Search Results for author:
Found 10 papers, 0 papers with code
Personalized Wireless Federated Learning for Large Language Models
Large Language Models (LLMs) have revolutionized natural language processing tasks.
Large Generative Model Assisted 3D Semantic Communication
Semantic Communication (SC) is a novel paradigm for data transmission in 6G.
Large Language Model Enhanced Multi-Agent Systems for 6G Communications
The rapid development of the Large Language Model (LLM) presents huge opportunities for 6G communications, e. g., network optimization and management by allowing users to input task requirements to LLMs by nature language.
Large AI Model Empowered Multimodal Semantic Communications
To this end, we propose a Large AI Model-based Multimodal SC (LAM-MSC) framework, in which we first present the MLM-based Multimodal Alignment (MMA) that utilizes the MLM to enable the transformation between multimodal and unimodal data while preserving semantic consistency.
LAMBO: Large Language Model Empowered Edge Intelligence
Next-generation edge intelligence is anticipated to bring huge benefits to various applications, e. g., offloading systems.
Large AI Model-Based Semantic Communications
Semantic communication (SC) is an emerging intelligent paradigm, offering solutions for various future applications like metaverse, mixed-reality, and the Internet of everything.
Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System
To address this issue, we propose a joint optimization framework of deployment and trajectory (JOLT), where an adaptive whale optimization algorithm (AWOA) is applied to optimize the deployment of the UAV, and an elastic ring self-organizing map (ERSOM) is introduced to optimize the trajectory of the UAV.
Distributed Resource Scheduling for Large-Scale MEC Systems: A Multi-Agent Ensemble Deep Reinforcement Learning with Imitation Acceleration
We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system.
AI Driven Heterogeneous MEC System with UAV Assistance for Dynamic Environment -- Challenges and Solutions
By taking full advantage of Computing, Communication and Caching (3C) resources at the network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for the next generation networks.
Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks
An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet of things (IoT) users, by optimizing offloading decision, transmission power and resource allocation in the large-scale mobile edge computing (MEC) system.
