AI-Enabled and Integrated Sensing-Based Beam Management Strategies in Open RAN - Dantas, Ycaro (Université d'Ottawa / University of Ottawa, 2023)
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 this end. The solution can cope with deployment challenges in novel RAN architectures and is applied to single and multi-level beam selection decisions, where the latter considers hierarchical codebook structures. With the proposed approach, accuracy levels above 90% can be achieved, while overhead decreases by 85% or more. SL achieves performance comparable to the centralized learning-based strategies, with the added value of accounting for privacy and data ownership issues.
Keywords: beamforming; beam management; o-ran; open ran; integrated sensing; machine learning; reinforcement learning; distributed learning