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Toward Next Generation Open Radio Access Network--What O-RAN Can and Cannot Do!
Aly S. Abdalla, Pratheek S. Upadhyaya, Vijay K. Shah, Vuk Marojevic
TL;DR
O-RAN offers an open, AI-enabled architecture for next-generation RANs, but its current specifications leave important 6G research and deployment needs unresolved. This paper combines a community survey with an analysis of O-RAN capabilities and limitations, identifying security, deterministic latency, physical-layer real-time control, and AI-control testing as priorities and outlining R&D directions to address them.
Problem
Current O-RAN specifications do not fully address end-to-end security, deterministic latency, physical-layer real-time control, and testing of AI-based RAN control applications.
Method
The paper surveys researchers, developers, and practitioners and analyzes O-RAN capabilities, limitations, and opportunities for extending its architecture.
Results
The majority of surveyed participants supported O-RAN for future cellular networks and identified interests including lower-layer access, experimentation, and AI-enabled controller testing.
Takeaways & Limitations
The paper outlines technologies and R&D opportunities for extending O-RAN’s architectural capabilities as a platform for 6G wireless.
Takeaways & Limitations
The paper’s scope is to identify current O-RAN limitations and directions for R&D rather than provide a complete implementation of the proposed extensions.
Abstract
from arXiv · showhide
The open radio access network (O-RAN) describes an industry-driven open architecture and interfaces for building next generation RANs with artificial intelligence (AI) controllers. We circulated a survey among researchers, developers, and practitioners to gather their perspectives on O-RAN as a framework for 6G wireless research and development (R&D). The majority responded in favor of O-RAN and identified R&D of interest to them. Motivated by these responses, this paper identifies the limitations of the current O-RAN specifications and the technologies for overcoming them. We recognize end-to-end security, deterministic latency, physical layer real-time control, and testing of AI-based RAN control applications as the critical features to enable and discuss R&D opportunities for extending the architectural capabilities of O-RAN as a platform for 6G wireless.
I. INTRODUCTION
O-RAN replaces vendor-specific, monolithic RAN designs with an open, virtualized, intelligent architecture for flexible network innovation. A community survey motivates examining O-RAN’s limitations and research priorities for 6G.
- O-RAN provides virtualization, intelligence, flexibility, and open interfaces to support service heterogeneity, coordination, and on-demand deployments.
- Its open architecture replaces vendor-specific interfaces and establishes modules for data collection, distribution, and processing in increasingly data-driven network management.
- Prior studies explore AI-based radio resource management, renewable-energy deployment, dynamic function splitting, and closed-loop RAN optimization.
- The paper uses community feedback to identify security, latency, real-time control, and testing as critical O-RAN components needing specification and R&D attention.
- The survey invited about 150 advanced wireless communications and networking experts, with 95 participants from 65 institutions responding.
6 How do you expect to use future O-RAN
The survey sought community views on O-RAN’s role in 6G research and the capabilities researchers most want to explore or develop. Respondents emphasized broad experimentation, lower-layer access, and AI-controller testing.
- The survey asked which O-RAN capabilities participants wanted to explore, develop, or use for advanced wireless and networking R&D.
- A majority agreed that O-RAN will be the foundation of future cellular networks.
- Around 38% wanted access to in-phase and quadrature data across time and frequency domains using different functional split options.
- Sixteen percent wanted support for dynamic functional splitting, while programmable PHY and MAC layers, MIMO, scheduling, and resource allocation ranked highly.
- Respondents prioritized data collection, realistic large-scale experimentation, novel networking features, and implementation and verification of AI-enabled controllers.
- The survey findings motivate studying O-RAN’s capabilities and limitations and developing complementary architectural elements and processes.
III. THE O-RAN ARCHITECTURE, COMPONENTS, AND INTERFACES
O-RAN extends the 5G NR RAN architecture with disaggregated components, open interfaces, and RIC-based control. Its architecture spans RAN functions, operations support, orchestration, and multi-RAT connectivity.
- The architecture encompasses the RAN, operations support systems, and open interfaces, with O-DU functions including higher-PHY, MAC, and RLC processing.
