ed variability of the random process under [MuSV05]. There the idea is to use existing already consideration is only a function of either time or parametrised measurement data to create a dataset that distance, making it a stochastic process; resembles closely the original channel information. The the variability is only a function of the time difference or approach is based on combining the radiation patterns distance interval between two samplings, as in a Wide- obtained in calibration measurement of the measure- Sense-Stationary (WSS) stochastic process. As ment arrays with the parametrised propagation data. a result, (ensemble) mean values should be indepen- This way a well-known but still realistic test data with dent of time or distance and autocorrelation functions multiple signals can be created for studying, e.g. the should only depend on the time or distance difference; correctness of estimated angular distributions. the stochastic process is ergodic, allowing replacing ensembles by isolated sample functions, mostly 4.7.5 Channel characterisation time series. Effectively, this means that the mean of the stochastic 4.7.5.1 Introduction process should be constant and that the covariance of the process should exist, conditions that could prove difficult This section will mainly deal with aspects of the to test in practical situations. It is also fair to say that in variability of power received through the mobile radio the literature tests are seldom reported that justify the channel. These aspects comprise the dependence with above assumptions. distance, both on a small and large scale, and its tem- One possibility is to assure that the stochastic process is poral and angular dispersion, all regarded separately. both WSS and Gaussian, as such processes are ergodic. The structure will be as follows: However, Gaussian properties should be inferred through the central limit theorem, as time series analysis in- 1. a short introduction to the formal requirements on the herently assumes ergodicity. Bultitude ta