Alnawayseh et al., 2022 - Google Patents
Smart congestion control in 5g/6g networks using hybrid deep learning techniquesAlnawayseh et al., 2022
View PDF- Document ID
- 2765582535975365586
- Author
- Alnawayseh S
- Al-Sit W
- Ghazal T
- Publication year
- Publication venue
- Complexity
External Links
Snippet
With the mobility and ease of connection, wireless sensor networks have played a significant role in communication over the last few years, making them a significant data carrier across networks. Additional security, lower latency, and dependable standards and communication …
- 238000000034 method 0 title description 21
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network-specific arrangements or communication protocols supporting networked applications
- H04L67/10—Network-specific arrangements or communication protocols supporting networked applications in which an application is distributed across nodes in the network
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a programme unit and a register, e.g. for a simultaneous processing of several programmes
- G06F15/163—Interprocessor communication
-
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W4/00—Mobile application services or facilities specially adapted for wireless communication networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Alnawayseh et al. | Smart congestion control in 5g/6g networks using hybrid deep learning techniques | |
Elfatih et al. | Internet of vehicle's resource management in 5G networks using AI technologies: Current status and trends | |
Wang et al. | Convergence of edge computing and deep learning: A comprehensive survey | |
Qi et al. | Federated reinforcement learning: Techniques, applications, and open challenges | |
Hu et al. | Distributed machine learning for wireless communication networks: Techniques, architectures, and applications | |
Bukhari et al. | An Intelligent Proposed Model for Task Offloading in Fog‐Cloud Collaboration Using Logistics Regression | |
Feng et al. | Computation offloading in mobile edge computing networks: A survey | |
Jiang | Graph-based deep learning for communication networks: A survey | |
Ali et al. | Machine learning technologies for secure vehicular communication in internet of vehicles: recent advances and applications | |
Bandani et al. | Multiplicative long short-term memory-based software-defined networking for handover management in 5G network | |
Khan et al. | Edge computing: A survey | |
Qi et al. | Knowledge-driven service offloading decision for vehicular edge computing: A deep reinforcement learning approach | |
Pourghebleh et al. | A roadmap towards energy‐efficient data fusion methods in the Internet of Things | |
Abdulazeez et al. | Offloading mechanisms based on reinforcement learning and deep learning algorithms in the fog computing environment | |
Shome et al. | Federated learning and next generation wireless communications: A survey on bidirectional relationship | |
Lee et al. | Federated learning-empowered mobile network management for 5G and beyond networks: From access to core | |
Nguyen et al. | DRL‐based intelligent resource allocation for diverse QoS in 5G and toward 6G vehicular networks: a comprehensive survey | |
Zhang et al. | Federated learning in intelligent transportation systems: Recent applications and open problems | |
Noman et al. | Machine Learning Empowered Emerging Wireless Networks in 6G: Recent Advancements, Challenges & Future Trends | |
Almutairi | Deep Learning‐Based Solutions for 5G Network and 5G‐Enabled Internet of Vehicles: Advances, Meta‐Data Analysis, and Future Direction | |
Bárcena et al. | Enabling federated learning of explainable AI models within beyond-5G/6G networks | |
Wang et al. | Building an improved Internet of Things smart sensor network based on a three-phase methodology | |
Alsulami et al. | A federated deep learning empowered resource management method to optimize 5G and 6G quality of services (QoS) | |
Zhang et al. | Evolution toward artificial intelligence of things under 6G ubiquitous-X | |
Bhattacharya et al. | Amalgamation of blockchain and sixth‐generation‐envisioned responsive edge orchestration in future cellular vehicle‐to‐anything ecosystems: Opportunities and challenges |