Roadrunner: O-RAN-based Cell Selection in Beyond 5G Networks

Coronado, Estefanía; Siddiqui, Shuaib; Riggio, Roberto

doi:10.1109/noms54207.2022.9789832

Cited by 10 publications

(2 citation statements)

References 15 publications

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“…Similarly, in [15], Pamuklu et al propose a reinforcement learning solution for mapping the split of functionalities between CUs and DUs in Green O-RAN, where the objective is to reduce energy consumption while utilizing renewable energy sources. In [16], the authors propose an algorithm to improve User Equipment (UE) placement taking into account radio quality, bandwidth, and user distribution. Finally, several works address the problem of Network Slicing using deep reinforcement learning [17], [18], [25]- [28], which involves the allocation of resources to create network partitions where different policies and QoS requirements can be met.…”

Section: Related Workmentioning

confidence: 99%

PRORL: Proactive Resource Orchestrator for Open RANs Using Deep Reinforcement Learning

Staffolani,

Darvariu,

Foschini

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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Open Radio Access Network (O-RAN) is an emerging paradigm proposed for enhancing the 5G network infrastructure. O-RAN promotes open vendor-neutral interfaces and virtualized network functions that enable the decoupling of network components and their optimization through intelligent controllers. The decomposition of base station functions enables better resource usage, but also opens new technical challenges concerning their efficient orchestration and allocation. In this paper, we propose Proactive Resource Orchestrator based on Reinforcement Learning (PRORL), a novel solution for the efficient and dynamic allocation of resources in O-RAN infrastructures. We frame the problem as a Markov Decision Process and solve it using Deep Reinforcement Learning; one relevant feature of PRORL is that it learns demand patterns from experience for proactive resource allocation. We extensively evaluate our proposal by using both synthetic and real-world data, showing that we can significantly outperform the existing algorithms, which are typically based on the analysis of static demands. More specifically, we achieve an improvement of 90% over greedy baselines and deal with complex trade-offs in terms of competing objectives such as demand satisfaction, resource utilization, and the inherent cost associated with allocating resources.

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Section: Related Workmentioning

confidence: 99%

PRORL: Proactive Resource Orchestrator for Open RANs Using Deep Reinforcement Learning

Staffolani,

Darvariu,

Foschini

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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“…The authors in [22] emphasize the significance of continuous monitoring of deployed AI/ML models to achieve good network services to avoid decrease in performance. Roadrunner [33] is an O-RAN-based solution designed to improve cell selection in 5G and beyond networks. In contrast to the legacy cell selection procedure, which prioritizes radio quality and seamless connectivity over high data rates, Roadrunner chooses cells that provide the best performance rather than just the best radio connectivity, even if a candidate cell with better radio connectivity is available.…”

Section: B Performancementioning

confidence: 99%

Open Radio Access Network challenges for Next Generation Mobile Network

Aryal

Bertin

Crespi

2023

2023 26th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)

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Due to the tightly-coupled hardware and software architecture of existing RAN systems and their non-flexibility, disaggregation of software and hardware can bring many unprecedented opportunities regarding enabling the entrance of multiple small-scale infrastructure providers to enter the RAN market, which creates more competitive and innovative RAN ecosystem. Moreover, mobile network operators (MNOs) will also have the advantage of selecting the services according to their network requirements. Open Radio Access Network (O-RAN) builds a multi-vendor RAN ecosystem and utilizes openness and intelligence to address the complexity of network functionalities, increase development agility, and provide more cost-effective platforms due to softwarization and the avoidance of dedicated hardware. However, O-RAN faces many challenges, such as interoperability, convergence, and AI/ML management which still need to be addressed before its wide deployment. This paper surveys the existing issues in the Open RAN ecosystem and explores existing solutions.

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