Search Results for author:
Found 31 papers, 6 papers with code
Communication-Efficient Federated Learning via Regularized Sparse Random Networks
In this setting, a binary mask is optimized instead of the model weights, which are kept fixed.
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks
Deploying teams of unmanned aerial vehicles (UAVs) to harvest data from distributed Internet of Things (IoT) devices requires efficient trajectory planning and coordination algorithms.
Revisiting Matching Pursuit: Beyond Approximate Submodularity
We study the problem of selecting a subset of vectors from a large set, to obtain the best signal representation over a family of functions.
User-Centric Federated Learning: Trading off Wireless Resources for Personalization
Statistical heterogeneity across clients in a Federated Learning (FL) system increases the algorithm convergence time and reduces the generalization performance, resulting in a large communication overhead in return for a poor model.
Channel Reuse for Backhaul in UAV Mobile Networks with User QoS Guarantee
In this paper, we study the problem of sum downlink capacity maximization in FlyBS-assisted networks with mobile users and with a consideration of wireless backhaul with channel reuse while a minimum required capacity to every user is guaranteed.
QoS-Aware Sum Capacity Maximization for Mobile Internet of Things Devices Served by UAVs
The use of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) is considered as an effective tool to improve performance of the mobile networks.
Sum Capacity Maximization in Multi-Hop Mobile Networks with Flying Base Stations
To this end, we propose an analytical approach based on an alternating optimization of the FlyBSs' 3D positions as well as the association of the users to the FlyBSs over time.
Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications
In this context, we explore the application of the framework of robust Bayesian learning.
UAV-Aided Multi-Community Federated Learning
We propose a heuristic metric as a proxy for the training performance of the different tasks.
Communication-Efficient Distributionally Robust Decentralized Learning
Decentralized learning algorithms empower interconnected devices to share data and computational resources to collaboratively train a machine learning model without the aid of a central coordinator.
UAV-aided Wireless Node Localization Using Hybrid Radio Channel Models
With this model and a set of offline RSS measurements, the unknown parameters are estimated.
UAV-aided RF Mapping for Sensing and Connectivity in Wireless Networks
The use of unmanned aerial vehicles (UAV) as flying radio access network (RAN) nodes offers a promising complement to traditional fixed terrestrial deployments.
Robust PAC$^m$: Training Ensemble Models Under Misspecification and Outliers
Standard Bayesian learning is known to have suboptimal generalization capabilities under misspecification and in the presence of outliers.
UAV-Aided Decentralized Learning over Mesh Networks
Decentralized learning empowers wireless network devices to collaboratively train a machine learning (ML) model relying solely on device-to-device (D2D) communication.
Joint Vehicular Localization and Reflective Mapping Based on Team Channel-SLAM
This approach exploits an initial (e. g. GPS-based) vehicle position information and allows subsequent tracking of vehicles by exploiting the shared nature of virtual transmitters associated to the reflecting surfaces.
User-Centric Federated Learning
Data heterogeneity across participating devices poses one of the main challenges in federated learning as it has been shown to greatly hamper its convergence time and generalization capabilities.
Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance
Reliable pedestrian crash avoidance mitigation (PCAM) systems are crucial components of safe autonomous vehicles (AVs).
LIDAR and Position-Aided mmWave Beam Selection with Non-local CNNs and Curriculum Training
Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high user mobility.
Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks
Deep Reinforcement Learning (DRL) is gaining attention as a potential approach to design trajectories for autonomous unmanned aerial vehicles (UAV) used as flying access points in the context of cellular or Internet of Things (IoT) connectivity.
Vehicle Localization via Cooperative Channel Mapping
Our approach builds on the recently proposed Channel-SLAM method which first enabled leveraging of multi-path so as to improve (single) vehicle positioning.
D2D-Aided Multi-Antenna Multicasting under Generalized CSIT
Multicasting, where a base station (BS) wishes to convey the same message to several user equipments (UEs), represents a common yet highly challenging wireless scenario.
Information Theory Information Theory
User Coordination for Fast Beam Training in FDD Multi-User Massive MIMO
Massive multiple-input multiple-output (mMIMO) communications are one of the enabling technologies of 5G and beyond networks.
Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning
Harvesting data from distributed Internet of Things (IoT) devices with multiple autonomous unmanned aerial vehicles (UAVs) is a challenging problem requiring flexible path planning methods.
UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning
Path planning methods for autonomous unmanned aerial vehicles (UAVs) are typically designed for one specific type of mission.
Team Deep Mixture of Experts for Distributed Power Control
In the context of wireless networking, it was recently shown that multiple DNNs can be jointly trained to offer a desired collaborative behaviour capable of coping with a broad range of sensing uncertainties.
UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach
Autonomous deployment of unmanned aerial vehicles (UAVs) supporting next-generation communication networks requires efficient trajectory planning methods.
Submodularity in Action: From Machine Learning to Signal Processing Applications
We introduce a variety of submodular-friendly applications, and elucidate the relation of submodularity to convexity and concavity which enables efficient optimization.
Decentralizing Multi-Operator Cognitive Radio Resource Allocation: An Asymptotic Analysis
We address the problem of resource allocation (RA) for spectrum underlay in a cognitive radio (CR) communication system with multiple secondary operators sharing resource with an incumbent primary operator.
Distributed Resource Allocation Algorithms for Multi-Operator Cognitive Communication Systems
We address the problem of resource allocation (RA) in a cognitive radio (CR) communication system with multiple secondary operators sharing spectrum with an incumbent primary operator.
UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning
Coverage path planning (CPP) is the task of designing a trajectory that enables a mobile agent to travel over every point of an area of interest.
Robotics Systems and Control Systems and Control
Machine Learning in the Air
Thanks to the recent advances in processing speed and data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner.
