Name
Abstract | ||
|---|---|---|
Machine learning has the capability to provide simpler solutions to complex problems by analyzing a huge volume of data in a short time, learning for adapting its functionality to dynamically changing environments, and predicting near future events with reasonably good accuracy. The 5G communication networks are getting complex due to emergence of unprecedentedly huge number of new connected devices and new types of services. Moreover, the requirements of creating virtual network slices suitable to provide optimal services for diverse users and applications are posing challenges to the efficient management of network resources, processing information about a huge volume of traffic, staying robust against all potential security threats, and adaptively adjustment of network functionality for time-varying workload. In this paper, we introduce about the envisioned 5G network slicing and elaborate the necessity of automation of network functions for the design, construction, deployment, operation, control and management of network slices. We then revisit the machine learning techniques that can be applied for the automation of network functions. We also discuss the status of artificial intelligence and machine learning related activities being progressed in standards development organizations and industrial forums. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.23919/ITU-WT.2018.8597639 | 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K) |
Keywords | Field | DocType |
Machine learning,artificial intelligence,5G network,slicing,standardization | Resource management,Virtual network,Information processing,Software deployment,Telecommunications network,Type of service,Workload,Computer science,Automation,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-5607-5 | 1 | 0.41 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ved P. Kafle | 1 | 209 | 35.01 |
| Yusuke Fukushima | 2 | 92 | 12.80 |
| Pedro Martinez-Julia | 3 | 109 | 20.06 |
| Takaya Miyazawa | 4 | 39 | 13.31 |