We study the impact of policies to associate users with base stations/access points on flow-level performance in interference limited wireless networks.
Most research in this area has used static interference models (i.e., neighboring base stations are always active) and resorted to intuitive objectives such as load balancing. In this paper, we show that this can be counter productive, and that asymmetries in load can lead to significantly better performance in the presence of dynamic interference which couples the transmission rates experienced by users at various base stations. We propose a methodology that can be used to optimize the performance of a class of coupled systems, and apply it to study the user association problem. We show that by properly inducing load asymmetries, substantial performance gains can be achieved relative to a load balancing policy (e.g., 15 times reduction in mean delay). We present a novel measurement based, interference-aware association policy that infers the degree of interference induced coupling and adapts to it.
Systematic simulations establish that both our optimized static and interference-sensitive, adaptive association policies substantially outperform various proposed dynamic policies and that these results are robust to changes in file size distributions, channel parameters, and spatial load distributions.
Who is Balaji Rengarajan?
More information about Balaji Rengarajan at The University of Texas at Austin
The conference will be conducted in English