A survey of recent advances in optimization methods for wireless communications
Mathematical optimization is now widely regarded as an indispensable modeling and
solution tool for the design of wireless communications systems. While optimization has …
solution tool for the design of wireless communications systems. While optimization has …
A review of sparse recovery algorithms
EC Marques, N Maciel, L Naviner, H Cai, J Yang - IEEE access, 2018 - ieeexplore.ieee.org
Nowadays, a large amount of information has to be transmitted or processed. This implies
high-power processing, large memory density, and increased energy consumption. In …
high-power processing, large memory density, and increased energy consumption. In …
Channel estimation for RIS-empowered multi-user MISO wireless communications
Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-
efficient solution for future wireless networks due to their fast and low-power configuration …
efficient solution for future wireless networks due to their fast and low-power configuration …
AMP-inspired deep networks for sparse linear inverse problems
M Borgerding, P Schniter… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Deep learning has gained great popularity due to its widespread success on many inference
problems. We consider the application of deep learning to the sparse linear inverse …
problems. We consider the application of deep learning to the sparse linear inverse …
AMP-Net: Denoising-based deep unfolding for compressive image sensing
Most compressive sensing (CS) reconstruction methods can be divided into two categories,
ie model-based methods and classical deep network methods. By unfolding the iterative …
ie model-based methods and classical deep network methods. By unfolding the iterative …
Grant-free massive MTC-enabled massive MIMO: A compressive sensing approach
A key challenge of massive MTC (mMTC), is the joint detection of device activity and
decoding of data. The sparse characteristics of mMTC makes compressed sensing (CS) …
decoding of data. The sparse characteristics of mMTC makes compressed sensing (CS) …
Orthogonal amp
Approximate message passing (AMP) is a low-cost iterative signal recovery algorithm for
linear system models. When the system transform matrix has independent identically …
linear system models. When the system transform matrix has independent identically …
Channel estimation in broadband millimeter wave MIMO systems with few-bit ADCs
We develop a broadband channel estimation algorithm for millimeter wave (mmWave)
multiple input multiple output (MIMO) systems with few-bit analog-to-digital converters …
multiple input multiple output (MIMO) systems with few-bit analog-to-digital converters …
A unifying tutorial on approximate message passing
Over the last decade or so, Approximate Message Passing (AMP) algorithms have become
extremely popular in various structured high-dimensional statistical problems. Although the …
extremely popular in various structured high-dimensional statistical problems. Although the …
Learned D-AMP: Principled neural network based compressive image recovery
Compressive image recovery is a challenging problem that requires fast and accurate
algorithms. Recently, neural networks have been applied to this problem with promising …
algorithms. Recently, neural networks have been applied to this problem with promising …