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Multi-Task Learning for Efficient Management of Beyond 5G Radio Access Network Architectures
- Source :
- IEEE Access, Vol 9, Pp 158892-158907 (2021), IEEE Access, r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Universitat Oberta de Catalunya (UOC)
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Automation of Radio Access Network (RAN) operation is a fundamental feature to manage sustainable and efficient Beyond Fifth-generation wireless (5G) networks, in the context of the Next Generation Self-Organizing Network (NG-SON) vision. Machine Learning (ML) is already identified as the key ingredient of this vision, with new standardized and open architectures, like Open-RAN (O-RAN), taking momentum. In this paper, we propose models based on single-task and Multi-Task Learning (MTL) paradigms to address two RAN use cases, handover management and initial Modulation and Coding Scheme (MCS) selection. Traditional handover schemes have the drawback of taking into account the quality of the signals from the serving, and the target cell, before the handover. Also, initial MCS at the start of the session and after a handover usually is handled conservatively. The proposed ML solutions allow to address these drawbacks by 1) considering the expected Quality of Experience (QoE) resulting from the decision of a target cell to handover, as the driving principle of the handover decision and 2) using the experience extracted from network data to make smarter initial MCS allocations. In this line, we implement a realistic cellular simulation scenario by incorporating coverage holes to build an extensive database to train and test the proposed models. The results show that the ML-based models outperform the 3rd Generation Partnership Project (3GPP) standardized handover and initial MCS selection approaches by improving the QoE of users resulting from a handover and the throughput obtained upon establishing a new connection with a network. Besides that, using the obtained results, this paper extensively discusses the merits of leveraging the MTL model to address different, but related multiple RAN functions because it allows reusing a common learning architecture for multiple RAN use cases, which provides significant implementation advantages.<br />This work was supported in part by under Grant PID2020-113832RB-C22 (ORIGIN)/AEI/10.13039/501100011033, and in part by Huawei Technologies, Sweden AB.
- Subjects :
- mobile networks
General Computer Science
Computer science
Linearization
Multi-task learning
RNN
ARPU
Hand over
5G mobile communication systems
Quality of service
Deep neural networks
Long short-term memory
3GPP
General Materials Science
Computer architecture
UE
next generation self-organizing networks
6G
Radio access network
business.industry
UL
Network architecture
General Engineering
deep learning
AE
Deep learning
Radio access networks
3rd generation partnership project
Radio
TK1-9971
Self-organising
Next generation networks
3rd generation
Mobile network
machine learning
Next generation self-organizing network
AI
Task analysis
V2X
Job analysis
Electrical engineering. Electronics. Nuclear engineering
LSTM
business
5G
Computer network
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....d6b0797461aaa32e17159d3380edabf0