5G 시대의 RAN 자동화, SON, RIC, xApps, rApps : 기회, 과제, 전략, 예측(2024-2030년)
RAN Automation, SON, RIC, xApps & rApps in the 5G Era: 2024 - 2030 - Opportunities, Challenges, Strategies & Forecasts
상품코드:1542849
리서치사:SNS Telecom & IT
발행일:2024년 08월
페이지 정보:영문 580 Pages, 47 Tables and Figures
라이선스 & 가격 (부가세 별도)
한글목차
주요 조사 결과
이 보고서의 주요 조사 결과는 다음과 같습니다.
브라운필드 사업자들의 2차 오픈 RAN 인프라 구축과 함께 RIC, SMO, x/rApps에 대한 세계 지출은 2024-2027년 연평균 125% 이상 성장할 것으로 예상됩니다. RIC 인터페이스, RIC 플랫폼 간 용도 이식성, x/rApps 간 충돌 완화 등 표준화 격차 및 기술적 과제가 해결됨에 따라 2027년 말까지 연간 투자 규모가 7억 달러에 육박할 것으로 예상됩니다.
RAN 자동화 소프트웨어 및 서비스 시장에는 오픈 RAN 자동화, RAN 벤더의 SON 솔루션, 서드파티 C-SON 플랫폼, 베이스밴드 통합 지능형 RAN 용도, RAN 계획 및 최적화 소프트웨어, 검사/측정 솔루션 등이 포함됩니다. 솔루션 등이 포함되며, 같은 기간 CAGR 약 8%의 성장이 예상됩니다.
기존의 D-SON 및 C-SON 접근 방식의 단점은 개방형 인터페이스, 공통 정보 모델, 가상화, 소프트웨어 기반 네트워킹으로 이동하는 셀룰러 산업의 변화와 함께 RAN의 프로그래밍 가능성과 자동화 수준을 높이는 표준 기반 컴포넌트를 갖춘 개방형 RAN 자동화로 전환하고 있습니다.를 갖춘 개방형 RAN 자동화로의 전환을 촉진하고 있습니다.
오픈 RAN 자동화 움직임은 다양한 애플리케이션 개발자 커뮤니티의 혁신을 불러일으키고 있으며, SMO, Non-RT RIC, Near-RT RIC 제품을 제공하는 10여개 기업 외에도 50여개 이상의 기업이 xApps 및 rApps 개발에 적극적으로 참여하고 있습니다. 적극적으로 참여하고 있습니다.
일부 모바일 사업자는 RAN 자동화 전문성을 상품화하기 위해 전문 사업부를 설립하기도 했는데, NTT DoCoMo의 OREX 브랜드와 Rakuten Mobile의 자매회사인 Rakuten Symphony가 대표적인 두 가지 유명 사례입니다. 사례입니다. 또한 향후 수년간 노스이스턴 대학의 zTouch Networks나 TU Ilmenau의 AiVader와 같이 상용 수준의 개방형 RAN 자동화를 제공하는 학술기관의 스핀오프가 늘어날 것으로 예상됩니다.
SMO와 RIC 생태계는 브로드컴(Broadcom)의 VMware 인수와 HPE의 주니퍼 네트웍스(Juniper Networks) 인수 계획으로 인해 조기 통합의 조짐을 보이고 있습니다. 서드파티 RAN 자동화 플랫폼의 상업적 성공 여부에 따라 지난 10년간의 SON 붐을 연상케 하는 M&A(인수합병)가 더욱 활발해질 것으로 예상됩니다.
라이브 네트워크에서 SON 기반 RAN 자동화의 장점은 잘 알려져 있지만, RIC, SMO, x/rApps 접근 방식은 더 많은 기대를 모으고 있습니다. 예를 들어 일본의 브라운필드 사업자인 NTT DoCoMo는 Open RAN을 이용한 자동화를 통해 TCO를 최대 30%까지 절감하고 기지국 전력 소비를 최대 50%까지 줄일 수 있을 것으로 예상하고 있습니다.
국내 라이벌인 Rakuten Mobile이 RIC가 호스팅하는 RAN 자동화 용도를 사용하여 라이브 네트워크에서 셀당 약 17%의 에너지 절약을 이미 달성했다는 점은 주목할 가치가 있습니다. 이 그린필드 사업자는 성공적인 실험실 테스트 후 더 정교한 AI/ML 모델을 사용하여 절감률을 25%까지 높이는 것을 목표로 하고 있습니다.
