1,405 research outputs found
Joint Scattering Environment Sensing and Channel Estimation Based on Non-stationary Markov Random Field
This paper considers an integrated sensing and communication system, where
some radar targets also serve as communication scatterers. A location domain
channel modeling method is proposed based on the position of targets and
scatterers in the scattering environment, and the resulting radar and
communication channels exhibit a two-dimensional (2-D) joint burst sparsity. We
propose a joint scattering environment sensing and channel estimation scheme to
enhance the target/scatterer localization and channel estimation performance
simultaneously, where a spatially non-stationary Markov random field (MRF)
model is proposed to capture the 2-D joint burst sparsity. An expectation
maximization (EM) based method is designed to solve the joint estimation
problem, where the E-step obtains the Bayesian estimation of the radar and
communication channels and the M-step automatically learns the dynamic position
grid and prior parameters in the MRF. However, the existing sparse Bayesian
inference methods used in the E-step involve a high-complexity matrix inverse
per iteration. Moreover, due to the complicated non-stationary MRF prior, the
complexity of M-step is exponentially large. To address these difficulties, we
propose an inverse-free variational Bayesian inference algorithm for the E-step
and a low-complexity method based on pseudo-likelihood approximation for the
M-step. In the simulations, the proposed scheme can achieve a better
performance than the state-of-the-art method while reducing the computational
overhead significantly.Comment: 15 pages, 13 figures, submitted to IEEE Transactions on Wireless
Communication
Large-scale Heteroscedastic Regression via Gaussian Process
Heteroscedastic regression considering the varying noises among observations
has many applications in the fields like machine learning and statistics. Here
we focus on the heteroscedastic Gaussian process (HGP) regression which
integrates the latent function and the noise function together in a unified
non-parametric Bayesian framework. Though showing remarkable performance, HGP
suffers from the cubic time complexity, which strictly limits its application
to big data. To improve the scalability, we first develop a variational sparse
inference algorithm, named VSHGP, to handle large-scale datasets. Furthermore,
two variants are developed to improve the scalability and capability of VSHGP.
The first is stochastic VSHGP (SVSHGP) which derives a factorized evidence
lower bound, thus enhancing efficient stochastic variational inference. The
second is distributed VSHGP (DVSHGP) which (i) follows the Bayesian committee
machine formalism to distribute computations over multiple local VSHGP experts
with many inducing points; and (ii) adopts hybrid parameters for experts to
guard against over-fitting and capture local variety. The superiority of DVSHGP
and SVSHGP as compared to existing scalable heteroscedastic/homoscedastic GPs
is then extensively verified on various datasets.Comment: 14 pages, 15 figure
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