This research report analyzes the motivations and requirements for Service-based RAN, which include the customized needs of industry users, capacity exposure, the introduction of new capabilities (AI, sensing) for 6G, service-oriented network management/orchestration and business innovation. For the basic considerations of Service-based RAN, this report analyzes the principles to be followed, including modular service definition, service reusability, self-contained functional structuring, etc., and analyzes the different directions of service implementation from different architectural perspectives such as control plane, user plane, and service access interfaces. Finally, the challenges faced by Service-based RAN are analyzed, including the rationality of service definition, performance impact, UE impact, and interoperability.
In this research report, we consider and analyze use cases related to the notion of dApps, lightweight, programmable, distributed applications that complement the scope of xApps and rApps by performing customizable data-driven tasks in O-RAN Distributed Units (O-DUs) and O-RAN Centralized Units (O-CU-CP and O-CU-UP). We propose 10 exemplary use cases that relate to spectrum management, scheduling, energy efficiency, traffic classification. Based on this analysis, we identify the requirements for the dApps architecture, including flows for data in and out of the O-DUs and O-CU-CP/UP and dApps, and compare real-time control solutions based on (i) standalone dApps or (ii) a real-time RAN Intelligent Controller (RIC) hosted within the RAN. This research report serves as an introduction to further investigation in the area of real-time control and optimization of the next generation of the O-RAN architecture.
Digital Twin Network (DTN) is foreseen as one of the essential tools for managing the complexity and demands for emerging 6G networks, offering a high-fidelity and real-time virtual environment that would mirror complex behaviors of the underlying physical network. Creating the perfect recipe of a highly accurate DTN requires several ingredients to come together into the mix - data collected from the physical network, advanced modeling techniques for DTN, and robust interfaces around DTN. The objective of this research report is to deep dive into a set of key technologies that would enable the realizations of these key ingredients into shaping up the high-fidelity digital avatar of a 6G network, and to ensure seamless interaction and synchronization between the digital and physical realms. In particular, this research report focuses on Digital Twin for Radio Access Network (RAN) or DT-RAN, one of the most complex domains within a network infrastructure. Understanding intricacies of Digital Twin enablers for RAN will be crucial in extending the scope of this technology beyond the RAN Domain and to other parts of the network.
With the integration of advanced IoT and network-enabled robotics into daily life, ensuring stable quality of service (QoS) across the entire service area is paramount for the upcoming 6G. A primary challenge in providing stable QoS in end-to-end (E2E) communications is the variation in radio communication channel quality, particularly the Signal-to-Interference-plus-Noise (SINR). This research report addressed the challenge of maintaining stable radio communication quality. It outlines the motivation for radio quality assurance, analyzes anticipated 6G use cases, and discusses gaps, challenges, and promising technical approaches for the development of O-RAN. Key findings underscore the necessity of a user-centric RAN approach to achieve cost-effective and stable radio quality. This highlights the importance of a RAN adaptability to SINR variations caused by changes in user location. Additionally, the report identifies significant technological gaps, particularly in supporting advanced applications in robotics, mission-critical operations, and Unmanned Aerial Vehicles/Urban Air Mobility (UAV/UAM).
Recognizing the critical role that indoor wireless communication plays in the current digital ecosystem, this research report provides examinations of emerging and advanced indoor use cases, and sheds light on the distinctive characteristics and requirements of these settings. Each of the use cases detailed in this research report is selected for its relevance to illustrate the potentials of 6G in indoor settings, e.g., either to enable the use cases that cannot be realized by 5G, or to bring the use cases that can to some extent already be achieved by 5G to the next level in terms of performance. These use cases include scenarios ranging from enhanced smart home functionalities and immersive entertainment experiences to efficient workplace communication systems. By providing a detailed description of each use case, the report aims to highlight the significant opportunities that 6G technology offers for indoor environments. Moreover, the report outlines the potential requirements of the discussed use cases, emphasizing the need for advanced connectivity solutions to meet the expectations of the next generation of users and applications. By analyzing the potential use cases and their requirements, this research report targets to contribute to the ongoing dialogue among stakeholders in the telecommunications industry, other related industries, policymakers, and technology developers. The ultimate goal is to pave the way for the successful integration of 6G technology into our indoor spaces, ensuring that the future wireless communication fulfils its promise of creating smarter, better connected, and more efficient indoor environments.
