CACAO: Distributed Client-Assisted Channel Assignment Optimization for Uncoordinated WLANs

IEEE 802.11 WLANs are becoming more and more popular in homes and urban areas. As compared to traditional WLAN setups (such as in campuses) where knowledgeable network administrators can make centralized decisions on channel selection, access points (APs) in these networks are often deployed by network nonspecialists in an uncoordinated manner, leading to unplanned topologies, interference, and therefore unsatisfactory throughput performance. We consider in this paper a distributed channel assignment algorithm for uncoordinated WLANs, where APs can self-configure their operating channels to minimize interference with adjacent APs. We first formulate the optimization problem on channel assignment which overcomes some of the weaknesses encountered by uncoordinated WLANs. We show that the problem is NP-hard, and propose an efficient, simple, and distributed algorithm termed CACAO (Client-Assisted Channel Assignment Optimization). In CACAO, the clients feed back traffic information to their APs. This leads to better network condition knowledge and better channel assignment decisions at the APs. We conduct extensive simulation study and comparisons using Network Simulator 2 (NS2). Our results show that CACAO outperforms other traditional and recent schemes in terms of TCP and UDP throughputs with a similar level of fairness. Furthermore, it converges quite fast and reduces cochannel interference significantly.