Proceedings ArticleDOI
Joint Sparse Recovery Using Deep Unfolding With Application to Massive Random Access
Anand P. Sabulal,Srikrishna Bhashyam +1 more
- pp 5050-5054
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TLDR
Numerical results for a massive random access system show that the proposed modification to the MMV-ADM method and deep unfolding provide significant improvement in convergence and estimation performance.Abstract:
We propose a learning-based joint sparse recovery method for the multiple measurement vector (MMV) problem using deep unfolding. We unfold an iterative alternating direction method of multipliers (ADM) algorithm for MMV joint sparse recovery algorithm into a trainable deep network. This ADM algorithm is first obtained by modifying the squared error penalty function of an existing ADM algorithm to a back-projected squared error penalty function. Numerical results for a massive random access system show that our proposed modification to the MMV-ADM method and deep unfolding provide significant improvement in convergence and estimation performance.read more
Citations
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Proceedings ArticleDOI
FASURA: A Scheme for Quasi-Static Massive MIMO Unsourced Random Access Channels
TL;DR: This article considers the massive MIMO unsourced random access problem on a quasi-static Rayleigh fading channel and shows that an appropriate combination of these ideas can substantially outperform state-of-the-art coding schemes when the number of active users is more than 100, making this the best performing scheme known for this regime.
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Proceedings ArticleDOI
ADMM-DAD Net: A Deep Unfolding Network for Analysis Compressed Sensing
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Model-Based Deep Learning for Joint Activity Detection and Channel Estimation in Massive and Sporadic Connectivity
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References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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Learning Fast Approximations of Sparse Coding
Karol Gregor,Yann LeCun +1 more
TL;DR: Two versions of a very fast algorithm that produces approximate estimates of the sparse code that can be used to compute good visual features, or to initialize exact iterative algorithms are proposed.
Journal ArticleDOI
Massive Connectivity With Massive MIMO—Part I: Device Activity Detection and Channel Estimation
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Journal ArticleDOI
AMP-Inspired Deep Networks for Sparse Linear Inverse Problems
TL;DR: This paper proposes two novel neural-network architectures that decouple prediction errors across layers in the same way that the approximate message passing (AMP) algorithms decouple them across iterations: through Onsager correction.
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