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Found 8 papers, 1 papers with code
Energy-Saving Cell-Free Massive MIMO Precoders with a Per-AP Wideband Kronecker Channel Model
We study cell-free massive multiple-input multiple-output precoders that minimize the power consumed by the power amplifiers subject to per-user per-subcarrier rate constraints.
Deep Unfolding for Fast Linear Massive MIMO Precoders under a PA Consumption Model
Massive multiple-input multiple-output (MIMO) precoders are typically designed by minimizing the transmit power subject to a quality-of-service (QoS) constraint.
Self-Supervised Learning of Linear Precoders under Non-Linear PA Distortion for Energy-Efficient Massive MIMO Systems
Massive multiple input multiple output (MIMO) systems are typically designed under the assumption of linear power amplifiers (PAs).
Floor Map Reconstruction Through Radio Sensing and Learning By a Large Intelligent Surface
Environmental scene reconstruction is of great interest for autonomous robotic applications, since an accurate representation of the environment is necessary to ensure safe interaction with robots.
User Clustering for Rate Splitting using Machine Learning
Hierarchical Rate Splitting (HRS) schemes proposed in recent years have shown to provide significant improvements in exploiting spatial diversity in wireless networks and provide high throughput for all users while minimising interference among them.
Exclusive Group Lasso for Structured Variable Selection
It is also shown that such an algorithm can be tailored to match more rigid structures than plain exclusive group sparsity.
Probability of Resolution of MUSIC and g-MUSIC: An Asymptotic Approach
Furthermore, this CLT is used to provide an accurate prediction of the resolution capabilities of the MUSIC and the g-MUSIC DoA estimation method.
On the Resolution Probability of Conditional and Unconditional Maximum Likelihood DoA Estimation
The objective of this paper is to characterize the resolution capabilities of ML algorithms in the threshold region.
