Distributed measurements for estimating and updating cellular system performance

We investigate the use of distributed measurements for estimating and updating the performance of a cellular system. Specifically, we discuss the number and placement of sensors in a given cell for estimating its signal coverage. Here, an "outage" is said to occur at a location if a mobile receiver there has inadequate signal-to-noise ratio (SNR -based outage) or, using another criterion, inadequate signal-to-interference ratio (SIR- based outage); and the "outage probability" is the fraction of the cell area over which outage occurs. A design goal is to improve measurement efficiency (i.e., minimizing the required number of measurement sensors) while accurately estimating the outage probability and mapping the coverage holes. The investigation uses a generic path loss model incorporating distance effects and spatially correlated shadow fading. Our emphasis is on the performance prediction accuracy of the sensor network, rather than on cellular system analysis per se. Through analysis and simulation, we assess several approaches to estimating the outage probability. Applying the principle of importance sampling to the sensor placement, we show that a cell outage probability of P_o can be accurately estimated using ~ 10/P_o power-measuring sensors distributed in a random uniform way over the area with base-sensor distances from 50% to 100% of the cell radius. This result applies to both SNR-based and SIR-based outage estimation for both indoor and outdoor environments.