Non-Gaussian Colored Noise Generation for Wireless Channel Simulation with Particle Swarm Optimizer

Random Variable (RV) with different Probability Density Function (PDF) and Power Spectral Density (PSD) is a critical component for simulation of different wireless channel fading profile. To get a specific PSD for simulation of different multi-path scenario, the usual method is to pass a white noise through a filter with the required shape. But the filtering process will cause the change of random variable’s PDF unless the input noise follows Gaussian Distribution. In this paper, a Particle Swarm optimization (PSO) based method to generate NonGaussian noise by a pre-distortion filter and Inverse Transform Sampling (ITS) that meets both the requirement of PSD and PDF is described. As the solution is based on filtering, after the filter weight is found using PSO, the simulation could be carried out in a real-time manner compared to block-based methods. The numerical simulation confirms that it can generate the required PDF and more than 90% similar to the required PSD

Adaptive broadband wireless interference mitigation architecture applying higher order statistics algorithm for computer platform

The broadband wireless interference in a computer platform is resulted by multiple electro-magnetic emission sources. This non-Gaussian interference is proved to be double-sided K-distributed in previous research. With the limitation of transmission power and dimension of the device, interference mitigation is an efficient way to improve received signal bit error rate (BER). When applied on the double-sided K-distributed interference in the presence of Gaussian noise, traditional interference/noise cancellation schemes are not able to produce satisfactory results. In this thesis, our target is to find an interference mitigation method with improved BER performance. By introducing a new criterion of goodness, i.e. the cross-cumulant, the new adaptive algorithm based on higher order statistics (HOS) is designed to reconstruct and to cancel the interference in a recursive fashion. It is proved to be effective on both experimental binary transmission system and the OFDM system which is widely applied in modern mobile communication devices. Compared to the previous cancellation method, the BER performance is improved considerably

Representation and generation of non-gaussian wide-sense stationary random processes with arbitrary psds and a class of PDFs

A new method for representing and generating realizations of a wide-sense stationary non-Gaussian random process is described. The representation allows one to independently specify the power spectral density and the first-order probability density function of the random process. The only proviso is that the probability density function must be symmetric and infinitely divisible. The method proposed models the sinusoidal component frequencies as random variables, a key departure from the usual representation a of wide-sense stationary random process by the spectral theorem. Ergodicity in the mean and autocorrelation is also proven, under certain conditions. An example is given to illustrate its application to the K distribution, which is important in many physical modeling problems in radar and sonar. © 2010 IEEE