AI-Enabled and Integrated Sensing-Based Beam Management Strategies in Open RAN
The growing adoption of millimeter wave (mmWave) turns efficient beamforming and beam management procedures into key enablers for 5th Generation (5G) and Beyond 5G (B5G) mobile networks. Recent research has sought to optimize beam management in modern Radio Access Network (RAN) architectures, where...
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ftunivottawa:oai:ruor.uottawa.ca:10393/45320 2023-10-01T03:55:40+02:00 AI-Enabled and Integrated Sensing-Based Beam Management Strategies in Open RAN Dantas, Ycaro Erol-Kantarci, Melike application/pdf http://hdl.handle.net/10393/45320 https://doi.org/10.20381/ruor-29526 en eng Université d'Ottawa / University of Ottawa http://hdl.handle.net/10393/45320 http://dx.doi.org/10.20381/ruor-29526 beamforming beam management o-ran open ran integrated sensing machine learning reinforcement learning distributed learning Thesis ftunivottawa https://doi.org/10.20381/ruor-29526 2023-09-02T23:00:09Z The growing adoption of millimeter wave (mmWave) turns efficient beamforming and beam management procedures into key enablers for 5th Generation (5G) and Beyond 5G (B5G) mobile networks. Recent research has sought to optimize beam management in modern Radio Access Network (RAN) architectures, where open, virtualized, disaggregated and multi-vendor environments are considered, and management platforms allow the use of Artificial Intelligence (AI) and Machine Learning (ML)-based solutions. Moreover, beam management represents some fundamental use cases defined by Open RAN Alliance (O-RAN). This work analyses beam management strategies in Open RAN and proposes solutions for codebook-based mmWave systems inspired by two use cases from O-RAN: the Grid of Beams (GoB) Optimization and the AI/ML-assisted Beam Selection. For the GoB Optimization use case, a scenario subject to constraints on the use of the full GoB due to overhead during beam selection is considered. An Advantage Actor Critic (A2C) learning-based framework is proposed to optimize the GoB, as well as the transmission power in a mmWave network. The proposed technique improves Energy Efficiency (EE) and ensures fair coverage is maintained. The simulations show that A2C-based joint optimization of GoB and transmission power is more effective than using Equally Spaced Beams (ESB) and fixed power, or the optimization of GoB and transmission power disjointly. Compared to the ESB and fixed transmission power strategy, the proposed approach achieves more than twice the average EE in the scenarios under test, and it is closer to the maximum theoretical EE. In the case of the AI/ML-assisted Beam Selection use case, the overhead during beam selection is addressed by a multi-modal sensing-aided ML-based method. When using sensing information sources external to the RAN in a multi-vendor disaggregated environment, such methods must account for privacy and data ownership issues. A Distributed Machine Learning (DML) strategy based on Split Learning (SL) is proposed to ... Thesis DML uO Research (University of Ottawa - uOttawa) |
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uO Research (University of Ottawa - uOttawa) |
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English |
topic |
beamforming beam management o-ran open ran integrated sensing machine learning reinforcement learning distributed learning |
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beamforming beam management o-ran open ran integrated sensing machine learning reinforcement learning distributed learning Dantas, Ycaro AI-Enabled and Integrated Sensing-Based Beam Management Strategies in Open RAN |
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beamforming beam management o-ran open ran integrated sensing machine learning reinforcement learning distributed learning |
description |
The growing adoption of millimeter wave (mmWave) turns efficient beamforming and beam management procedures into key enablers for 5th Generation (5G) and Beyond 5G (B5G) mobile networks. Recent research has sought to optimize beam management in modern Radio Access Network (RAN) architectures, where open, virtualized, disaggregated and multi-vendor environments are considered, and management platforms allow the use of Artificial Intelligence (AI) and Machine Learning (ML)-based solutions. Moreover, beam management represents some fundamental use cases defined by Open RAN Alliance (O-RAN). This work analyses beam management strategies in Open RAN and proposes solutions for codebook-based mmWave systems inspired by two use cases from O-RAN: the Grid of Beams (GoB) Optimization and the AI/ML-assisted Beam Selection. For the GoB Optimization use case, a scenario subject to constraints on the use of the full GoB due to overhead during beam selection is considered. An Advantage Actor Critic (A2C) learning-based framework is proposed to optimize the GoB, as well as the transmission power in a mmWave network. The proposed technique improves Energy Efficiency (EE) and ensures fair coverage is maintained. The simulations show that A2C-based joint optimization of GoB and transmission power is more effective than using Equally Spaced Beams (ESB) and fixed power, or the optimization of GoB and transmission power disjointly. Compared to the ESB and fixed transmission power strategy, the proposed approach achieves more than twice the average EE in the scenarios under test, and it is closer to the maximum theoretical EE. In the case of the AI/ML-assisted Beam Selection use case, the overhead during beam selection is addressed by a multi-modal sensing-aided ML-based method. When using sensing information sources external to the RAN in a multi-vendor disaggregated environment, such methods must account for privacy and data ownership issues. A Distributed Machine Learning (DML) strategy based on Split Learning (SL) is proposed to ... |
author2 |
Erol-Kantarci, Melike |
format |
Thesis |
author |
Dantas, Ycaro |
author_facet |
Dantas, Ycaro |
author_sort |
Dantas, Ycaro |
title |
AI-Enabled and Integrated Sensing-Based Beam Management Strategies in Open RAN |
title_short |
AI-Enabled and Integrated Sensing-Based Beam Management Strategies in Open RAN |
title_full |
AI-Enabled and Integrated Sensing-Based Beam Management Strategies in Open RAN |
title_fullStr |
AI-Enabled and Integrated Sensing-Based Beam Management Strategies in Open RAN |
title_full_unstemmed |
AI-Enabled and Integrated Sensing-Based Beam Management Strategies in Open RAN |
title_sort |
ai-enabled and integrated sensing-based beam management strategies in open ran |
publisher |
Université d'Ottawa / University of Ottawa |
url |
http://hdl.handle.net/10393/45320 https://doi.org/10.20381/ruor-29526 |
genre |
DML |
genre_facet |
DML |
op_relation |
http://hdl.handle.net/10393/45320 http://dx.doi.org/10.20381/ruor-29526 |
op_doi |
https://doi.org/10.20381/ruor-29526 |
_version_ |
1778524299657740288 |