Robust dual cumulative sum algorithm for cooperative spectrum sensing

Spectrum sensing is an important problem in cognitive radio. Outliers present in the channel deteriorate the performance of existing non-robust algorithms. We consider the problem of limiting the influence of outliers in cooperative spectrum sensing techniques. In this work we use Huber's least favorable pair based on mixture model with appropriate nominal distributions in the DualCUSUM algorithm, a sequential change point detection algorithm used for spectrum sensing. We show that proposed robust DualCUSUM algorithm performs better than existing DualCUSUM algorithm in the presence of outliers. It is also shown by simulation results that better performance can be achieved when the design parameter used in obtaining the least favorable pair equals the actual contamination level in the data. A method to generate random numbers which follow least favorable pair of distributions is also discussed.