공공 통신 사업자 네트워크 외에도 수직 산업 및 민간 무선 부문에서도 관심이 높아지고 있습니다. 미국 국방부(DoD)는 RIC가 호스팅하는 x/rApps의 잠재력을 적극적으로 탐색하고 있으며, 상용 및 전투기 통신 시나리오 모두에서 Open RAN 네트워크의 광범위한 보안 위협을 탐지, 분석 및 완화할 수 있는 능력을 강화하기 위해 RIC가 호스팅하는 x/rApps를 적극 활용하고 있습니다. 또 다른 예로 대만의 전자제품 제조업체인 인벤텍(Inventec)은 스마트 팩토리를 위한 프라이빗 5G 네트워크 솔루션의 일부로 실내 위치 추적 및 트래픽 조향에 rApps를 통합하고 있습니다.
세계의 RAN 자동화 시장에 대해 조사분석했으며, 밸류체인, 시장 성장 촉진요인, 채택 장벽, 실현 기술, 기능 분야, 사용 사례, 주요 동향, 향후 로드맵, 표준화, 사례 연구, 에코시스템 기업의 개요와 전략 등 상세한 평가를 제공하고 있습니다.
Potevio(CETC - China Electronics Technology Group Corporation)
QCT(Quanta Cloud Technology)
Qualcomm
Quanta Computer
Qucell Networks(InnoWireless)
RADCOM
Radisys(Reliance Industries)
Radware
Rakuten Symphony
Ranlytics
Ranplan Wireless
Rebaca Technologies
Red Hat(IBM)
RED Technologies
REPLY
RIMEDO Labs
Rivada Networks
Rohde & Schwarz
Ruijie Networks
RunEL
SageRAN(Guangzhou SageRAN Technology)
Samji Electronics
Samsung
Sandvine
Sercomm Corporation
ServiceNow
Shabodi
Signalwing
SIRADEL
Skyvera(TelcoDR)
SOLiD
Sooktha
Spectrum Effect
Spirent Communications
SRS(Software Radio Systems)
SSC(Shared Spectrum Company)
Star Solutions
Subex
Sunwave Communications
Supermicro(Super Micro Computer)
SynaXG Technologies
Systemics-PAB
T&W(Shenzhen Gongjin Electronics)
Tarana Wireless
TCS(Tata Consultancy Services)
Tech Mahindra
Tecore Networks
TECTWIN
Telrad Networks
TEOCO/Aircom
ThinkRF
TI(Texas Instruments)
TietoEVRY
Tropico(CPQD - Center for Research and Development in Telecommunications, Brazil)
TTG International
Tupl
ULAK Communication
Vavitel(Shenzhen Vavitel Technology)
VHT(Viettel High Tech)
VIAVI Solutions
VMware(Broadcom)
VNL - Vihaan Networks Limited(Shyam Group)
Wave Electronics
WDNA(Wireless DNA)
WIM Technologies
Wind River Systems
Wipro
Wiwynn(Wistron Corporation)
WNC(Wistron NeWeb Corporation)
Xingtera
ZaiNar
Z-Com
Zeetta Networks
Zinkworks
ZTE
zTouch Networks
Zyxel(Unizyx Holding Corporation)
제8장 시장 규모 추산과 예측
모바일 네트워크 자동화
네트워크 도메인 2차 시장
RAN 자동화 기능 분야
SON 기반 자동화 2차 시장
오픈 RAN 자동화 2차 시장
액세스 기술 세대
지역 세분화
북미
아시아태평양
유럽
중동 및 아프리카
라틴아메리카와 중앙아메리카
제9장 결론과 전략적 추천
왜 시장은 성장이 전망되는가?
향후 로드맵(2024-2030년)
RAN 자동화의 실제 이익과 TCO 삭감의 가능성 검토
RAN 엔지니어링의 역할에 대한 지능형 자동화의 영향
SON으로부터 오픈 RAN 자동화로의 이동
사용 사례와 AI/ML 알고리즘의 진화
에너지 효율과 지속가능성에 대한 주목의 증가
수직 산업과 민간 무선 자동화
x/rApp 개발자의 다양한 커뮤니티
SMO와 RIC 에코시스템의 통합 조짐
어느 RAN 자동화 플랫폼과 애플리케이션 벤더가 시장을 선도하고 있는가?
RAN 베이스밴드 제품내에서 서드파티 애플리케이션 호스팅의 전망
AI/ML 기반 6G 에어 인터페이스로의 길을 연다.
