Robust Performance of Spectrum Sensing in Cognitive Radio Networks

The successful coexistence of secondary users (SUs) and primary users (PUs) in cognitive radio networks requires SUs to be spectrum aware and know which spectrum bands are occupied by PUs. Such awareness can be achieved in several ways, one of which is spectrum sensing. While existing spectrum sensing methods usually assume known distributions of the received primary signals, such an assumption is often too strong and unrealistic, and leads to unreliable detection performance in practical networks. In this paper, we design robust spectrum sensing algorithms under the distribution uncertainty of primary signals. After formulating the optimal sensing design as a robust optimization problem, we decompose it into a series of analytically tractable semi-definite programs, and propose an iterative algorithm to search the optimal decision threshold while maintaining the desirable false alarm probability during the iterations. Numerical results verify that our robust sensing algorithm improves the worst-case detection probability and reduces the system sensitivity on decision variables.