Parameters Optimization for Cooperative Sensing in Multi-Channel Cognitive Radio Networks

In this paper, we propose an optimization model under the scenario where multi-channels are cooperatively sensed and used by multi-secondary users (SUs). The model aims to maximize the system throughput and optimize the parameters including the sensing time and the weight coefficient of the sampling result of each SU for each channel, meanwhile the false access probability for each channel must not exceed the given threshold, so that primary user (PU) transmission is protected. To solve this non-linear optimization model, we propose a heuristic sequential parameters optimization method (SPO). The method begins with deriving the lower bound of the objective function of the optimization model. Then it maximizes this lower bound by optimizing the weight coefficients through solving a series of sub-optimal problems using Lagrange method. Given the weight coefficients are found, it finally transforms the problem into another monotonic programming problem and exploits a fast-convergent polyblock algorithm to find an optimized sensing time parameter. Extensive experiments by simulations demonstrate that, in terms of the throughput gained by the system, SPO can deliver a solution that is up to 99.3% of the optimal on average, which indicates that SPO can solve the proposed optimization model effectively. In addition, the performance advantage of the proposed model on improving the system throughput is further verified by comparing with other optimization models.