White noise

About: White noise is a research topic. Over the lifetime, 16496 publications have been published within this topic receiving 318633 citations.

Channel estimation for OFDM systems based on comb-type pilot arrangement in frequency selective fading channels

Abstract: The channel estimation methods for OFDM systems based on a comb-type pilot sub-carrier arrangement are investigated. The channel estimation algorithm based on comb-type pilots is divided into pilot signal estimation and channel interpolation. The pilot signal estimation based on LS or MMSE criteria, together with channel interpolation based on piecewise-linear interpolation or piecewise second-order polynomial interpolation is studied. Owing to the MMSE estimate of the pilot signals, the inter-carrier interference and additive white Gaussian noise are reduced considerably. The computational complexity of pilot signal estimation based on MMSE criterion can be reduced by using a simplified LMMSE estimator with low-rank approximation using singular value decomposition. Phase compensators before and after interpolation are also presented to combat the phase changes of subchannel symbols arising from the frame synchronization errors. Compared to the transform-domain processing based channel estimation algorithm the MMSE estimate of pilot signals together with phase compensated linear interpolation algorithm provides a better performance and requires less computations.

A generalization of median filtering using linear combinations of order statistics

Abstract: We consider a class of nonlinear filters whose output is given by a linear combination of the order statistics of the input sequence. Assuming a constant signal in white noise, the coefficients in the linear combination are chosen to minimize the output MSE for several noise distributions. It is shown that the optimal order statistic filter (OSF) tends toward the median filter as the noise becomes more impulsive. The optimal OSF is applied to an actual noisy image and is shown to perform well, combining properties of both the averaging and median filters. A more general design scheme for applications involving nonconstant signals is also given.

The effect of red noise on planetary transit detection

Abstract: Since the discovery of short-period exoplanets a decade ago, photometric surveys have been recognized as a feasible method to detect transiting hot Jupiters. Many transit surveys are now underway, with instruments ranging from 10-cm cameras to the Hubble Space Telescope. However, the results of these surveys have been much below the expected capacity, estimated in the dozens of detections per year. One of the reasons is the presence of systematics (‘red noise’) in photometric time-series. In general, yield predictions assume uncorrelated noise (‘white noise’). In this paper, we show that the effect of red noise on the detection threshold and the expected yields cannot be neglected in typical ground-based surveys. We develop a simple method to determine the effect of red noise on photometric planetary transit detections. This method can be applied to determine detection thresholds for transit surveys. We show that the detection threshold in the presence of systematics can be much higher than that with the assumption of white noise, and obeys a different dependence on magnitude, orbital period and the parameters of the survey. Our method can also be used to estimate the significance level of a planetary transit candidate (to select promising candidates for spectroscopic follow-up). We apply our method to the OGLE planetary transit search, and show that it provides a reliable description of the actual detection threshold with real correlated noise. We point out in what way the presence of red noise could be at least partly responsible for the dearth of transiting planet detections from existing surveys, and examine some possible adaptations in survey planning and strategy. Finally, we estimate the photometric stability necessary to the detection of transiting ‘hot Neptunes’.

An introduction to long-memory time series models and fractional differencing

Abstract: The idea of fractional differencing is introduced in terms of the infinite filter that corresponds to the expansion of (1 - B)d. When the filter is applied to white noise, a class of time series is generated with distinctive properties, particularly in the very low frequencies and provides potentially useful long-memory forecasting properties. Such models are shown possibly to arise from aggregation of independent components. Generation and estimation of these models are considered and applications on generated and real data presented.