Importance

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Abstract

Mobile broadband traffic has been exploding in wireless networks resulting in an increase of interferences and reduced operator control. Networks are also becoming more heterogeneous putting additional demand in interference management. Scheduler schedules uplink transmissions from UEs based on a load prediction algorithm that typically assumes worst case. However, UEs do not always use full power granted, and thus, much of granted radio resources are wasted. To address these and other issues, techniques to accurately predict/estimate other cell interferences and thermal noise separately and to accurately predict/estimate load utilization probabilities, rise-over-thermals, and load factor biases are described. The described techniques are applicable in a radio network node with any number of antenna branches. The techniques can be used to schedule UEs to more fully utilize available radio resources. Extended Kalman filtering can be adapted for use in estimation providing low order computational complexity.

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First Claim