Modeling of non-Gaussian colored noise and application in CR multi-sensor networks

Abstract

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.

Task scheduling algorithm based on fireworks algorithm

“…, m. Let the location vector of fireworks or sparks be an mdimensional vector X i , as shown in Eq. (13). {1, 2, 3, .…”

“…Research on task scheduling strategies for heterogeneous multi-processors has drawn much attention in recent years [8][9][10]. At present, the combination optimization algorithm, which is widely used in task scheduling problem, is genetic algorithm (GA) [11][12][13]. Nevertheless, the parameters of genetic algorithm are complicated to configure, and the effectiveness of the crossover and mutation operations decreases when the number of tasks increases, and the "premature" phenomenon can be easily triggered by the population initialization of the individuals [12,[14][15][16][17][18].…”

Spectrum sensing for cognitive radio network with multiple receive antennas under impulsive noise environments

“…As a result, it is meaningful to consider non-Gaussian noise environment in the spectrum sensing in the CR. Examples of non-Gaussian impairments include man-made impulsive noise, heavy-tailed noise, and co-channel interference from other CRs [12]- [16]. When the noise distribution is non-Gaussian, it is well-known that detectors designed for AWGN do not perform adequately.…”

Performance Evaluation of Machine Learning in Wireless Connected Robotics Swarms

“…Wen et al [10] used the block orthogonal matching pursuit (BOMP) algorithm to recover block-sparse signals from measurements. More details about modulation recognition were presented in other previous works [11][12][13][14][15].…”