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Found 9 papers, 0 papers with code
Meta Reinforcement Learning for Resource Allocation in Multi-Antenna UAV Network with Rate Splitting Multiple Access
In this paper, we propose a LAS-aided multi-antenna UAV to serve ground users in the downlink transmission of the terahertz (THz) band, utilizing rate splitting multiple access (RSMA) for effective beam division multiplexing.
Meta Reinforcement Learning for Resource Allocation in Unmanned Aerial Vehicles with MIMO Visible Light Communication
This paper centers around a multiple-input-multiple-output (MIMO) visible light communication (VLC) system, where an unmanned aerial vehicle (UAV) benefits from a light emitting diode (LED) array to serve photo-diode (PD)-equipped users for illumination and communication simultaneously.
QOCO: A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing
In this study, we delve into computation offloading in MEC systems, where strict task processing deadlines and energy constraints can adversely affect the system performance.
Dynamic VNF Placement, Resource Allocation and Traffic Routing in 5G
5G networks are going to support a variety of vertical services, with a diverse set of key performance indicators (KPIs), by using enabling technologies such as software-defined networking and network function virtualization.
Networking and Internet Architecture
Linear Temporal Public Announcement Logic: a new perspective for reasoning about the knowledge of multi-classifiers
In the model, first, we consider classifiers, which capture single-framed data.
Meet MASKS: A novel Multi-Classifier's verification approach
In this study, a new ensemble approach for classifiers is introduced.
A Matrix Factorization Model for Hellinger-based Trust Management in Social Internet of Things
We model the SIoT with a flexible bipartite graph (containing two sets of nodes: service providers and service requestors), then build a social network among the service requestor nodes, using the Hellinger distance.
Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction
Hence, we build our method, called Hell-TrustSVD, on top of the state-of-the-art social recommendation technique to incorporate both the extracted implicit social relations and ratings given by users on the prediction of items for an active user.
Learning an Integrated Distance Metric for Comparing Structure of Complex Networks
According to natural witnesses of network similarities (such as network categories) the distance metric is learned by the means of a dataset of some labeled real networks.
