In this thesis, a method for finding the
parameters to model interference probability in a wireless channel, using the
ALOHA model, is presented. For extraction of the model parameters, an algorithm which relies on a recording of interference in the wireless channel is proposed. The algorithm computes and segments the recording spectrograms to extract the parameters of individual transmission and clusters these to find the dominating interference sources in the channel. Using simulated data the parameter estimate errors are found in different congestion scenarios. For 2% congestion 92.41%, 57.57% and 15.31% of the
interference transmissions is found with
power levels −60dBm/Hz, −75dBm/Hz
and −90dBm/Hz respectively. The fraction of mid and low powered transmissions
found depends on the level of congestion in
the channel. Finally, a test on real-world
data quantifies the amount of interference
that the algorithm is capable of extracting.