Empowering the 6G Cellular Architecture With Open RAN
Michele Polese,
Mischa Dohler,
Falko Dressler
et al.
Abstract:Innovation and standardization in 5G have brought advancements to every facet of the cellular architecture. This ranges from the introduction of new frequency bands and signaling technologies for the radio access network (RAN), to a core network underpinned by micro-services and network function virtualization (NFV). However, like any emerging technology, the pace of real-world deployments does not instantly match the pace of innovation. To address this discrepancy, one of the key aspects under continuous deve… Show more
With the centralization of the RAN control functionality, a very large number of near real-time network optimization use cases have emerged. Until now, each of them has been implemented and validated as isolated network management functions, named xApps in Open RAN terminology. To be able to progress towards realistic, commercially deployable functionality, comprehensive conflict mitigation is needed, because the complete isolation of these xApps would result in conflicting decisions and instability. In this article, we study how the majority of these use cases can be implemented harmoniously, to create an enhanced RAN control plane, underlining the functionality required from the central units of the RAN, the grouping, and the interaction of the network management decision. Analyzing the gap existing in the O-RAN architecture, we identify the functionality needed and propose a management framework. Furthermore, we present an implementation roadmap for the development of such functionality as part of the Fraunhofer FOKUS Open5GCore toolkit, as a reference for how such functionality can be prototyped, validated, and integrated with external algorithms easily, to benefit from the large body of academic research.
With the centralization of the RAN control functionality, a very large number of near real-time network optimization use cases have emerged. Until now, each of them has been implemented and validated as isolated network management functions, named xApps in Open RAN terminology. To be able to progress towards realistic, commercially deployable functionality, comprehensive conflict mitigation is needed, because the complete isolation of these xApps would result in conflicting decisions and instability. In this article, we study how the majority of these use cases can be implemented harmoniously, to create an enhanced RAN control plane, underlining the functionality required from the central units of the RAN, the grouping, and the interaction of the network management decision. Analyzing the gap existing in the O-RAN architecture, we identify the functionality needed and propose a management framework. Furthermore, we present an implementation roadmap for the development of such functionality as part of the Fraunhofer FOKUS Open5GCore toolkit, as a reference for how such functionality can be prototyped, validated, and integrated with external algorithms easily, to benefit from the large body of academic research.
Next-generation sensor and radio access networks (NG-SRANs) namely, Hydra radio access networks (H-RANs) represent a significant evolution in the telecommunications and sensor ecosystem landscape in anticipation of 6G deployment and beyond. H-RAN's vision derives its strength from integrating various technologies and networks into a single central network with the widespread incorporation of artificial intelligence (AI) technologies throughout the network. As a result, H-RAN's unique features and characteristics can serve as a baseline for innovating new applications and significantly enhance the overall functions of conventional open radio access networks (O-RANs). However, among the many improvements and innovations that the H-RAN architecture promises in its functionality, this paper focuses on the initial access implementation "task 1 " approach. Our solution contains several novelties that enhance both overhead and model accuracy. To this end, we define a novel intelligent perception network inspired by the knowledge distribution idea for collaborative H-RAN networks. We develop sparse multi-task learning (SMTL) as part of the AI/ML D-engine for federated learning to perform multiple tasks simultaneously. The SMTL is designed to select the optimal solution from a list of recommended solutions, namely "tasks". In the simulation, figures of merit include metrics such as top-k validation accuracy, beam selection accuracy, throughput ratios, beam sweep time, latency, and initial access times, which are used to evaluate the performance and efficiency of the proposed technologies. Simulation results demonstrate that by exploiting contextual information from distributed collaborative SRUs, and UE sends its own sensing information via a physical random-access channel in addition to using SMTL, our H-RAN-based initial access scheme can achieve 82.9% throughput of an exhaustive beam search (EBS) based-O-RAN network without any beam search overhead and 96.7% by searching among as few as 5 beams. Compared to the conventional MMW 5G-NR solution, our proposed method significantly minimizes the beam search time needed to reach the desired throughput.
INDEX TERMSHydra Radio Access Network (H-RAN), Multi-functional networks, Perceptive networks, heterogeneous data, AI/ML engines, Collaborative-based approach, Sparse multi-task learning (SMTL), Initial access.
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