AI와 RAN 인프라의 융합
전략적 추천
제10장 전문가의 의견 - 인터뷰 기록
AirHop Communications
Amdocs
Groundhog Technologies
Innovile
Net AI
Nokia
P.I. Works
Qualcomm
Rakuten Mobile
RIMEDO Labs
KSA
영문 목차
영문목차
Automation of the RAN (Radio Access Network) - the most expensive, technically complex and power-intensive part of cellular infrastructure - is a key aspect of mobile operators' digital transformation strategies aimed at reducing their TCO (Total Cost of Ownership), improving network quality and achieving revenue generation targets. In conjunction with AI (Artificial Intelligence) and ML (Machine Learning), RAN automation has the potential to significantly transform mobile network economics by reducing the OpEx (Operating Expenditure)-to-revenue ratio, minimizing energy consumption, lowering CO2 (Carbon Dioxide) emissions, deferring avoidable CapEx (Captial Expenditure), optimizing performance, improving user experience and enabling new services.
The RAN automation market traces its origins to the beginning of the LTE era when SON (Self-Organizing Network) technology was introduced to reduce cellular network complexity through self-configuration, self-optimization and self-healing. While embedded D-SON (Distributed SON) capabilities such as ANR (Automatic Neighbor Relations) have become a standard feature in RAN products, C-SON (Centralized SON) solutions that abstract control from edge nodes for network-wide actions have been adopted by less than a third of world's approximately 800 national mobile operators due to constraints associated with multi-vendor interoperability, scalability and latency.
These shortcomings, together with the cellular industry's shift towards open interfaces, common information models, virtualization and software-driven networking, are driving a transition from the traditional D-SON and C-SON approach to Open RAN automation with standards-based components - specifically the Near-RT (Real-Time) and Non-RT RICs (RAN Intelligent Controllers), SMO (Service Management & Orchestration) framework, xApps (Extended Applications) and rApps (RAN Applications) - that enable greater levels of RAN programmability and automation.
Along with the ongoing SON to RIC transition, RAN automation use cases have also evolved over the last decade. For example, relatively basic MLB (Mobility Load Balancing) capabilities have metamorphosed into more sophisticated policy-guided traffic steering applications that utilize AI/ML-driven optimization algorithms to efficiently adapt to peaks and troughs in network load and service usage by dynamically managing and redistributing traffic across radio resources and frequency layers.
Due to the much higher density of radios and cell sites in the 5G era, energy efficiency has emerged as one of the most prioritized use cases of RAN automation as forward-thinking mobile operators push ahead with sustainability initiatives to reduce energy consumption, carbon emissions and operating costs without degrading network quality. Some of the other use cases that have garnered considerable interest from the operator community include network slicing enablement, application-aware optimization and anomaly detection.
While the benefits of SON-based RAN automation in live networks are well-known, expectations are even higher with the RIC, SMO and x/rApps approach. For example, Japanese brownfield operator NTT DoCoMo expects to lower its TCO by up to 30% and decrease power consumption at base stations by as much as 50% using Open RAN automation. It is worth highlighting that domestic rival Rakuten Mobile has already achieved approximately 17% energy savings per cell in its live network using RIC-hosted RAN automation applications. Following successful lab trials, the greenfield operator aims to increase savings to 25% with more sophisticated AI/ML models.
Although Open RAN automation efforts seemingly lost momentum beyond the field trial phase for the past couple of years, several commercial engagements have emerged since then, with much of the initial focus on the SMO, Non-RT RIC and rApps for automated management and optimization across Open RAN, purpose-built and hybrid RAN environments. Within the framework of its five-year $14 Billion Open RAN infrastructure contract with Ericsson, AT&T is adopting the Swedish telecommunications giant's SMO and Non-RT RIC solution to replace two legacy C-SON systems. In neighboring Canada, Telus has also initiated the implementation of an SMO and RIC platform along with its multi-vendor Open RAN deployment to transform up to 50% of its RAN footprint and swap out Huawei equipment from its 4G/5G network.
Similar efforts are also underway in other regions. For example, in Europe, Swisscom is deploying an SMO and Non-RT RIC platform to provide multi-technology network management and automation capabilities as part of a wider effort to future-proof its brownfield mobile network, while Deutsche Telekom is progressing with plans to develop its own vendor-independent SMO framework. Open RAN automation is also expected to be introduced as part of Vodafone Group's global tender for refreshing 170,000 cell sites.
Deployments of newer generations of proprietary SON-based RAN automation solutions have not stalled either. In its pursuit of achieving L4 (Highly Autonomous Network) operations, China Mobile has recently initiated the implementation of a hierarchical RAN automation platform and an associated digital twin system, starting with China's Henan province. Among other interesting examples, SoftBank is implementing a closed loop automation solution for cluster-wide RAN optimization in stadiums, event venues, and other strategic locations across Japan, which supports data collection and parameter tuning in 1-5 minute intervals as opposed to the 15-minute control cycle of traditional C-SON systems. It should be noted that the Japanese operator eventually plans to adopt RIC-hosted centralized RAN optimization applications in the future.
