  With the increasing power of machine learning-based reasoning, the use of
meta-information (e.g., digital signal modulation parameters, channel
conditions, etc.) to predict the performance of various signal processing
techniques has become feasible. One such problem of practical interest is
choosing a proper interference mitigation method based on the meta information
of the received signal. Since heuristic table-based methods suffer from limited
prediction capability for unseen cases, we propose a recommendation system
based on the use of Random Forests (RF). Specifically, RF used to predict the
Bit-Error-Rate (BER) of all mitigation approaches so as to determine the
approach with the best performance. We found RF can predict BER with high
accuracy, and its importance factor demonstrates which input attributes matter
most. These BER prediction results can also benefit other functions such as
adaptive modulation, channel sensing, beaming selection, etc.