- The O-CU provides RRC, PDCP, and SDAP layers and supports simultaneous LTE and 5G NR O-DUs through multi-RAT operation.
- The E2 interface carries measurements from O-DU and O-CU to the near-RT RIC and returns configuration commands for near-real-time control through xApps.
- The OSS performs radio planning, testing, service management, orchestration, monitoring, and lifecycle operations for softwarized O-RAN components.
- A1, O1, and O2 support AI-related parameter exchange, RAN fault and performance management, and NFVI resource management, respectively.
IV. WHAT O-RAN CAN DO: CAPABILITIES AND USE CASES
O-RAN opens and disaggregates RAN functions to support innovation, multi-vendor deployment, flexible placement, and virtualization. Functional splits and open interfaces are central mechanisms for these capabilities.
- Opening RAN interfaces enables new developers and vendors to contribute innovations while potentially reducing maintenance costs and accelerating new services.
- O-RAN adopts functional split Option 2 for F1 between O-DU and O-CU and Option 7-2x for the fronthaul between O-DU and O-RU.
- Open interfaces allow operators to combine vendors’ strengths, share RAN functions, and deploy interoperable, flexible, independently fault-tolerant systems.
- O-CU, O-DU, and near-RT RIC functions can be deployed centrally or at the edge depending on bandwidth and latency requirements.
- Virtual-machine and container implementations on commercial off-the-shelf hardware support scalability, diverse network support, and interoperability.
B. Support for Different Timescales
O-RAN supports closed-loop RAN control across distinct timescales, combining slower policy and orchestration with faster service optimization. This enables operations ranging from network slicing to scheduling and beamforming.
- 5G RAN operations require timescales as fine as sub-millisecond granularity, while O-RAN distributes control across RICs operating at different timescales.The architecture defines non-RT and near-RT control loops for heterogeneous service requirements.
- The non-RT control loop operates at least 1 s through A1 and O1 for slice orchestration, infrastructure resource allocation, and policy guidance.Its decisions may occur over seconds, hours, or days.
- The near-RT control loop operates between 10 ms and 1 s through E2 for scheduling, beamforming, load balancing, and handover.xApps use user-session, MAC-layer, and PHY-layer data to control time-sensitive services.
- Combining near-RT fast processing with non-RT larger-timescale analysis is a distinctive O-RAN capability.This combination supports RAN operations across the architecture’s different control-loop timescales.
C. AI Integration and xApps/rApps
O-RAN integrates AI through non-RT and near-RT RIC functions, enabling data-driven resource management and xApp/rApp control. These capabilities target per-UE performance, automation, and management of RAN resources.
- The non-RT RIC manages policies, AI models, and enriched data for the near-RT RIC, which performs analytics and management functions.AI models can be transferred and updated periodically or when triggered.
- O-RAN enables per-UE performance enhancement using long-term traffic, coverage, and observed interference information.Near-RT resource management can reduce core-network burden and data-transfer overhead, improving system efficiency and latency.
- AI control use cases include load balancing, resource allocation, fault detection, and security across RAN layers.The use cases specify the implementing layer, AI operation, and training-data requirements.
- xApps support network slicing, deep-reinforcement-learning resource scheduling, and traffic steering based on traffic predictions.A traffic-steering xApp demonstrates dynamic user handover between cells.
- Non-RT RIC rApps can provide AI-based network-slice orchestration and data-driven policy management for RAN resources and xApps.
V. WHAT O-RAN CANNOT DO: LIMITATIONS AND R&D DIRECTIONS
The paper identifies limitations in current O-RAN specifications and outlines research and development directions to overcome them. It frames this work as part of O-RAN’s evolution for next-generation wireless deployments.
- The section identifies major limitations of current O-RAN specifications and outlines R&D directions for overcoming them.The stated aim is to spur O-RAN’s evolution as an architectural framework for next-generation wireless deployments.
A. End-to-End Security
O-RAN’s disaggregation, open interfaces, virtualization, and multi-vendor design expand the threat surface beyond the 3GPP architecture. The paper organizes these risks into six classes and discusses mutual authentication, trusted implementations, cryptography, and zero-trust security as research directions.
