Maksymyuk et al., 2020 - Google Patents
Intelligent framework for radio access network designMaksymyuk et al., 2020
- Document ID
- 5200014545498536319
- Author
- Maksymyuk T
- Šlapak E
- Bugár G
- Horváth D
- Gazda J
- Publication year
- Publication venue
- Wireless Networks
External Links
Snippet
The evolution of 5G networks over the last few years has introduced a variety of technologies for more efficient radio access networks (RANs), which end up in ultra-dense heterogeneous infrastructure with deployments of high complexity. In this paper, we propose …
- 238000010801 machine learning 0 abstract description 26
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
-
- 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
-
- 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
- G06F17/50—Computer-aided design
-
- 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
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimizing operational condition
-
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11410046B2 (en) | Learning-based service migration in mobile edge computing | |
| Ouyang et al. | Adaptive user-managed service placement for mobile edge computing via contextual multi-armed bandit learning | |
| Pérez-Romero et al. | Knowledge-based 5G radio access network planning and optimization | |
| Lin et al. | Deep reinforcement learning-based task scheduling and resource allocation for NOMA-MEC in Industrial Internet of Things | |
| Liang et al. | An online algorithm for virtualized network function placement in mobile edge industrial Internet of Things | |
| Moysen et al. | On the potential of ensemble regression techniques for future mobile network planning | |
| Huang et al. | Delay constrained SFC orchestration for edge intelligence-enabled IIoT: A DRL approach | |
| Kafle et al. | Intelligent and agile control of edge resources for latency-sensitive IoT services | |
| Zou et al. | ST-EUA: Spatio-temporal edge user allocation with task decomposition | |
| Saeedi et al. | Energy efficient clustering in IoT-based wireless sensor networks using binary whale optimization algorithm and fuzzy inference system | |
| Kumar | AI/ML enabled automation system for software defined disaggregated open radio access networks: Transforming telecommunication business | |
| Maksymyuk et al. | Intelligent framework for radio access network design | |
| Sheelam | Deep Learning-Based Protocol Stack Optimization in High-Density 5G Environments | |
| Gures et al. | A comparative study of machine learning-based load balancing in high-speed train system | |
| Staffolani et al. | PRORL: Proactive resource orchestrator for open RANs using deep reinforcement learning | |
| Ye et al. | Processing capability and QoE driven optimized computation offloading scheme in vehicular fog based F-RAN | |
| Boulogeorgos et al. | Artificial intelligence empowered multiple access for ultra reliable and low latency thz wireless networks | |
| Maksymyuk et al. | Artificial intelligence based 5G coverage design and optimization using deep generative adversarial neural networks | |
| Gilly et al. | Supporting Location Transparent Services in a Mobile Edge Computing Environment. | |
| Hakimi et al. | Resilient DNN for joint sub-band allocation and power control in mobile factory subnetworks | |
| Sun et al. | Resource Allocation in Heterogeneous Network with Supervised GNNs | |
| Wang et al. | An energy-efficient computing offloading strategy based on improved sparrow search algorithm in mobile edge computing | |
| Kasi et al. | Risk-aware Reinforcement Learning Framework for User-centric O-RAN | |
| Mahesh et al. | AI/ML for next generation wireless networks | |
| Da Costa et al. | Cluster-Based Machine Learning-Driven Routing for UAV Networks in 6G Environment |