[go: up one dir, main page]

Sun et al., 2022 - Google Patents

Aerial edge computing for 6G

Sun et al., 2022

View PDF
Document ID
7870723782208424030
Author
Sun M
Yan Z
Publication year
Publication venue
中国邮电高校学报 (英文)

External Links

Snippet

In the 6th generation mobile communication system (6G) era, a large number of delay- sensitive and computation-intensive applications impose great pressure on resource- constrained Internet of things (IoT) devices. Aerial edge computing is envisioned as a …
Continue reading at jcupt.bupt.edu.cn (PDF) (other versions)

Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATIONS NETWORKS
    • H04W84/00Network topologies
    • H04W84/18Self-organizing networks, e.g. ad-hoc networks or sensor networks
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATIONS NETWORKS
    • H04W16/00Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22Traffic simulation tools or models
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATIONS NETWORKS
    • H04W16/00Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/24Cell structures
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATIONS NETWORKS
    • H04W28/00Network traffic or resource management
    • H04W28/02Traffic management, e.g. flow control or congestion control
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATIONS NETWORKS
    • H04W84/00Network topologies
    • H04W84/02Hierarchical pre-organized networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATIONS NETWORKS
    • H04W72/00Local resource management, e.g. wireless traffic scheduling or selection or allocation of wireless resources
    • H04W72/04Wireless resource allocation
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network-specific arrangements or communication protocols supporting networked applications
    • H04L67/10Network-specific arrangements or communication protocols supporting networked applications in which an application is distributed across nodes in the network
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/14Relay systems
    • H04B7/15Active relay systems
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATIONS NETWORKS
    • H04W88/00Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
    • H04W88/08Access point devices

Similar Documents

Publication Publication Date Title
Seid et al. Collaborative computation offloading and resource allocation in multi-UAV-assisted IoT networks: A deep reinforcement learning approach
Yang et al. Privacy-preserving federated learning for UAV-enabled networks: Learning-based joint scheduling and resource management
Zhou et al. An air-ground integration approach for mobile edge computing in IoT
Luo et al. Deep reinforcement learning based computation offloading and trajectory planning for multi-UAV cooperative target search
KR102235763B1 (en) Multi-access edge computing based Heterogeneous Networks System
Parvaresh et al. A tutorial on AI-powered 3D deployment of drone base stations: State of the art, applications and challenges
Dai et al. Reconfigurable intelligent surface for low-latency edge computing in 6G
Hortelano et al. A comprehensive survey on reinforcement-learning-based computation offloading techniques in edge computing systems
Hazra et al. Collaborative AI-enabled intelligent partial service provisioning in green industrial fog networks
Song et al. A comprehensive survey on aerial mobile edge computing: Challenges, state-of-the-art, and future directions
Luo et al. Path planning for UAV communication networks: Related technologies, solutions, and opportunities
Song et al. Multitask and multiobjective joint resource optimization for UAV-assisted air-ground integrated networks under emergency scenarios
Alwarafy et al. AI-based radio resource allocation in support of the massive heterogeneity of 6G networks
Sun et al. Aerial edge computing for 6G
Nyalapelli et al. Recent advancements in applications of artificial intelligence and machine learning for 5G technology: A review
Ahmed et al. Joint optimization of UAV-IRS placement and resource allocation for wireless powered mobile edge computing networks
Kuang et al. Utility-aware UAV deployment and task offloading in multi-UAV edge computing networks
Nehra et al. Federated learning based trajectory optimization for UAV enabled MEC
Li et al. Computing over the sky: Joint UAV trajectory and task offloading scheme based on optimization-embedding multi-agent deep reinforcement learning
Gupta et al. Resource allocation for UAV-assisted 5G mMTC slicing networks using deep reinforcement learning
Farajzadeh et al. Self-evolving integrated vertical heterogeneous networks
Wu et al. Its: Improved tabu search algorithm for path planning in uav-assisted edge computing systems
Zhao et al. Multi-agent deep reinforcement learning based resource management in heterogeneous V2X networks
Elghitani Dynamic UAV routing for multi-access edge computing
Termehchi et al. Distributed safe multi-agent reinforcement learning: Joint design of THz-enabled UAV trajectory and channel allocation