kes the occur- processing of measured data; rence of less than 15 multipath components on the radio 2. a subsection on path loss, its distance dependence and channel as an indication that Gaussian properties cannot small- and large-scale fluctuations; be assumed for the channel transfer [Bult02b]. 3. a subsection on temporal and angular dispersion; and Depending on the distributions and the independence of the multi-path components, much larger numbers than 4. a subsection on fading prediction techniques. 15 could be needed to safely assume Gaussian properties. Bultitude tested on WSS [Bult03] by comparing sub- sequent Doppler spectra. He used a MUSIC algorithm to 4.7.5.2 Statistical processing of extract Doppler parameters from half-overlapping 1 measurements metre segments of measurements and computed the running variance of both the mean power-weighted AoA The properties of the mobile channel to be treated in this and the angular spread. Changes of greater than 10% of the subsection are all modelled as random processes. The maximum value of either variances were taken to indicate bulk of the characteristics are derived, as in the majority non-stationarity. For measurements in a microcellular CHAPTER 4.7 Propagation modelling and channel characterisation Uplink Downlink Frequency lag (MHz) Frequency lag (MHz) (a) Direct correlation: uplink. (b) Direct correlation: downlink. Uplink Downlink Frequency lag (MHz) Frequency lag (MHz) (c) Fourier transforming PDPs: uplink. (d) Fourier transforming PDPs: downlink. Figure 4.7-31 Comparison between frequency correlation estimates at 2 GHz (after [SaGo01]). scenario in downtown Ottawa (Manhattan grid) at 1.8 transforms. Salous and Gokalp presented an example GHz, approximately negatively exponentially distributed where the relatively small differences between urban stationarity intervals, 'consistency lengths', were found, up- and downlink characteristics at 2 GHz resulted in but the measurements showed greater probabilities for clear differences between frequency correl