Optimal multiband joint detection in cognitive radio networks with the Taguchi method

Abstract

SUMMARYThis paper studies multiband joint detection in cognitive radio networks with the Taguchi method. At the fusion center, linear fusion rule is adopted to collect the observations from distributed secondary users. We aim to achieve maximum aggregate throughput with limited aggregate interference. The problem is challenging due to its nonconvexity and high computational complexity to find the global optimum. Existing works attempt to convert the problem into convex optimization problem. In this paper, the Taguchi method is employed to estimate the gradient of the aggregate throughput, determine the optimal thresholds of the energy detectors and combination weights of the linear fusion rule regardless of convexity. To optimize thresholds and linear weights simultaneously, we employ newly defined variables to represent the changing ranges of detector thresholds when we search for optimal linear weights. In addition, the sensing duration is another factor to be optimized in the Taguchi method. The simulation results show that the proposed method is efficient and applicable for all classes of cognitive radio without considering convexity. The optimization performance is considerably improved. Moreover, the Taguchi method is insensitive to parameter initialization, which provides a relatively robust output. Copyright © 2013 John Wiley & Sons, Ltd.