In addition, with the support of several mobile operators, including SoftBank, Vodafone, Bell Canada and Viettel, the idea of hosting third party applications for real-time intelligent control and optimization - also referred to as dApps (Distributed Applications) - directly within RAN baseband platforms is beginning to gain traction. As a counterbalance to this approach, Ericsson, Nokia, Huawei and other established RAN vendors are making considerable progress with a stepwise approach towards embedding AI and ML functionalities deeper into their DU (Distributed Unit) and CU (Centralized Unit) products in line with the 3GPP's long-term vision of an AI/ML-based air interface in the 6G era.
SNS Telecom & IT estimates that global spending on RIC, SMO and x/rApps will grow at a CAGR of more than 125% between 2024 and 2027 alongside the second wave of Open RAN infrastructure rollouts by brownfield operators. The Open RAN automation market will eventually account for nearly $700 Million in annual investments by the end of 2027 as standardization gaps and technical challenges in terms of the SMO-to-Non-RT RIC interface, application portability across RIC platforms and conflict mitigation between x/rApps are ironed out. The wider RAN automation software and services market - which includes Open RAN automation, RAN vendor SON solutions, third party C-SON platforms, baseband-integrated intelligent RAN applications, RAN planning and optimization software, and test/measurement solutions - is expected to grow at a CAGR of approximately 8% during the same period.
The "RAN Automation, SON, RIC, xApps & rApps in the 5G Era: 2024 - 2030 - Opportunities, Challenges, Strategies & Forecasts" report presents an in-depth assessment of the RAN automation market, including the value chain, market drivers, barriers to uptake, enabling technologies, functional areas, use cases, key trends, future roadmap, standardization, case studies, ecosystem player profiles and strategies. The report also provides global and regional market size forecasts for RAN and end-to-end mobile network automation from 2024 to 2030. The forecasts cover three network domains, nine functional areas, three access technologies and five regional markets.
The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.
Key Findings
The report has the following key findings:
SNS Telecom & IT estimates that global spending on RIC, SMO and x/rApps will grow at a CAGR of more than 125% between 2024 and 2027 alongside the second wave of Open RAN infrastructure rollouts by brownfield operators. The Open RAN automation market will eventually account for nearly $700 Million in annual investments by the end of 2027 as standardization gaps and technical challenges in terms of the SMO-to-Non-RT RIC interface, application portability across RIC platforms and conflict mitigation between x/rApps are ironed out.
The wider market for RAN automation software and services - which includes Open RAN automation, RAN vendor SON solutions, third party C-SON platforms, baseband-integrated intelligent RAN applications, RAN planning and optimization software, and test/measurement solutions - is expected to grow at a CAGR of approximately 8% during the same period.
The shortcomings of the traditional D-SON and C-SON approach, together with the cellular industry's shift towards open interfaces, common information models, virtualization and software-driven networking, are driving a transition to Open RAN automation with standards-based components that enable greater levels of RAN programmability and automation.
The Open RAN automation movement is stimulating innovation from a diversified community of application developers. In addition to well over a dozen providers of SMO, Non-RT RIC and Near-RT RIC products, more than 50 companies are actively engaged in the development of xApps and rApps.
Some mobile operators have established dedicated business units to commoditize their RAN automation expertise. NTT DoCoMo's OREX brand and Rakuten Mobile's sister company Rakuten Symphony are two well-known cases in point. In the coming years, we also expect to see more spinoffs of academic institutes with commercial-grade Open RAN automation offerings, such as Northeastern University's zTouch Networks and TU Ilmenau's AiVader.
The SMO and RIC ecosystem is exhibiting early signs of consolidation with Broadcom's takeover of VMware and HPE's planned acquisition of Juniper Networks, although both deals have much wider ranging implications for the AI infrastructure and networking industries. Depending on the commercial success of third party RAN automation platforms, we anticipate seeing further M&A (Mergers & Acquisition) activity reminiscent of the SON boom in the previous decade.
While the benefits of SON-based RAN automation in live networks are well-known, expectations are even higher with the RIC, SMO and x/rApps approach. For example, Japanese brownfield operator NTT DoCoMo expects to lower its TCO by up to 30% and decrease power consumption at base stations by as much as 50% using Open RAN automation.
It is worth highlighting that domestic rival Rakuten Mobile has already achieved approximately 17% energy savings per cell in its live network using RIC-hosted RAN automation applications. Following successful lab trials, the greenfield operator aims to increase savings to 25% with more sophisticated AI/ML models.
