325 research outputs found
Jointly Sparse Support Recovery via Deep Auto-encoder with Applications in MIMO-based Grant-Free Random Access for mMTC
In this paper, a data-driven approach is proposed to jointly design the
common sensing (measurement) matrix and jointly support recovery method for
complex signals, using a standard deep auto-encoder for real numbers. The
auto-encoder in the proposed approach includes an encoder that mimics the noisy
linear measurement process for jointly sparse signals with a common sensing
matrix, and a decoder that approximately performs jointly sparse support
recovery based on the empirical covariance matrix of noisy linear measurements.
The proposed approach can effectively utilize the feature of common support and
properties of sparsity patterns to achieve high recovery accuracy, and has
significantly shorter computation time than existing methods. We also study an
application example, i.e., device activity detection in Multiple-Input
Multiple-Output (MIMO)-based grant-free random access for massive machine type
communications (mMTC). The numerical results show that the proposed approach
can provide pilot sequences and device activity detection with better detection
accuracy and substantially shorter computation time than well-known recovery
methods.Comment: 5 pages, 8 figures, to be publised in IEEE SPAWC 2020. arXiv admin
note: text overlap with arXiv:2002.0262
Deep Physics-Guided Unrolling Generalization for Compressed Sensing
By absorbing the merits of both the model- and data-driven methods, deep
physics-engaged learning scheme achieves high-accuracy and interpretable image
reconstruction. It has attracted growing attention and become the mainstream
for inverse imaging tasks. Focusing on the image compressed sensing (CS)
problem, we find the intrinsic defect of this emerging paradigm, widely
implemented by deep algorithm-unrolled networks, in which more plain iterations
involving real physics will bring enormous computation cost and long inference
time, hindering their practical application. A novel deep
hysics-guided unolled recovery earning
() framework is proposed by generalizing the traditional
iterative recovery model from image domain (ID) to the high-dimensional feature
domain (FD). A compact multiscale unrolling architecture is then developed to
enhance the network capacity and keep real-time inference speeds. Taking two
different perspectives of optimization and range-nullspace decomposition,
instead of building an algorithm-specific unrolled network, we provide two
implementations: and . Experiments exhibit
the significant performance and efficiency leading of PRL networks over other
state-of-the-art methods with a large potential for further improvement and
real application to other inverse imaging problems or optimization models.Comment: Accepted by International Journal of Computer Vision (IJCV) 202
REST: Robust lEarned Shrinkage-Thresholding Network Taming Inverse Problems with Model Mismatch
We consider compressive sensing problems with model mismatch where one wishes to recover a sparse high-dimensional vector from low-dimensional observations subject to uncertainty in the measurement operator. In particular, we design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem. Our proposed network –named Robust lErned Shrinkage-Thresholding (REST) –exhibits additional features including enlarged number of parameters and normalization processing compared to state-of-the-art deep architecture Learned Iterative Shrinkage-Thresholding Algorithm (LISTA), leading to the reliable recovery of the signal under sample-wise varying model mismatch. Our proposed network is also shown to outperform LISTA in compressive sensing problems under sample-wise varying model mismatch
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