Transfer functions and pulse distortion for an ionospheric reflection channel with embedded random irregularities

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The ionospheric reflection channel is known to be dispersive, time varying, and random. The dispersive nature of the ionosphere and the propagation geometry make the transfer function frequency selective, i.e., frequency dependent; while the ionospheric motions cause the communication channel to be time dependent or fading. Frequently, the background ionosphere is permeated by random irregularities. A radio wave propagating through such an irregular ionosphere will have its phase randomized, followed by phase mixing through diffraction. As a result, the transfer function also acquires a random character. The transfer function for a random, dispersive, and time‐varying ionospheric reflection channel can be simulated numerically using the phase‐screen‐diffraction‐layer method. The detailed behavior of the calculated transfer function is examined in several different ways. First, the spatial behavior of the transfer function, both magnitude and phase, is depicted in three‐dimensional plots. This spatial behavior can also be interpreted as a temporal behavior for a moving ionosphere. A curious phase jump of 180° accompanied by a deep fade at some frequency within the passband of the signal is found to occur. To facilitate the examination of this phase jump, locus plots are made. Using real and imaginary parts of the transfer function as coordinates, the transfer function traces out a locus with increasing frequency on this complex plane. In the presence of severe selective fading the locus is shown to pass through the origin occasionally. When this happens, the magnitude of the transfer function dips down to zero with an accompanied phase jump of 180°. Such an event is caused by destructive interference among multipath components. Second, the channel behavior is examined by taking temporal moments of the received pulse. This is done for several sample pulses, each up to the moments of the fourth order, giving the arrival time, pulsewidth, skewness, and kurtosis. Each of these four quantities is then treated as a time series in the computation of the mean, standard deviation, histogram, power spectrum, and autocorrelation function. The stochastic behavior of these four quantities is thus thoroughly studied.