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On-Line AP Association Algorithms for 802.11n WLANs with Heterogeneous Clients

As the latest amendment of IEEE 802.11 standard, 802.11n allows a maximum raw data rate as high as 600 Mbps, making it a desirable candidate for wireless local area network (WLAN) deployment. In typical deployment, the coverage areas of nearby access points (APs) usually overlap with each other to provide satisfactory coverage and seamless mobility support. Clients tend to associate (connect) to the AP with the strongest signal strength, which may lead to poor client throughput and overloaded APs. Although a number of AP association schemes have been proposed for IEEE 802.11 WLANs in the literature, the challenges brought by the new features in 802.11n have not been thoroughly studied nor the impact of legacy 802.11a/b/g clients in 802.11n WLANS on AP association. To fill in this gap, in this paper, we explore AP association for 802.11n with heterogeneous clients (802.11a/b/g/n). We first present a bi-dimensional Markov model to estimate the uplink and downlink throughput of clients and formulate AP association into an optimization problem, aiming at providing each client a bandwidth proportional to its usable data rate. Based on this Markov model, we propose an on-line AP association algorithm under the condition that each client can acquire timely information of all clients associated with nearby APs. Furthermore, for WLANs with densely deployed APs, we provide another on-line AP association algorithm with lower complexity, which takes full advantage of 802.11n transmissions by simply associating different types of clients with different APs. We have conducted extensive simulations and experiments to validate the proposed algorithms. The results show that our algorithms can significantly improve both 802.11n throughput and aggregated network throughput under various network scenarios, compared to previous AP association schemes. Our experiments also confirm the effectiveness of the algorithms in enhancing network throughput, maintaining proportional fairness among clients, and balancing load among APs.