A Wavelet Based Long Range Signal Strength Prediction in Wireless Networks

Prediction of rapidly time varying fading channel conditions enables adaptive data transmissions in wireless systems, which in turn improves the quality of service for end users and reduces the power consumption for data transmissions. Most of the existing long range prediction methods for fast fading in wireless networks use autoregressive (AR) models and make the assumption that the input fading signal is stationary and wireless channel parameters vary slowly (S. Semmelrodt and R. Kattenbach, 2003). In this paper, we provide a method to predict the non-stationary received signal strength in a more realistic and fast varying wireless environment, using multiresolution wavelet analysis. We first use discrete wavelet decomposition (DWT) to decompose the signal strength trace into components at different scales, then use AR and linear regression models to predict small, medium and large scale fading components respectively, and finally synthesize the output signal of our prediction algorithm. By properly choosing the wavelet basis, we map the non-stationary signal strength trace into stationary wavelet detail coefficients and use them as input to the AR predictor at different scales. Longer prediction range is easily achieved by choosing the appropriate maximum decomposition scale, while still achieving low prediction error. Our experimental results shows that our wavelet based algorithm outperforms existing time-domain AR prediction methods in terms of both prediction accuracy and computational complexity.