Efficient SINR Estimating with Accuracy Control in Large Scale Cognitive Radio Networks

Recently, the SINR-model has been widely utilized in link scheduling, spectrum allocation and other applications. The SINR model requires the receiving power information of all potential link peers, which is usually assumed to be known as priori or following a uniform propagation model. We have performed experiments to illustrate how real power data could improve the performance of the SINR-based applications with considerable margin. Thus, obtaining the real power data through measurements is promising. However, this method faces many challenges. We propose a pathloss model based solution, including a representative link selection method to cut down the measurement pairs; accuracy control to determine the sample size; and a measurement distribution method to shorten the measurement duration. Our experiments show that our solution significantly improves the SINR-based scheduling´s performance.