-Recently we proposed the long range prediction algorithm for fast fading channels. This method provides the enabling technology for adaptive transmission techniques such as adaptive coding, adaptive power control, etc. In this paper, we used actual field measurements to validate the long range prediction method and its utilization in adaptive power control.

For a sustainable integration of wind power into the electricity grid, precise and robust predictions are required. With increasing installed capacity and changing energy markets, there is a growing demand for short-term predictions. Machine learning methods can be used as a purely data-driven, spatio-temporal prediction model that yields better results than traditional physical models based on...

We discuss the accuracy of the prediction of the aggregated power output of wind farms distributed over given regions. Our forecasting procedure provides the expected power output for a time horizon up to 48 hours ahead. It is based on the large scale wind field prediction which is generated operationally by the German weather service. Our investigation focuses on the statistical analysis of th...

This paper explores the role of branch predictor organization in power/energy/performance tradeoffs for processor design. We find that as a general rule, to reduce overall energy consumption in the processor it is worthwhile to spend more power in the branch predictor if this results in more accurate predictions that improve running time. Two techniques, however, provide substantial reductions ...

.4bstractBecause the inductive noise L d i l d t is induced by the power change and can have disastrous impact on the timing and reliability of the system, high-performance CPU designs are more concerned with the step power reduction instead of the average power reduction. The step power is defined as the power difference between the previous and present clock cycles, and represents the L d i l...

The power transformation family studied by Box and Cox (1964) for transforming to a normal linear model has recently been further studied by Bickel and Doksum (1978), who show that "the cost of not knowing Aand estimating it . . . is generally severe"; some of the variances for regression parameters can be extremely large. We consider prediction of future observations (untransformed) when the d...

The greatest current challenge in wireless sensor networks consists in building a completely adaptive network without fixed infrastructure and with the smallest energy resources. Applications built upon such networks are various: from telemetry to medical follow-up and from intrusion detection to infrastructures maintenance. Nevertheless, their design requires a large effort to guarantee quasi ...

This paper presents an empirical methodology for low power driven complex DSP embedded systems design. Unlike DSP design for high performance, research of low power DSP design has received little attention, yet power dissipation is an increasingly important and growing problem. Highly accurate power prediction models for DSP software are derived. Unlike previous techniques, the methodology deri...

In mobile systems, energy efficiency is critical to extend battery life. Therefore, power consumption should be taken into account to develop software in addition to performance. Efficient software design in power and performance is possible if accurate power prediction is accomplished during the execution of software. In this paper, power estimation model is developed using statistical analysi...

Ekman, T., 2002. Prediction of Mobile Radio Channels: Modeling and Design, 254 pp. Uppsala. ISBN 91-506-1625-0. Prediction of the rapidly fading envelope of a mobile radio channel enables a number of capacity improving techniques like fast resource allocation and fast link adaptation. This thesis deals with linear prediction of the complex impulse response of a channel and unbiased quadratic pr...