We propose an energy efficient distributed cooperative Change Detection scheme called DualCUSUM based on Page's CUSUM algorithm. In the algorithm, each sensor runs a CUSUM and transmits only when the CUSUM is above some threshold. The transmissions from the sensors are fused at the physical layer. The channel is modeled as a Multiple Access Channel (MAC) corrupted with noise. The fusion center performs another CUSUM to detect the change. The algorithm performs better than several existing schemes when energy is at a premium. We generalize the algorithm to also include nonparametric CUSUM and provide a unified analysis. Our results show that while the false alarm probability is smaller for observation distribution with a lighter tail, the detection delay is asymptotically the same for any distribution. Consequently, we provide a new viewpoint on why parametric CUSUM performs better than nonparametric CUSUM. In the process, we also develop new results on a reflected random walk which can be of independent interest.

Abstract-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.

Abstract-This paper considers the problem of Spectrum Sensing in Cognitive Radio Networks. For this we use a recently developed distributed cooperative algorithm DualCUSUM. 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.

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This paper considers the problem of spectrum sensing in cognitive radio networks when the primary user employs Orthogonal Frequency Division Multiplexing (OFDM). We develop cooperative sequential detection algorithms based on energy detectors and the autocorrelation property of cyclic prefix (CP) used in OFDM systems and compare their performances. We show that sequential detection provides much better performance than the traditional fixed sample size (snapshot) based detectors. We also study the effect of model uncertainties such as timing and frequency offset, IQ-imbalance and uncertainty in noise and transmit power on the performance of the detectors. We modify the detectors to mitigate the effects of these impairments. The performance of the proposed algorithms are studied via simulations.It is shown that energy detector performs significantly better than the CP-based detector, except in case of a snapshot detector with noise power uncertainty. Also, unlike for the CP-based detector, most of the above mentioned impairments have no effect on the energy detector.