Multi-Task Learning for Efficient Management of Beyond 5G Radio Access Network Architectures
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 ingred...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) |
| Repositorio: | r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) |
| OAI Identifier: | oai:cttc.fundanetsuite.com:p4121 |
| Acceso en línea: | https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=4121 |
| Access Level: | acceso abierto |
| Palabra clave: | LSTM RNN next generation self-organizing networks deep learning machine learning mobile networks |
| Sumario: | 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. |
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