992 research outputs found
Deep Signal Recovery with One-Bit Quantization
Machine learning, and more specifically deep learning, have shown remarkable
performance in sensing, communications, and inference. In this paper, we
consider the application of the deep unfolding technique in the problem of
signal reconstruction from its one-bit noisy measurements. Namely, we propose a
model-based machine learning method and unfold the iterations of an inference
optimization algorithm into the layers of a deep neural network for one-bit
signal recovery. The resulting network, which we refer to as DeepRec, can
efficiently handle the recovery of high-dimensional signals from acquired
one-bit noisy measurements. The proposed method results in an improvement in
accuracy and computational efficiency with respect to the original framework as
shown through numerical analysis.Comment: This paper has been submitted to the 44th International Conference on
Acoustics, Speech, and Signal Processing (ICASSP 2019
Noisy Tensor Completion for Tensors with a Sparse Canonical Polyadic Factor
In this paper we study the problem of noisy tensor completion for tensors
that admit a canonical polyadic or CANDECOMP/PARAFAC (CP) decomposition with
one of the factors being sparse. We present general theoretical error bounds
for an estimate obtained by using a complexity-regularized maximum likelihood
principle and then instantiate these bounds for the case of additive white
Gaussian noise. We also provide an ADMM-type algorithm for solving the
complexity-regularized maximum likelihood problem and validate the theoretical
finding via experiments on synthetic data set
Quantized Radio Map Estimation Using Tensor and Deep Generative Models
Spectrum cartography (SC), also known as radio map estimation (RME), aims at
crafting multi-domain (e.g., frequency and space) radio power propagation maps
from limited sensor measurements. While early methods often lacked theoretical
support, recent works have demonstrated that radio maps can be provably
recovered using low-dimensional models -- such as the block-term tensor
decomposition (BTD) model and certain deep generative models (DGMs) -- of the
high-dimensional multi-domain radio signals. However, these existing provable
SC approaches assume that sensors send real-valued (full-resolution)
measurements to the fusion center, which is unrealistic. This work puts forth a
quantized SC framework that generalizes the BTD and DGM-based SC to scenarios
where heavily quantized sensor measurements are used. A maximum likelihood
estimation (MLE)-based SC framework under a Gaussian quantizer is proposed.
Recoverability of the radio map using the MLE criterion are characterized under
realistic conditions, e.g., imperfect radio map modeling and noisy
measurements. Simulations and real-data experiments are used to showcase the
effectiveness of the proposed approach.Comment: 16 pages, 9 figure
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