itional value at the be- broadband case, to be discussed later on, were only re- ginning of this excitation zone, the delay spread at the cently made by Semmelrodt and Kattenbach [SeKa02b]. end, Figure 4.7-45. The length of the excitation zone A common approach of fading prediction is that of depends on the elevation angle of the transmitter as do frequency analysis on the complex fading signal, for in- the maximum transitional values of the spreads: the stance, by the use of high-resolution spectral estimation smaller the elevation, the longer the excitation zone, and schemes like ESPRIT or root-MUSIC. The constituting the smaller the maximum transitional spreads. sinusoidal signals are easily extrapolated and the predicted signal is formed by the same linear combina- tion of the now extrapolated sinusoids as in the original 4.7.5.8 Fading prediction signal, Figure 4.7-46. The attractiveness of this approach lies in the fact that numerous frequency analysis tools are Introduction available, see for instance [StMo97], and in the physical In mobile radio system engineering it would be an ad- interpretation of the results. The sinusoidal components vantage if channel fading behaviour could be forecast, are readily identified as Doppler components [AJJF99], CHAPTER 4.7 Propagation modelling and channel characterisation 7 m3 h) at 5.2 GHz over a distance of 1.84 m. The data was filtered in the Doppler domain in order to suppress out-of-band noise. Figure 4.7-47 shows that the best performance is achieved with the Modified Covariance method for the stochastic synthetic channels and the measured channels, Distance (a) not with Jakes channels (not shown). The prediction length shown is defined as the length for which the rela- tive mean-square error between the exact and predicted fading signal reaches -20 dB [SeKa03] and is normalised with respect to the wavelength. The QR-RLS method showed a performance close to that of the Modified Covariance method. For Jakes channels, subspace-based Distance (a) parameter estimatio