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Found 14 papers, 4 papers with code
Multi-Agent Deep Reinforcement Learning for Safe Autonomous Driving with RICS-Assisted MEC
Environment sensing and fusion via onboard sensors are envisioned to be widely applied in future autonomous driving networks.
Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions and user experiences, especially in the context of 6G systems.
GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks
We build GDSG as a multi-task diffusion model utilizing a Graph Neural Network (GNN) to acquire the distribution of high-quality solutions.
Diffusion Models as Network Optimizers: Explorations and Analysis
Next, we provide a concise theoretical proof and intuitive demonstration of the advantages of generative models over discriminative models in network optimization.
DiffSG: A Generative Solver for Network Optimization with Diffusion Model
In summary, we demonstrate the potential of generative diffusion models in tackling complex network optimization problems and outline a promising path for their broader application in the communication community.
Computation Offloading for Multi-server Multi-access Edge Vehicular Networks: A DDQN-based Method
In this paper, we investigate a multi-user offloading problem in the overlapping domain of a multi-server mobile edge computing system.
A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading
To improve the MEC performance, it is required to design an optimal offloading strategy that includes offloading decision (i. e., whether offloading or not) and computational resource allocation of MEC.
Reconfigurable Intelligent Computational Surfaces: When Wave Propagation Control Meets Computing
The envisioned sixth-generation (6G) of wireless networks will involve an intelligent integration of communications and computing, thereby meeting the urgent demands of diverse applications.
Spectrum Learning-Aided Reconfigurable Intelligent Surfaces for 'Green' 6G Networks
In the sixth-generation (6G) era, emerging large-scale computing based applications (for example processing enormous amounts of images in real-time in autonomous driving) tend to lead to excessive energy consumption for the end users, whose devices are usually energy-constrained.
A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks
In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic.
Intelligent Spectrum Learning for Wireless Networks with Reconfigurable Intelligent Surfaces
Reconfigurable intelligent surface (RIS) has become a promising technology for enhancing the reliability of wireless communications, which is capable of reflecting the desired signals through appropriate phase shifts.
Offloading Optimization in Edge Computing for Deep Learning Enabled Target Tracking by Internet-of-UAVs
This motivates us to consider offloading this type of deep learning (DL) tasks to a mobile edge computing (MEC) server due to limited computational resource and energy budget of the UAV, and further improve the inference accuracy.
Computation Offloading in Multi-Access Edge Computing Networks: A Multi-Task Learning Approach
Multi-access edge computing (MEC) has already shown the potential in enabling mobile devices to bear the computation-intensive applications by offloading some tasks to a nearby access point (AP) integrated with a MEC server (MES).
Lessons Learned from Accident of Autonomous Vehicle Testing: An Edge Learning-aided Offloading Framework
This letter proposes an edge learning-based offloading framework for autonomous driving, where the deep learning tasks can be offloaded to the edge server to improve the inference accuracy while meeting the latency constraint.
