Search Results for author:
Found 21 papers, 2 papers with code
Communication and Control Co-Design in 6G: Sequential Decision-Making with LLMs
This article investigates a control system within the context of six-generation wireless networks.
Select2Col: Leveraging Spatial-Temporal Importance of Semantic Information for Efficient Collaborative Perception
Collaborative perception by leveraging the shared semantic information plays a crucial role in overcoming the individual limitations of isolated agents.
Semantics-enhanced Temporal Graph Networks for Content Popularity Prediction
On top of that, we customize its temporal and structural learning modules to further boost the prediction performance.
Adversarial Learning for Implicit Semantic-Aware Communications
In this paper, we consider the implicit semantic communication problem in which hidden relations and closely related semantic terms that cannot be recognized from the source signals need to also be delivered to the destination user.
Semantics-Empowered Communication: A Tutorial-cum-Survey
Along with the springing up of the semantics-empowered communication (SemCom) research, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e. g., theories, applications, metrics and implementations) in both academia and industry.
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.
Rethinking Modern Communication from Semantic Coding to Semantic Communication
Modern communications are usually designed to pursue a higher bit-level precision and fewer bits while transmitting a message.
Reinforcement Learning-powered Semantic Communication via Semantic Similarity
We introduce a new semantic communication mechanism - SemanticRL, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision.
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
DRLE: Decentralized Reinforcement Learning at the Edge for Traffic Light Control in the IoV
To this end, we propose a Decentralized Reinforcement Learning at the Edge for traffic light control in the IoV (DRLE).
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.
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.
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.
GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing
Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function.
Distributional Reinforcement Learning
Generative Adversarial Network
+3
Deep Learning with Long Short-Term Memory for Time Series Prediction
Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values.
Deep Reinforcement Learning for Resource Management in Network Slicing
Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices.
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.
Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory
So, the RCLSTM, with certain intrinsic sparsity, have many neural connections absent (distinguished from the full connectivity) and which leads to the reduction of the parameters to be trained and the computational cost.
TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in Cellular Radio Access Networks
Recent works have validated the possibility of improving energy efficiency in radio access networks (RANs), achieved by dynamically turning on/off some base stations (BSs).
