency per unit area, and to eliminate coverage holes. In such networks, intelligent user association and resource allocation schemes are needed to achieve gains in performance. We focus on heterogeneous cellular networks that consist of macro and pico BSs, and study the interplay between user association and resource allocation using two modeling approaches, namely a static modeling approach and a dynamic modeling approach. Our first study focuses on modeling heterogeneous cellular networks with a static approach. We propose a unified static framework to study the interplay of user association and resource allocation under a well-defined set of assumptions. This framework allows us to compare the performance of three resource allocation strategies: partially Shared deployment, orthogonal deployment, and co-channel deployment when the user association is optimized. We have formulated joint optimization problems that are non-linear integer programs which are NP-hard. We have, therefore, developed techniques to obtain upper bounds on the system’s performance. We also propose a simple association rule that performs much better than all existing user association rules. We have used these upper bounds as benchmarks to provide many engineering