Motivated by the practical and accurate demand of intelligent cognitive radio (CR) sensor networks, a new modeling method of practical background noise and a novel sensing scheme are presented, where the noise model is the non-Gaussian colored noise based on α stable process and the sensing method is improved fractional low-order moment (FLOM) detection algorithm with balance parameter. First, we establish the non-Gaussian colored noise model through combining α-distribution with a linear system represented by a matrix. And a fitting curve of practical noise data is given to verify the validity of the proposed model. Then we present a parameter estimation method with low complexity to obtain the balance parameter, which is an important part of the detection algorithm. The balance parameter-based FLOM (BP-FLOM) detector does not require any a priori knowledge about the primary user signal and channels. Monte Carlo simulations clearly demonstrate the performance of the proposed method versus the generalized signal-to-noise ratio, the characteristic exponent α, and the number of detectors in sensing networks.
In this paper, a multi-carrier cognitive decision engine based on a binary particle swarm optimization with a non-linear decreasing inertia-weight (NDI-BPSO) is presented. Our main goal is to solve the optimization problem of transmitter parameters in different wireless communication modes for cognitive radio systems (CRSs), especially for the transmitter in communication systems based on the environment sensing. In the new algorithm, the multi-carrier cognitive decision engine based on an NDI-BPSO algorithm can mitigate the local extreme points effectively and reduce the oscillation phenomenon in the process of optimization. We apply the NDI-BPSO to the cognitive orthogonal frequency division multiplexing (OFDM) system to determine the best parameters to obtain good performances in different communication modes. The simulation results show that the proposed multi-objective cognitive decision engine, which has a high fitness value and strong robustness for different communication modes, is better than the existing engines. The novel NDI-BPSO algorithm achieves the objective of parameter optimization effectively.
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