Assessment Of Urban-Scale Wireless Networks With A Small Number Of Measurements

In order to evaluate, improve, or expand a deployed, city-wide wireless mesh network, it is necessary to assess the network's spatial performance. In this paper, we present a general framework to accurately predict a network's well-served area, termed the metric region, via a small number of measurements. Assessment of deployed networks must address two key issues: non-uniform physical-layer propagation and high spatial variance in performance. Addressing non-uniformity, our framework estimates a mesh node's metric region via a data-driven sectorization of the region. We find each sector's boundary (radius) with a two-stage process of estimation and then measurement-driven "push-pull" refinement of the estimated boundary. To address high spatial variation, our coverage estimation couples signal strength measurements with terrain information from publicly available digital maps to estimate propagation characteristics between a wireless node and the client's location. To limit measurements and yield connected metric regions, we consider performance metrics (such as signal strength) to be monotonic with distance from the wireless node within each sector. We show that despite measured violations in coverage monotonicity, we obtain high accuracy with this assumption. We validate our estimation and refinement framework with measurements from 30,000 client locations obtained in each of two currently operational mesh networks, Google-WiFi and TFA. We study three illustrative metrics: coverage, modulation rate, and redundancy, and find that to achieve a given accuracy, our framework requires two to five times fewer measurements than grid sampling strategies. Finally, we use the framework to evaluate the two deployments and study the average size and location of their coverage holes as well as the impact of client association policies on load-balancing.