Self-management in chaotic wireless deployments

Over the past few years, wireless networking technologies have made vast forays into our daily lives. Today, one can find 802.11 hardware and other personal wireless technology employed at homes, shopping malls, coffee shops and airports. Present-day wireless network deployments bear two important properties: they are unplanned, with most access points (APs) deployed by users in a spontaneous manner, resulting in highly variable AP densities; and they are unmanaged, since manually configuring and managing a wireless network is very complicated. We refer to such wireless deployments as being chaotic. In this paper, we present a study of the impact of interference in chaotic 802.11 deployments on end-client performance. First, using large-scale measurement data from several cities, we show that it is not uncommon to have tens of APs deployed in close proximity of each other. Moreover, most APs are not configured to minimize interference with their neighbors. We then perform trace-driven simulations to show that the performance of end-clients could suffer significantly in chaotic deployments. We argue that end-client experience could be significantly improved by making chaotic wireless networks self-managing. We design and evaluate automated power control and rate adaptation algorithms to minimize interference among neighboring APs, while ensuring robust end-client performance.