The objective of this research report is to identify network Energy Efficiency (EE) requirements for different network functions, especially in the O-RAN architecture. Network EE requirements both in computation and in communication are evaluated. The report discusses advanced design methodologies for EE, including the adaptation of energy-aware protocols and system architectures. It introduces strategies to enhance network EE KPIs, focusing on innovative energy-saving mechanisms and efficient use of resources. It also emphasizes on the necessity of establishing energy-aware design fundamentals and integrating energy-efficient technologies across the network. Furthermore, it elaborates on the critical role of energy-efficient cloud data centers for 6G and subsequent generations, highlighting their influence on the network's overall energy consumption and the importance of employing current and new potential energy-efficient strategies. Additionally, the construction strategies for an energy-efficient AI/ML platform emphasize the integration of sustainable design methodologies and the optimization of energy usage from infrastructure to application services. This research report further suggests ways to reduce energy consumption and promote green communication in networks for 6G and beyond.
In this research report, we explore the area of Digital Twin RAN (DT-RAN) use cases and perform an analysis of the potential gaps in the O-RAN standards which are then provided as inputs to next DT-RAN research phases and other research streams. In particular, the research report focuses on top 5 use cases that emerge through a comprehensive survey conducted within O-RAN community. The use cases are – DT-RAN for AI/ML training, evaluation and testing, DT-RAN for network testing automation, DT-RAN for network planning, DT-RAN for network energy Saving, and DT-RAN for site specific network optimization.
In the dynamic landscape of wireless communication, the arrival of 6G technology emerges as a transformative force, opening the way for exciting capabilities. This research report delves into the complex domain of mmWave Antenna Distribution (mWAD), a critical aspect in the evolution from 5G to 6G. The underlying concepts to interpret the potential of mmWave technology, particularly focusing on its applications in various areas and challenges. During the research work for this report, we have conducted an extensive survey wherein we analyzed 170 6G use cases from 4 global documents, categorized them into 29 integrated use cases, and extracted 31 requirements. A deeper requirement analysis was conducted for use cases where the application of mWAD is anticipated, and a gap analysis was performed in comparison with 5G technologies.
This research report addresses how AI/ML systems can be seamlessly integrated into the next generation network architecture, ensuring compatibility and scalability across various network paradigms, including Open RAN, and how testing methodologies can be adapted to accommodate the intrinsic uncertainties associated with AI/ML algorithms in the network, while remaining adaptable for both traditional RAN and Open RAN configurations, as well as how the safety implications of AI integration in next generation networks can be addressed, ensuring that these technologies enhance user privacy, security, and trust. It concludes by discussing how predictive analytics and advanced testing techniques can be employed to proactively identify network bottlenecks, optimize resource allocation, and enhance overall network efficiency, regardless of the specific network infrastructure.
This research report discusses the use cases, requirements, and potential technological directions of cross-domain AI for the next generation networks. Firstly, it provides a brief overview of the current research and application status of Artificial Intelligence (AI) in the various domains of Network as a Service. The report describes the progress of AI-related research within different standards organizations such as 3GPP, O-RAN, ONAP, ETSI, etc. Then this research report further explores cross-domain AI technology. To that end, it focuses on the next-generation network, provides potential technical considerations, and impacts of cross-domain AI on the current network, and presents potential technological directions for the collaboration of data, computing power, and models. This report identifies key research areas in cross-domain AI research and serves as a starting point for further exploration in each key direction.
The objective of this research report is to identify and analyze key architecture principles relevant to the standardization of a future cloud-friendly 6G RAN functional architecture. The report describes the design aspects for a cloudified Radio Access Network (RAN), such as automation, observability, root cause analysis, and zero trust architecture. These aspects aim to improve network performance, service assurance, configuration, deployment, security, and fault detection in a heterogeneous and multi-stakeholder environment. The reports also suggests enablers for these aspects, such as intent based APIs, life cycle management, trusted execution environments, and remote attestation. The principles and design aspects proposed in the report aim to be useful input to later 6G standardization activities in 3GPP and O-RAN.
Native Artificial Intelligence (AI) is the enabler technology for 6G. The RAN Intelligent Controller (RIC) of O-RAN is the potential approach for native AI. For novel scenarios in the future, AI can help to improve the quality of user experience and network efficiency. The report analyzes the requirements of Native AI in 6G and discusses the principles and features of native AI, followed by a recommended future framework for native AI.
