Robust Performance of Spectrum Sensing in Cognitive Radio Networks

Harmonic coexistence of cognitive radio systems and licensed systems requires the secondary users to have the capability of sensing and keeping track of primary users' transmissions. While existing spectrum sensing methods usually assume known distributions of the primary signals, such an assumption is often not true in practice. As a result, applying existing sensing methods will often lead to unreliable detection performance in practical networks. In this chapter, the authors try to investigate the sensing performance under the distribution uncertainty of primary signals. They first investigate the performance bounds for single user detection with unknown distribution, and provide an analytical expression for the lower bound of detection probability. Moreover, they bring the distribution uncertainty into multi-user cooperative sensing. The authors formulate the optimal sensing design as a robust optimization problem, and propose an iterative algorithm to determine the optimal decision threshold for each user. Extensive simulations demonstrate the effectiveness of the proposed algorithm.