4,790 research outputs found
Detection of multiplicative noise in stationary random processes using second- and higher order statistics
This paper addresses the problem of detecting the presence of colored multiplicative noise, when the information process can be modeled as a parametric ARMA process. For the case of zero-mean multiplicative noise, a cumulant based suboptimal detector is studied. This detector tests the nullity of a specific cumulant slice. A second detector is developed when the multiplicative noise is nonzero mean. This detector consists of filtering the data by an estimated AR filter. Cumulants of the residual data are then shown to be well suited to the detection problem. Theoretical expressions for the asymptotic probability of
detection are given. Simulation-derived finite-sample ROC curves are shown for different sets of model parameters
Multi-modal Blind Source Separation with Microphones and Blinkies
We propose a blind source separation algorithm that jointly exploits
measurements by a conventional microphone array and an ad hoc array of low-rate
sound power sensors called blinkies. While providing less information than
microphones, blinkies circumvent some difficulties of microphone arrays in
terms of manufacturing, synchronization, and deployment. The algorithm is
derived from a joint probabilistic model of the microphone and sound power
measurements. We assume the separated sources to follow a time-varying
spherical Gaussian distribution, and the non-negative power measurement
space-time matrix to have a low-rank structure. We show that alternating
updates similar to those of independent vector analysis and Itakura-Saito
non-negative matrix factorization decrease the negative log-likelihood of the
joint distribution. The proposed algorithm is validated via numerical
experiments. Its median separation performance is found to be up to 8 dB more
than that of independent vector analysis, with significantly reduced
variability.Comment: Accepted at IEEE ICASSP 2019, Brighton, UK. 5 pages. 3 figure
Free Probability based Capacity Calculation of Multiantenna Gaussian Fading Channels with Cochannel Interference
During the last decade, it has been well understood that communication over
multiple antennas can increase linearly the multiplexing capacity gain and
provide large spectral efficiency improvements. However, the majority of
studies in this area were carried out ignoring cochannel interference. Only a
small number of investigations have considered cochannel interference, but even
therein simple channel models were employed, assuming identically distributed
fading coefficients. In this paper, a generic model for a multi-antenna channel
is presented incorporating four impairments, namely additive white Gaussian
noise, flat fading, path loss and cochannel interference. Both point-to-point
and multiple-access MIMO channels are considered, including the case of
cooperating Base Station clusters. The asymptotic capacity limit of this
channel is calculated based on an asymptotic free probability approach which
exploits the additive and multiplicative free convolution in the R- and
S-transform domain respectively, as well as properties of the eta and Stieltjes
transform. Numerical results are utilized to verify the accuracy of the derived
closed-form expressions and evaluate the effect of the cochannel interference.Comment: 16 pages, 4 figures, 1 tabl
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