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OCD-FL: A Novel Communication-Efficient Peer Selection-based Decentralized Federated Learning
The conjunction of edge intelligence and the ever-growing Internet-of-Things (IoT) network heralds a new era of collaborative machine learning, with federated learning (FL) emerging as the most prominent paradigm.
Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data
Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others.
RL-Based Cargo-UAV Trajectory Planning and Cell Association for Minimum Handoffs, Disconnectivity, and Energy Consumption
Unmanned aerial vehicle (UAV) is a promising technology for last-mile cargo delivery.
Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning
Unmanned aerial vehicle (UAV)-enabled edge federated learning (FL) has sparked a rise in research interest as a result of the massive and heterogeneous data collected by UAVs, as well as the privacy concerns related to UAV data transmissions to edge servers.
Multi-UAV Speed Control with Collision Avoidance and Handover-aware Cell Association: DRL with Action Branching
This paper presents a deep reinforcement learning solution for optimizing multi-UAV cell-association decisions and their moving velocity on a 3D aerial highway.
FLSTRA: Federated Learning in Stratosphere
We propose a federated learning (FL) in stratosphere (FLSTRA) system, where a high altitude platform station (HAPS) facilitates a large number of terrestrial clients to collaboratively learn a global model without sharing the training data.
On the Design of Communication-Efficient Federated Learning for Health Monitoring
To tackle this problem, we propose in this paper a communication-efficient federated learning (CEFL) framework that involves clients clustering and transfer learning.
Multi-Mode High Altitude Platform Stations (HAPS) for Next Generation Wireless Networks
To tackle this issue, we envision the use of a multi-mode HAPS that can adaptively switch between different modes so as to reduce energy consumption and extend the HAPS loitering time.
UAV-based Crowd Surveillance in Post COVID-19 Era
Consequently, we propose in this paper a complete UAV framework for intelligent monitoring of post COVID-19 outdoor activities.
Optimization of Quantized Phase Shifts for Reconfigurable Smart Surfaces Assisted Communications
Reconfigurable Smart Surface (RSS) is assumed to be a key enabler for future wireless communication systems due to its ability to control the wireless propagation environment and, thus, enhance communications quality.
Disconnectivity-Aware Energy-Efficient Cargo-UAV Trajectory Planning with Minimum Handoffs
In this paper, we formulate the trajectory planning as a multi-objective problem aiming to minimize both the UAV's energy consumption and the handoff rate, constrained by the UAV battery size and disconnectivity rate.
HAPS-ITS: Enabling Future ITS Services in Trans-Continental Highways
To this end, high altitude platform station (HAPS) systems, due to their mobility, sustainability, payload capacity, and communication/caching/computing capabilities, are seen as a key enabler of future ITS services for trans-continental highways; this paradigm is referred to as HAPS-ITS.
3D Aerial Highway: The Key Enabler of the Retail Industry Transformation
Like highways for cargo trucks, 3D routes in the airspace should be designed for cargo-UAVs to fulfill their operations safely and efficiently.
Energy-Efficient Multi-UAV Data Collection for IoT Networks with Time Deadlines
In the first step, an efficient method is proposed to determine the minimal number of CHs and their best locations.
Dynamics of Laser-Charged UAVs: A Battery Perspective
Subsequently, using these models, the path planning problem in a particular Internet-of-Things based use-case is revisited from the battery perspective.
Link Budget Analysis for Reconfigurable Smart Surfaces in Aerial Platforms
Specifically, we analyze the characteristics of RSS-equipped aerial platforms and compare their communication performance with that of RSS-assisted terrestrial networks, using standardized channel models.
On the Optimal 3D Placement of a UAV Base Station for Maximal Coverage of UAV Users
Specifically, the 3D placement problem of a directional antenna equipped UAV-BS, aiming to maximize the number of covered aerial users under a spectrum sharing policy with terrestrial networks, is investigated.
Rate-Splitting Multiple Access: Unifying NOMA and SDMA in MISO VLC Channels
Then, we introduce rate-splitting multiple access (RSMA), which was initially proposed for RF systems and discuss its potentials in VLC systems.
Aerial Platforms with Reconfigurable Smart Surfaces for 5G and Beyond
In this article, we discuss comprehensive approaches to the integration of RSS in aerial platforms.
Multiple Access in Aerial Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting
In particular, space-division multiple access(SDMA) and power-domain non-orthogonal multiple access (NOMA) present promising multiplexing gains for aerial downlink and uplink.
