Search Results for author:
Found 19 papers, 9 papers with code
Semantic Communication for Cooperative Multi-Task Processing over Wireless Networks
In this paper, we have expanded the current status of semantic communication limited to processing one task to a more general system that can handle multiple tasks concurrently.
Flexible Robust Beamforming for Multibeam Satellite Downlink using Reinforcement Learning
Low Earth Orbit (LEO) satellite-to-handheld connections herald a new era in satellite communications.
Scheduling for On-Board Federated Learning with Satellite Clusters
Mega-constellations of small satellites have evolved into a source of massive amount of valuable data.
Model-free Reinforcement Learning of Semantic Communication by Stochastic Policy Gradient
Following the recent success of Machine Learning tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention.
A Multi-Task Approach to Robust Deep Reinforcement Learning for Resource Allocation
With increasing complexity of modern communication systems, machine learning algorithms have become a focal point of research.
On the Importance of Exploration for Real Life Learned Algorithms
The quality of data driven learning algorithms scales significantly with the quality of data available.
Robust Deep Reinforcement Learning Scheduling via Weight Anchoring
Questions remain on the robustness of data-driven learning methods when crossing the gap from simulation to reality.
Learning Resource Scheduling with High Priority Users using Deep Deterministic Policy Gradients
Advances in mobile communication capabilities open the door for closer integration of pre-hospital and in-hospital care processes.
Learning Model-Free Robust Precoding for Cooperative Multibeam Satellite Communications
Direct Low Earth Orbit satellite-to-handheld links are expected to be part of a new era in satellite communications.
Energy and Bandwidth Efficiency of Event-Based Communication
Wireless sensor nodes need a drastically reduced technical complexity to fit constraints of future applications.
Robust Precoding via Characteristic Functions for VSAT to Multi-Satellite Uplink Transmission
In case of imperfect position knowledge, the performance degradation of the robust precoder is relatively small.
Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellations
Distributed training of machine learning models directly on satellites in low Earth orbit (LEO) is considered.
Federated Learning in Satellite Constellations
Federated learning (FL) has recently emerged as a distributed machine learning paradigm for systems with limited and intermittent connectivity.
Semantic Information Recovery in Wireless Networks
Thus, we model semantics by means of hidden random variables and define the semantic communication task as the data-reduced and reliable transmission of messages over a communication channel such that semantics is best preserved.
Beamspace MIMO for Satellite Swarms
In this paper, we propose a distributed linear precoding scheme and a GS equalizer relying on local position information.
On-Board Federated Learning for Dense LEO Constellations
Mega-constellations of small-size Low Earth Orbit (LEO) satellites are currently planned and deployed by various private and public entities.
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical Care
Greater capabilities of mobile communications technology enable interconnection of on-site medical care at a scale previously unavailable.
Ground-Assisted Federated Learning in LEO Satellite Constellations
In Low Earth Orbit (LEO) mega constellations, there are relevant use cases, such as inference based on satellite imaging, in which a large number of satellites collaboratively train a machine learning model without sharing their local datasets.
CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection with Low Complexity
Following the great success of Machine Learning (ML), especially Deep Neural Networks (DNNs), in many research domains in 2010s, several ML-based approaches were proposed for detection in large inverse linear problems, e. g., massive MIMO systems.
