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Found 10 papers, 0 papers with code
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks
Although DGNN has recently received considerable attention by AI community and various DGNN models have been proposed, building a distributed system for efficient DGNN training is still challenging.
Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning
We then put forward a novel offline DAC scheme, which estimates the optimal control policy from a previously collected dataset without any further interactions with the system.
FedGraph: Federated Graph Learning with Intelligent Sampling
In this paper, we propose FedGraph for federated graph learning among multiple computing clients, each of which holds a subgraph.
Distributed Spectrum and Power Allocation for D2D-U Networks: A Scheme based on NN and Federated Learning
In this paper, a Device-to-Device communication on unlicensed bands (D2D-U) enabled network is studied.
Fairness
Federated Learning
Information Theory
Information Theory
Learning-based WiFi Traffic Load Estimation in NR-U Systems
An unsupervised Neural Network (NN) structure is applied to filter the detected transmission collision probability on the unlicensed spectrum, which enables the NR users to precisely rectify the measurement error and estimate the number of active WiFi users.
Information Theory Information Theory
Computation Offloading in Beyond 5G Networks: A Distributed Learning Framework and Applications
Facing the trend of merging wireless communications and multi-access edge computing (MEC), this article studies computation offloading in the beyond fifth-generation networks.
Information Freshness-Aware Task Offloading in Air-Ground Integrated Edge Computing Systems
Using the proposed deep RL scheme, each MU in the system is able to make decisions without a priori statistical knowledge of dynamics.
Age of Information-Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective
In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network.
Decentralized Deep Reinforcement Learning for Delay-Power Tradeoff in Vehicular Communications
To simplify the decision-making process, we first decompose the MDP into a series of per-VUE-pair MDPs.
Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning
To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services.
