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Found 6 papers, 2 papers with code
Learning to Perform Downlink Channel Estimation in Massive MIMO Systems
The second one is a deep-learning-based approach that uses a neural network to identify a mapping between the available information and the effective channel gain.
On the Performance of Image Recovery in Massive MIMO Communications
Massive MIMO (Multiple Input Multiple Output) has demonstrated as a potential candidate for 5G-and-beyond wireless networks.
Information Theory Information Theory
Power Control in Cellular Massive MIMO with Varying User Activity: A Deep Learning Solution
We first consider the sum spectral efficiency (SE) optimization problem for systems with a dynamically varying number of active users.
Information Theory Information Theory
Large-Scale-Fading Decoding in Cellular Massive MIMO Systems with Spatially Correlated Channels
The numerical results show that both data power control and LSFD improves the sum SE performance over single-layer decoding multi-cell Massive MIMO systems.
Information Theory Information Theory
Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation
Although block compressive sensing (BCS) makes it tractable to sense large-sized images and video, its recovery performance has yet to be significantly improved because its recovered images or video usually suffer from blurred edges, loss of details, and high-frequency oscillatory artifacts, especially at a low subrate.
Total variation reconstruction for compressive sensing using nonlocal Lagrangian multiplier
Total variation has proved its effectiveness in solving inverse problems for compressive sensing.
