Abstract

Received total wideband power (RTWP) data is a measurement of the wanted and unwanted power levels received by a 3G radio base station (RBS) and is a concise indicator of uplink network performance. Using a statistical physics approach, we aim to detect periods of unusual activity between cells by assessing a sample of RTWP measurement data from a live network. Using wavelet correlation and cross-correlation techniques we analyse multivariate non-stationary time series for statistical relationships at different time scales. We analyse the seasonal component of the dataset as well as examining the autocorrelation and partial autocorrelation methods. We then explore the Hurst exponent of the dataset and inspect the intraday correlations for patterns of events. Next, we examine the eigenvalue spectrum using different sized sliding windows. Finally, we compare approaches for assessing multiscale relationships among several variables using the wavelet multiple correlation and wavelet zero-lag cross-correlation on non-stationary RTWP time series data.