Guo et al., 2022 - Google Patents
Federated reinforcement learning-based resource allocation in D2D-enabled 6GGuo et al., 2022
- Document ID
- 811005931288399927
- Author
- Guo Q
- Tang F
- Kato N
- Publication year
- Publication venue
- IEEE Network
External Links
Snippet
The current 5G and conceived 6G era with ultra-high density, ultra-high frequency bandwidth, and ultra-low latency can support emerging applications like Extended Reality (XR), Vehicle to Everything (V2X), and massive Internet of Things (IoT). With the rapid …
- 230000002787 reinforcement 0 title abstract description 10
Classifications
-
- 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
- H04W16/24—Cell structures
- H04W16/32—Hierarchical cell structures
-
- 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
- H04W16/02—Resource partitioning among network components, e.g. reuse partitioning
- H04W16/12—Fixed resource partitioning
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W84/00—Network topologies
- H04W84/02—Hierarchical pre-organized networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
- H04W84/04—Large scale networks; Deep hierarchical networks
-
- 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
- H04W72/048—Wireless resource allocation where an allocation plan is defined based on terminal or device properties
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas
- H04B7/022—Site diversity; Macro-diversity
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organizing networks, e.g. ad-hoc networks or sensor networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W4/00—Mobile application services or facilities specially adapted for wireless communication networks
- H04W4/06—Selective distribution or broadcast application services; Mobile application services to user groups; One-way selective calling services
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W36/00—Hand-off or reselection arrangements
- H04W36/24—Reselection being triggered by specific parameters used to improve the performance of a single terminal
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W4/00—Mobile application services or facilities specially adapted for wireless communication networks
- H04W4/02—Mobile application Services making use of the location of users or terminals, e.g. OMA SUPL, OMA MLP or 3GPP LCS
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W48/00—Access restriction; Network selection; Access point selection
- H04W48/20—Selecting an access point
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W40/00—Communication routing or communication path finding
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W52/00—Power Management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC [Transmission power control]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W8/00—Network data management
- H04W8/02—Processing of mobility data, e.g. registration information at HLR [Home Location Register] or VLR [Visitor Location Register]; Transfer of mobility data, e.g. between HLR, VLR or external networks
- H04W8/08—Mobility data transfer
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W92/00—Interfaces specially adapted for wireless communication networks
- H04W92/16—Interfaces between hierarchically similar devices
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W28/00—Network traffic or resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Guo et al. | Federated reinforcement learning-based resource allocation in D2D-enabled 6G | |
Tang et al. | Deep reinforcement learning for dynamic uplink/downlink resource allocation in high mobility 5G HetNet | |
Attiah et al. | A survey of mmWave user association mechanisms and spectrum sharing approaches: An overview, open issues and challenges, future research trends | |
Yang et al. | Advanced spectrum sharing in 5G cognitive heterogeneous networks | |
Hassan et al. | Interference mitigation and dynamic user association for load balancing in heterogeneous networks | |
Hashima et al. | On softwarization of intelligence in 6G networks for ultra-fast optimal policy selection: Challenges and opportunities | |
Pan et al. | Artificial intelligence-based energy efficient communication system for intelligent reflecting surface-driven VANETs | |
Hashida et al. | Mobility-aware user association strategy for IRS-aided mm-wave multibeam transmission towards 6G | |
Wu et al. | QoE-based distributed multichannel allocation in 5G heterogeneous cellular networks: A matching-coalitional game solution | |
Johnson et al. | An optimized algorithm for vertical handoff in heterogeneous wireless networks | |
Zhang et al. | Deep reinforcement learning driven UAV-assisted edge computing | |
Ejaz et al. | Learning paradigms for communication and computing technologies in IoT systems | |
Islam et al. | Survey on the state-of-the-art in device-to-device communication: A resource allocation perspective | |
Ao et al. | Resource allocation for RIS-assisted device-to-device communications in heterogeneous cellular networks | |
Qi et al. | Advanced user association in non-orthogonal multiple access-based fog radio access networks | |
Gupta et al. | Group mobility assisted network selection framework in 5G vehicular cognitive radio networks | |
Liao et al. | Robust task offloading for IoT fog computing under information asymmetry and information uncertainty | |
Wu et al. | Device-to-device communications at the terahertz band: Open challenges for realistic implementation | |
Qiao et al. | Joint optimization of resource allocation and user association in multi-frequency cellular networks assisted by RIS | |
Ju et al. | Energy-Efficient Cooperative Secure Communications in mmWave Vehicular Networks Using Deep Recurrent Reinforcement Learning | |
Tafintsev et al. | Airborne integrated access and backhaul systems: learning-aided modeling and optimization | |
Gao et al. | Mobility assisted device-to-device communications underlaying cellular networks | |
Murtadha et al. | Flexible handover solution for vehicular ad-hoc networks based on software defined networking and fog computing. | |
Gui et al. | Network Capacity Optimization for Cellular‐Assisted Vehicular Systems by Online Learning‐Based mmWave Beam Selection | |
Quer et al. | A Wireless Vehicle-based mobile network infrastructure designed for smarter cities |