New spectrum is increasingly more difficult to secure than it was for prior generations of mobile networks. It is now more important than ever that all available spectrum can be efficiently utilized. While radio spectrum can be licensed for the exclusive use of a single operator,an operator may share licensed spectrum with multiple other operators. Shared use of spectrum among multiple operators can lead to better utilization of spectrum without the constraints associated with non-exclusive use of spectrum, resulting in economic benefits for operators and end users alike. Unlicensed spectrum may be used by anyone as long as the access rules are followed. In general, unlicensed spectrum, while essentially designed to enable spectrum sharing, is often unsuitable, or at least very challenging for operator use due to relatively low power limits and sensing requirements leading to inability for operators to manage KPIs for service quality. 3GPP standardized RAN sharing is a method for operators to share equipment and spectrum while maintaining the same service quality associated with the exclusively licensed spectrum. The downside is that a very high level of cooperation is required among cooperating operators for planning, equipment selection, siting, and operation. The result is less room for operators to differentiate their services and the ability to upgrade, balance, or enhance the equipment is less agile. Finally, 3GPP RAN sharing is not as amenable to sharing between operators and non-operator networks (e.g., Private Networks, government networks, non-3GPP incumbents, ...).A framework based on sharing O-RUs among system operators for spectrum sharing, which builds upon the O-RAN Open Fronthaul’s innate shared O-RU capabilities, addresses many of these limitations and the drawbacks associated with unlicensed spectrum or 3GPP RAN sharing. Power limits can be relaxed, and quality of service can be controlled. The result is a user experience that is comparable to the exclusively licensed spectrum deployments. A “neutral host” like deployment of an O-RU can support shared use from multiple independent O-DUs accessed dynamically via standardized fronthaul interfaces. This can support differentiation between operators. In addition, it retains for MNOs and Private Networks alike all of the cloud-based, complete, centralized control capabilities of the Open RAN system. This includes all of the functionality of the O-DU, O-CU, RIC, and advanced network automation capabilities inherent in the O-RAN architecture. A shared O-RU architecture relies on prioritized use of resources to guarantee service quality and allows statistical multiplexing of traffic among operators, private networks, and other spectrum stakeholders, resulting in more efficient overall use of spectrum resources. In order for such a scheme to work effectively for all of the cooperating spectrum users, the improvement in efficiency is critically dependent on how fast the shared O-RU procedure to allocate idle resource among spectrum users is. The proposed shared O-RU spectrum sharing framework is applicable to sharing between spectrum users including public, private, and government. Additionally, it is radio technology agnostic, i.e., not all of the cooperating systems need to deploy the same 3GPP radio technology versions, and non-3GPP radio technologies can potentially be accommodated using the same O-RAN Open Fronthaul-based shared O-RU mechanisms. Shared O-RU with spectrum sharing feature can create opportunities for new spectrum for the next generation networks. In addition, it can lead to sustainability improvements and reduction to CapEx and OpEx for operators.
Quantum computing poses threats to the security of mobile systems. Both public-key and symmetric-key cryptographic algorithms that are widely used in mobile systems as of today would become vulnerable to quantum attacks. The mobile industry needs to transition to quantum-resistant cryptography to ensure the security and integrity of communications in mobile systems. PQC and QKD are fields of research that aims to develop new cryptographic algorithms that are secure against quantum attacks. There are few challenges such as performance, interoperability that need to be addressed in order to adopt those new algorithms for secure communication in future mobile systems.
This document provides a broad view of the functional aspects that needs to be considered for the incorporation of native and cross domain AI into next generation networks. It begins with a brief overview of the current status of AI in global standards organizations, including 3GPP, O-RAN and ETSI-ZSM. The report provides concise definitions of the terms native and cross domain AI in the context of wireless networks and then goes on to discuss the impact of AI on the architecture. The challenges of ingesting large amounts of disparate data across multiple layers, and the corresponding requirements on data modeling, formatting and representation are discussed. A unified data ingestion model is emerging as a key requirement. The importance of distributed and edge intelligence to solve the complex multi-layered issues in wireless networks is emphasized, along with the notion of trustworthiness in such a distributed architecture. Enablers for large scale distributed intelligence, including HPC platforms and accompanying software platforms including open-source, are discussed. The emerging paradigm of intent-driven management, and its interplay with AI/ML are described. The necessity to have collaborative AI across disaggregated RAN and between RAN and CN are discussed.This research report is the first attempt in O-RAN next Generation Research Group (nGRG) to survey the landscape with respect to AI/ML as it applies to next generation networks, and provides a foundation based on which several further explorations into each of the highlighted areas can be initiated.
The O-RAN ALLIANCE’s next Generation Research Group (nGRG) has been established to bring the O-RAN towards 6G and beyond. One of the key criteria to achieve such a feat is to know what the 6G will be all about. Therefore, it is necessary to look at the future 6G use cases and their requirements. RS01 research stream within nGRG explores the area of 6G use cases and performs an analysis of the potential 6G gaps in the O-RAN Architecture and specifications. From these use cases and requirements, we can steer the O-RAN research towards 6G, find the gaps in the current O-RAN standards, and enhance O-RAN standards to close the gaps.To collect information about interest of O-RAN members in use cases towards 6G, a survey was conducted during October and November 2022. This research report is based on the conducted survey. This research report explains the survey questions and summarizes the results from survey answers.