- Dynamic disaggregation, new interfaces, and the lower-layer split expand O-RAN’s threat surface beyond that of the 3GPP architecture.
- The six security-threat classes cover new functions, unprotected interfaces, the 7-2x split, multi-vendor decoupling, virtualization, and open-source software.Risks include misconfiguration, missing authentication or ciphering, decision conflicts, absent root of trust, insufficient virtualization security, and attack discovery through open-source inspection.
- O-RAN’s open interfaces and O-Cloud deployment introduce risks from unprotected communication, misconfiguration, poor isolation, and insufficient access management.
- Functional decoupling can leave hardware and software without a shared root of trust, motivating bottom-up trust chains and zero-trust security.The proposed zero-trust approach assumes no implicit trust and continuously evaluates risks.
- Security research directions include mutual authentication, trusted xApps and AI models, and cryptographic key generation, storage, rotation, and revocation.
B. Deterministic Latency
O-RAN’s open, multi-vendor architecture complicates deterministic latency guarantees, especially across functional splits and systems with incompatible delay specifications. Dynamic functional splitting is proposed to adapt network-function placement to differing latency requirements.
- Latency challenge: Open interfaces and multi-vendor support make data- and control-plane latency harder to control and optimize.The O-RAN latency model is based on the eCPRI reference model for delay management.
- Latency challenge: O-RAN timing requirements cannot be guaranteed when communicating with systems that do not follow its delay specifications.Packets arriving before or after their permitted windows should be discarded.
- Latency challenge: The specification does not prevent processing packets that arrive too early or too late, potentially disturbing control- or user-plane timing.
- Latency challenge: CPRI uses strictly periodic scheduling with a k28.5 time marker, whereas eCPRI relies on statistical synchronization without a fixed marker.The comparison concerns determinism in latency and delay measurement.
- R&D direction: Dynamic functional splitting varies network-function placement based on network feedback to satisfy latency requirements.The proposed architecture supports multiple functional splits through a single near-RT RIC for different timing requirements.
C. PHY Layer RT Control
The near-RT RIC provides intelligent RAN control but operates too slowly for many physical-layer processes. The paper proposes a sub-millisecond RT RIC control loop while identifying computational, energy, response-time, signaling, compatibility, and testing challenges for AI-based control.
- Control gap: The near-RT RIC operates at 10 ms–1 s, making it difficult to control many PHY-layer processes.A sub-millisecond real-time controller hosted at the O-CU or O-DU is therefore needed.
- RT control loop: The proposed RT RIC adds a third, real-time control loop that may reside in the O-CU, O-DU, or O-RU.It can host lower-layer RAN control applications called zApps.
- RT control loop: AI-based RT control faces constraints from node compute power, energy efficiency, sub-millisecond decision latency, and low-level PHY signaling overhead.These factors may adversely affect low-latency next-generation cellular networks.
- R&D directions: Near-constant-time zApps, subject-matter-expert validation, lightweight AI models, and RAN hardware acceleration are proposed as solutions.Echo state networks are given as an example of less data-intensive lightweight AI algorithms.
- AI control risks: Unpredictable closed-loop AI behavior can produce unstable configurations and performance losses, while incompatible xApps, rApps, and zApps may make conflicting decisions.
- AI control risks: AI control requires stability, robustness, predeployment certification, and online testing against unfamiliar data and operational conditions.Testing should address noise, interference, unexpected signals, conflicting messages, and incomplete or uncertain training data.
- AI control risks: Testing should cover both AI models and their data, supported by workflow validation services and high-fidelity live representations of deployed RAN functions.O-CU, O-DU, and O-RU configurations may differ within and across operators.
VI. CONCLUSIONS
The paper concludes that O-RAN’s open, intelligent, disaggregated architecture has critical specification limitations in security, latency, real-time control, and AI-application testing. It outlines technologies and R&D opportunities for extending O-RAN’s capabilities.
- Conclusion: The survey identifies security, latency, real-time control, and AI-based RAN-control testing as critical limitations of current O-RAN specifications.
- Conclusion: The paper outlines technologies and R&D opportunities intended to overcome these limitations and extend O-RAN’s architectural capabilities.