Data reduction and analysis is a major aspect of radio channel modelling. Many radio link planning tools and international standards are based on empirical or semi-empirical models extracted from measurements. Hence it is essential that data are acquired at the appropriate rate and accurately analyzed.
In this chapter we study the analysis techniques pertinent to various radio channels. We start with the analysis of a single radio channel measurement (a snapshot) to estimate the impulse response and the frequency response of the channel using basic spectral analysis techniques. We commence with the discrete Fourier transform (DFT) and the effect of the window functions on the estimated channel response. In particular for frequency dispersive channels move on to the more advanced spectral estimation techniques of parametric modelling such as autoregressive (AR), moving average (MA) and autoregressive moving average (ARMA) modelling. Techniques to reduce the effect of interference from measured data will be discussed in particular in relation to ionospheric propagation where the high frequency (HF) spectrum is highly congested.
Another important topic addressed in this chapter is statistical analysis of time and space series. Here, we define and use the RUNS test to determine the stationarity of a process. We then apply it to spatial and time averaging of impulse responses for the estimation of small-scale parameters such as the power delay profile (PDP) and ...