Cooperative robust sequential detection algorithms for Spectrum Sensing in Cognitive Radio

We consider the problem of spectrum sensing in cognitive radio networks. In our previous work we have developed DualCUSUM, a distributed algorithm for change detection and used it for cooperative spectrum sensing. The algorithm is based on sequential change detection techniques which optimally use the past observations. But DualCUSUM requires the knowledge of the channel gains for each of the secondary users. In this work we modify DualCUSUM to develop GLR-CUSUM which can work with imprecise estimates of the channel gains. Next we extend the algorithm to the case where the receiver noise power is also not known exactly. We show that the SNR wall problem encountered in this scenario is not experienced by our algorithm. We also analyze these algorithms theoretically.