A collaborative algorithm is developed to track the channel gains from arbitrary positions in a geographical area to each node of a cognitive radio network. Spatio-temporal shadow fading effects are characterized using an experimentally verified spatial loss field model. Kriged Kalman filtering (KKF) is then applied to track the time-varying shadowing field. The proposed KKF algorithm consists of a distributed Kalman filter that estimates the spatio-temporal trend field, and a Kriging interpolator which captures the temporally white yet spatially descriptive component at the individual sensors. Numerical tests demonstrate that the collaborative tracking algorithm outperforms a non-collaborative alternative in terms of mean-square error when applied to a cognitive radio sensing task.

Collaborative Channel Gain Map Tracking for Cognitive Radios

DALL'ANESE, EMILIANO;PUPOLIN, SILVANO
2010

Abstract

A collaborative algorithm is developed to track the channel gains from arbitrary positions in a geographical area to each node of a cognitive radio network. Spatio-temporal shadow fading effects are characterized using an experimentally verified spatial loss field model. Kriged Kalman filtering (KKF) is then applied to track the time-varying shadowing field. The proposed KKF algorithm consists of a distributed Kalman filter that estimates the spatio-temporal trend field, and a Kriging interpolator which captures the temporally white yet spatially descriptive component at the individual sensors. Numerical tests demonstrate that the collaborative tracking algorithm outperforms a non-collaborative alternative in terms of mean-square error when applied to a cognitive radio sensing task.
Proc. of The Second International Workshop on Cognitive Information Processing (CIP)
9781424464579
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2469770
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