Kalman filtering for power estimation in mobile communications

In wireless cellular communications, accurate local mean (shadow) power estimation performed at a mobile station is important for use in power control, handoff, and adaptive transmission. Window-based weighted sample average shadow power estimators are commonly used due to their simplicity. In practice, the performance of these estimators degrades severely when the window size deviates beyond a certain range. The optimal window size for window-based estimators is hard to determine and track in practice due to the continuously changing fading environment. Based on a first-order autoregressive model of the shadow process, we propose a scalar Kalman-filter-based approach for improved local mean power estimation, with only slightly increased computational complexity. Our analysis and experiments show promising results.