Optimization of Non-Convex Multiband Cooperative Sensing With Genetic Algorithms

Abstract

In cognitive radios (CRs), secondary users (SUs) transmit alongside primary users (PUs). In order to avoid interference SU perform spectrum sensing and adaptive transmission. Reliable detection in wide geographical regions needs to perform collaborative sensing. The state of the art for efficient cooperative sensing is linear statistics combination. Spatial-spectral joint detection also provides multiband cooperative sensing to access opportunistically several bands at a time. Convex maximization is able to solve only an approximation of the optimization within a restricted solution domain, due to its non-convex nature. In this paper, we demonstrate that convex constraints are counterproductive and we propose an alternative optimization technique based on genetic algorithms. The genetic programming performs direct search of the optimal solution one step before the reformulations needed previously. We demonstrate that, by operating directly on the objective and abstracting from the convexity, the collaborative multiband sensing is optimized consistently with the problem formulation

Optimal Cooperative Spectrum Sensing in Cognitive Radio with Taguchi Method

“…The optimization problem is nonconvex in general. We choose two pairs of α and β to represent two cases which can be found in [11] (please note: in [11], 1 − P f ≥ β, however, in our work, P f ≤ β). Each pair of α and β is one example.…”

“…For conservative and hostile system, the problems are both nonconvex. [11] defines another class of CR, insolent system, and genetic algorithm (GA) has been applied to find the maximum throughput in general. However, GA does not have stable performance because of the randomness in its procedure.…”

Pattern-Search-Based Nonconvex Cooperative Sensing in Multiband Cognitive Radio Systems

“…, c K ] T is the cost of transmitting in the band when a PU is using it. The threshold limits γ min and γ max can be calculated as described in [27]. In addition…”

“…In [25] and [26], the authors solved this convex problem using artificial immune system and Taguchi method, respectively. However, it has been shown in [27] that these constraints limit the performance of the system. Consequently, the authors used genetic algorithm (GA) to deal with the nonconvex optimization problem and showed that a higher throughput versus interference can be achieved.…”

Ten years of research in spectrum sensing and sharing in cognitive radio

“…The original non-convex problems can be successfully converted into convex subproblems. To avoid the convex approximation, an alternative optimization technique based on genetic algorithms [50] has been proposed to directly search for the optimal solution.…”