Power Allocation for Beamforming Relay Networks under Channel Uncertainties

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

The training of neural networks by gradient descent methods is a cornerstone of the deep learning revolution. Yet, despite some recent progress, a complete theory explaining its success is still missing. This article presents, for orthogonal input vectors, ...

Background: Signal processing tools are required to efficiently analyze data collected in body-surface-potential map (BSPM) recordings. A limited number of such tools exist for studying persistent atrial fibrillation (persAF). We propose two novel, spatiot ...

This paper introduces a new method for solving the distributed AC power flow (PF) problem by further exploiting the problem formulation. We propose a new variant of the ALADIN algorithm devised specifically for this type of problem. This new variant is cha ...

Variance-reduced gradient estimators for policy gradient methods have been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance-reduced policy-gradient m ...

The use of model-based numerical simulations of wave propagation in rooms for engineering applications requires that acoustic conditions for multiple parameters are evaluated iteratively, which is computationally expensive. We present a reduced basis metho ...

This paper considers an additive Gaussian noise channel with arbitrarily distributed finite variance input signals. It studies the differential entropy of the minimum mean-square error (MMSE) estimator and provides a new lower bound which connects the diff ...

Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation of reliable design solutions difficult. We propose an algorithm fo ...

Least squares estimators, when trained on a few target domain samples, may predict poorly. Supervised domain adaptation aims to improve the predictive accuracy by exploiting additional labeled training samples from a source distribution that is close to th ...

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real ...

There has been a recent surge in methods targeting the recovery of a speed-of-sound map from pulse-echo ultrasound measurements. We focused in this work on a particular technique and identified a drawback shared by similar methods - namely the necessity of ...

The control of robotic prosthetic hands (RPHs) for upper limb amputees is far from optimal. Simultaneous and proportional finger control of a RPH based on EMG signals is still challenging. Based on EMG and kinematics recordings of subjects following a pre- ...

Power Allocation for Beamforming Relay Networks under Channel Uncertainties

Chat with Graph Search

Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs

Novel spatiotemporal processing tools for body-surface potential map signals for the prediction of catheter ablation outcome in persistent atrial fibrillation

Rapid Scalable Distributed Power Flow with Open-Source Implementation

Momentum-Based Policy Gradient with Second-Order Information

Reduced basis methods for numerical room acoustic simulations with parametrized boundaries

Differential Entropy of the Conditional Expectation under Gaussian Noise

Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models

Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Directional Cross-Correlation for Improved Aberration Phase Estimation in Pulse-Echo Speed-of-Sound Imaging

Deep Learning with Convolutional Neural Network for Proportional Control of Finger Movements from surface EMG Recordings