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Found 23 papers, 3 papers with code
Energy-Efficient Cellular-Connected UAV Swarm Control Optimization
To maximize the number of UAVs that receive the message successfully within the latency and energy constraints, we formulate the problem as a Constrained Markov Decision Process to find the optimal policy.
Federated and Meta learning over Non-Wireless and Wireless Networks: A Tutorial
Typically, FL focuses on learning a global model for a given task and all devices and hence cannot adapt the model to devices with different data distributions.
Task-Oriented and Semantics-Aware 6G Networks
In this article, we propose a generic task-oriented and semantics-aware (TOSA) communication framework for various tasks with diverse data types, which incorporates both semantic level information and effectiveness level performance metrics.
Time-triggered Federated Learning over Wireless Networks
The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner.
Optimization of Grant-Free NOMA with Multiple Configured-Grants for mURLLC
To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs.
Scalable Multi-agent Reinforcement Learning Algorithm for Wireless Networks
This introduces the scalability challenges, while multi-agent reinforcement shows the opportunity to cope this challenges and extend the intelligent algorithm to cooperative large-scale network.
Learning-based Prediction, Rendering and Transmission for Interactive Virtual Reality in RIS-Assisted Terahertz Networks
This high data rate over short transmission distances may be achieved via abundant bandwidth in the terahertz (THz) band.
Machine Learning for Massive Industrial Internet of Things
With the deployment of massive IIoT devices, it is difficult for the wireless network to support the ubiquitous connections with diverse quality-of-service (QoS) requirements.
QoE Optimization for Live Video Streaming in UAV-to-UAV Communications via Deep Reinforcement Learning
In this paper, we co-design the video resolution, the movement, and the power control of UAV-BS and UAV-UEs to maximize the Quality of Experience (QoE) of real-time video streaming.
Analyzing Novel Grant-Based and Grant-Free Access Schemes for Small Data Transmission
Motivated by this, 3GPP has proposed 4/2-step SDT RA schemes based on the existing grant-based (4-step) and grant-free (2-step) RA schemes, with the aim to enable data transmission during RA procedures in Radio Resource Control (RRC) Inactive state.
Real-time Video Streaming and Control of Cellular-Connected UAV System: Prototype and Performance Evaluation
Unmanned aerial vehicles (UAVs) play an increasingly important role in military, public, and civilian applications, where providing connectivity to UAVs is crucial for its real-time control, video streaming, and data collection.
Learning based signal detection for MIMO systems with unknown noise statistics
Existing detection methods have mainly focused on specific noise models, which are not robust enough with unknown noise statistics.
Learning-based Prediction and Uplink Retransmission for Wireless Virtual Reality (VR) Network
While for the online learning algorithm, based on the VR user's actual viewpoint delivered through uplink transmission, we compare it with the predicted viewpoint and update the parameters of the online learning algorithm to further improve the prediction accuracy.
RACH in Self-Powered NB-IoT Networks: Energy Availability and Performance Evaluation
In this work, we analyze RACH success probability in a self-powered NB-IoT network taking into account the repeated preamble transmissions and collisions, where each IoT device with data is active when its battery energy is sufficient to support the transmission.
A Vision of XR-aided Teleoperation System Towards 5G/B5G
Extended Reality (XR)-aided teleoperation has shown its potential in improving operating efficiency in mission-critical, rich-information and complex scenarios.
Correlation-aware Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Deep Reinforcement Learning Approach
Our proposed learning architectures is effective and scalable for a high-dimensional cooperative association problem with increasing APs and VR users.
Analysis of Random Access in NB-IoT Networks with Three Coverage Enhancement Groups: A Stochastic Geometry Approach
NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks.
Low-Complexity Robust Beamforming Design for IRS-Aided MISO Systems with Imperfect Channels
In this paper, large-scale intelligent reflecting sur-face (IRS)-assisted multiple-input single-output (MISO) system is considered in the presence of channel uncertainty.
Learning-based Prediction, Rendering and Association Optimization for MEC-enabled Wireless Virtual Reality (VR) Network
Wireless-connected Virtual Reality (VR) provides immersive experience for VR users from any-where at anytime.
Analyzing Grant-Free Access for URLLC Service
Based on this framework, we define the latent access failure probability to characterize URLLC reliability and latency performances.
Cellular-Connected Wireless Virtual Reality: Requirements, Challenges, and Solutions
Cellular-connected wireless connectivity provides new opportunities for virtual reality(VR) to offer seamless user experience from anywhere at anytime.
Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing
In this paper, the beneficial role of IRSs is investigated in MEC systems, where single-antenna devices may opt for off-loading a fraction of their computational tasks to the edge computing node via a multi-antenna access point with the aid of an IRS.
Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge
To scale learning across geographic areas, a spatial transfer learning scheme is proposed to further promote the learning efficiency of distributed DRL entities by exploiting the traffic demand correlations.
