14 research outputs found
Signal Recovery From 1-Bit Quantized Noisy Samples via Adaptive Thresholding
In this paper, we consider the problem of signal recovery from 1-bit noisy
measurements. We present an efficient method to obtain an estimation of the
signal of interest when the measurements are corrupted by white or colored
noise. To the best of our knowledge, the proposed framework is the pioneer
effort in the area of 1-bit sampling and signal recovery in providing a unified
framework to deal with the presence of noise with an arbitrary covariance
matrix including that of the colored noise. The proposed method is based on a
constrained quadratic program (CQP) formulation utilizing an adaptive
quantization thresholding approach, that further enables us to accurately
recover the signal of interest from its 1-bit noisy measurements. In addition,
due to the adaptive nature of the proposed method, it can recover both fixed
and time-varying parameters from their quantized 1-bit samples.Comment: This is a pre-print version of the original conference paper that has
been accepted at the 2018 IEEE Asilomar Conference on Signals, Systems, and
Computer
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
Joint Optimization of Waveform Covariance Matrix and Antenna Selection for MIMO Radar
In this paper, we investigate the problem of jointly optimizing the waveform
covariance matrix and the antenna position vector for
multiple-input-multiple-output (MIMO) radar systems to approximate a desired
transmit beampattern as well as to minimize the cross-correlation of the
received signals reflected back from the targets. We formulate the problem as a
non-convex program and then propose a cyclic optimization approach to
efficiently tackle the problem. We further propose a novel local optimization
framework in order to efficiently design the corresponding antenna positions.
Our numerical investigations demonstrate a good performance both in terms of
accuracy and computational complexity, making the proposed framework a good
candidate for real-time radar signal processing applications.Comment: This paper is accepted for publication in the 2019 IEEE Asilomar
Conference on Signals, Systems, and Computers (Asilomar 2019
One-Bit Compressive Sensing: Can We Go Deep and Blind?
One-bit compressive sensing is concerned with the accurate recovery of an
underlying sparse signal of interest from its one-bit noisy measurements. The
conventional signal recovery approaches for this problem are mainly developed
based on the assumption that an exact knowledge of the sensing matrix is
available. In this work, however, we present a novel data-driven and
model-based methodology that achieves blind recovery; i.e., signal recovery
without requiring the knowledge of the sensing matrix. To this end, we make use
of the deep unfolding technique and develop a model-driven deep neural
architecture which is designed for this specific task. The proposed deep
architecture is able to learn an alternative sensing matrix by taking advantage
of the underlying unfolded algorithm such that the resulting learned recovery
algorithm can accurately and quickly (in terms of the number of iterations)
recover the underlying compressed signal of interest from its one-bit noisy
measurements. In addition, due to the incorporation of the domain knowledge and
the mathematical model of the system into the proposed deep architecture, the
resulting network benefits from enhanced interpretability, has a very small
number of trainable parameters, and requires very small number of training
samples, as compared to the commonly used black-box deep neural network
alternatives for the problem at hand.Comment: IEEE SIGNAL PROCESSING LETTERS,202