[go: up one dir, main page]

Pan et al., 2023 - Google Patents

Joint optimization of video-based AI inference tasks in MEC-assisted augmented reality systems

Pan et al., 2023

View PDF
Document ID
17976496837346795337
Author
Pan G
Zhang H
Xu S
Zhang S
Chen X
Publication year
Publication venue
IEEE Transactions on Cognitive Communications and Networking

External Links

Snippet

The high computational complexity and energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems. However, mobile edge computing (MEC) makes it possible to solve this problem. This paper considers the …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W72/00—Local resource management, e.g. wireless traffic scheduling or selection or allocation of wireless resources
    • H04W72/04—Wireless resource allocation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W72/00—Local resource management, e.g. wireless traffic scheduling or selection or allocation of wireless resources
    • H04W72/12—Dynamic Wireless traffic scheduling; Dynamically scheduled allocation on shared channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models

Similar Documents

Publication Publication Date Title
Pan et al. Joint optimization of video-based AI inference tasks in MEC-assisted augmented reality systems
Gao et al. Task partitioning and offloading in DNN-task enabled mobile edge computing networks
Yang et al. Privacy-preserving federated learning for UAV-enabled networks: Learning-based joint scheduling and resource management
Zhu et al. Efficient offloading for minimizing task computation delay of NOMA-based multiaccess edge computing
Al-Quraan et al. Edge-native intelligence for 6G communications driven by federated learning: A survey of trends and challenges
Khayyat et al. Advanced deep learning-based computational offloading for multilevel vehicular edge-cloud computing networks
Chen et al. Multi-tenant cross-slice resource orchestration: A deep reinforcement learning approach
Xue et al. Joint task offloading and resource allocation for multi-task multi-server NOMA-MEC networks
Guo et al. Energy-aware computation offloading and transmit power allocation in ultradense IoT networks
Zhang et al. Resource allocation for a UAV-enabled mobile-edge computing system: Computation efficiency maximization
Wen et al. Joint scheduling and resource allocation for hierarchical federated edge learning
Li et al. Mobiqor: Pushing the envelope of mobile edge computing via quality-of-result optimization
Huang et al. Reinforcement learning based resource management for 6G-enabled mIoT with hypergraph interference model
Albaseer et al. Fine-grained data selection for improved energy efficiency of federated edge learning
Chen et al. Code caching-assisted computation offloading and resource allocation for multi-user mobile edge computing
He et al. Optimizing the learning performance in mobile augmented reality systems with CNN
He et al. Computation offloading and resource allocation based on DT-MEC-assisted federated learning framework
Zhou et al. Joint optimization of energy consumption and completion time in federated learning
Huda et al. Deep reinforcement learning-based computation offloading in UAV swarm-enabled edge computing for surveillance applications
Younis et al. Energy-latency computation offloading and approximate computing in mobile-edge computing networks
Yang et al. Mutual interference-aware throughput enhancement in massive IoT: A graph reinforcement learning framework
Zhou et al. Communication-efficient offloading for mobile-edge computing in 5G heterogeneous networks
Yang et al. Sparse optimization for green edge AI inference
ZHAO et al. Federated learning for 6G: A survey from perspective of integrated sensing, communication and computation
Jiang et al. Research on new edge computing network architecture and task offloading strategy for Internet of Things