## Fairness and Load Balancing in Wireless LANs Using Association Control

Citations: | 94 - 3 self |

### BibTeX

@MISC{Bejerano_fairnessand,

author = {Yigal Bejerano and Seung-Jae Han and Li (Erran) Li},

title = { Fairness and Load Balancing in Wireless LANs Using Association Control},

year = {}

}

### Years of Citing Articles

### OpenURL

### Abstract

Recent studies on operational wireless LANs (WLANs) have shown that the traffic load is often unevenly distributed among the access points (APs). Such load imbalance results in unfair bandwidth allocation among users. We argue that the load imbalance and consequent unfair bandwidth allocation can be greatly alleviated by intelligently associating users to APs, termed association control, rather than having users associate with the APs of strongest signal strength. In this paper, we present an efficient algorithmic solution to determine the user-AP associations for max-min fair bandwidth allocation. We provide a rigorous formulation of the association control problem, considering bandwidth constraints of both the wireless and backhaul links. We show the strong correlation between fairness and load balancing, which enables us to use load balancing techniques for obtaining optimal max-min fair bandwidth allocation. As this problem is NP-hard, we devise algorithms that achieve constant-factor approximation. In particular, we present a 2-approximation algorithm for unweighted users and a 3-approximation algorithm for weighted users. In our algorithms, we first compute a fractional association solution, in which users can be associated with multiple APs simultaneously. This solution guarantees the fairest bandwidth allocation in terms of max-min fairness. Then, by utilizing a rounding method, we obtain the integral solution from the fractional solution. We also consider time fairness and present a polynomialtime algorithm for optimal integral solution. We further extend our schemes for the on-line case where users may join and leave dynamically. Our simulations demonstrate that the proposed algorithms achieve close to optimal load balancing (i.e., maxmin fairness) and they outperform commonly-used heuristic approaches.

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