3,938 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
A decomposition algorithm for two-stage stochastic programs with nonconvex recourse
In this paper, we have studied a decomposition method for solving a class of
nonconvex two-stage stochastic programs, where both the objective and
constraints of the second-stage problem are nonlinearly parameterized by the
first-stage variable. Due to the failure of the Clarke regularity of the
resulting nonconvex recourse function, classical decomposition approaches such
as Benders decomposition and (augmented) Lagrangian-based algorithms cannot be
directly generalized to solve such models. By exploring an implicitly
convex-concave structure of the recourse function, we introduce a novel
decomposition framework based on the so-called partial Moreau envelope. The
algorithm successively generates strongly convex quadratic approximations of
the recourse function based on the solutions of the second-stage convex
subproblems and adds them to the first-stage master problem. Convergence under
both fixed scenarios and interior samplings is established. Numerical
experiments are conducted to demonstrate the effectiveness of the proposed
algorithm
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