Outside of public mobile operator networks, interest is also growing in vertical industries and the private wireless segment. The U.S. DOD (Department of Defense) is actively exploring the potential of RIC-hosted x/rApps to enhance the ability to detect, analyze, and mitigate a wide range of security threats in Open RAN networks for both commercial and warfighter communication scenarios. Among other examples, Taiwanese electronics manufacturer Inventec has incorporated rApps for indoor positioning and traffic steering as part of its private 5G network solution for smart factories.
Although Open RAN automation efforts seemingly lost momentum beyond the field trial phase for the past couple of years, several commercial engagements have emerged since then, with much of the initial focus on the SMO, Non-RT RIC and rApps for automated management and optimization across Open RAN, purpose-built and hybrid RAN environments.
Within the framework of its five-year $14 Billion Open RAN infrastructure contract with Ericsson, AT&T is adopting the Swedish telecommunications giant's SMO and Non-RT RIC solution to replace two legacy C-SON systems. In neighboring Canada, Telus has also initiated the implementation of an SMO and RIC platform along with its multi-vendor Open RAN deployment to transform up to 50% of its RAN footprint and swap out Huawei equipment from its 4G/5G network.
Similar efforts are also underway in other regions. For example, in Europe, Swisscom is deploying an SMO and Non-RT RIC platform to provide multi-technology network management and automation capabilities as part of a wider effort to future-proof its brownfield mobile network, while Deutsche Telekom is progressing with plans to develop its own vendor-independent SMO framework. Open RAN automation is also expected to be introduced as part of Vodafone Group's global tender for refreshing 170,000 cell sites.
Deployments of newer generations of proprietary SON-based RAN automation solutions have not stalled either. In its pursuit of achieving L4 automation, China Mobile has recently initiated the implementation of a hierarchical RAN automation platform and an associated digital twin system, starting with China's Henan province.
Among other interesting examples, SoftBank is implementing a closed loop automation solution for cluster-wide RAN optimization in stadiums, event venues, and other strategic locations across Japan, which supports data collection and parameter tuning in 1-5 minute intervals as opposed to the 15-minute control cycle of traditional C-SON systems. It should be noted that the Japanese operator eventually plans to adopt RIC-hosted centralized RAN optimization applications in the future.
In addition, with the support of several mobile operators, including SoftBank, Vodafone, Bell Canada and Viettel, the idea of hosting third party applications for real-time intelligent control and optimization - also referred to as dApps - directly within RAN baseband platforms is beginning to gain traction.
As a counterbalance to this approach, Ericsson, Nokia, Huawei and other established RAN vendors are making considerable progress with a stepwise approach towards embedding AI and ML functionalities deeper into their DU and CU products in line with the 3GPP's long-term vision of an AI/ML-based air interface in the 6G era.
Beyond AI-driven RAN performance and efficiency improvements, mobile operators, technology suppliers and other stakeholders are also setting their sights on TCO benefits and new revenue opportunities enabled by the convergence of AI and RAN, including co-hosting vRAN and AI workloads on the same underlying infrastructure to maximize asset utilization and leveraging the RAN as a platform for edge AI services.
Topics Covered
The report covers the following topics:
Introduction to RAN automation
Value chain and ecosystem structure
Market drivers and challenges
Functional areas of RAN automation
RAN automation technology and architecture, including D-SON, C-SON, H-SON, Near-RT/Non-RT RICs, SMO, x/rApps, baseband-integrated intelligent RAN applications, RAN planning and optimization software, and test & measurement solutions
Review of over 70 RAN automation use cases, ranging from ANR, PCI and RACH optimization to advanced traffic steering, QoE-based resource allocation, energy savings, network slicing, private 5G automation, anomaly detection and dynamic RAN security
Key trends in intelligent RAN implementations, including the SON-to-RIC transition, closed loop automation, intent-driven management, operational AI/ML, Gen AI, data analytics and application awareness
Cross-domain mobile network automation enablers and application scenarios across the RAN, core and xHaul transport segments of cellular infrastructure
Detailed case studies of 20 production-grade RAN automation deployments and examination of ongoing projects covering both traditional SON and Open RAN automation approaches
Future roadmap of RAN automation
Standardization and collaborative initiatives
Profiles and strategies of more than 280 ecosystem players, including RAN infrastructure vendors, SON, RIC and SMO platform providers, x/rApp developers, AI/ML technology specialists, RAN planning and optimization software suppliers, and test/measurement solution providers
Exclusive interview transcripts from 10 companies across the RAN automation value chain: AirHop Communications, Amdocs, Groundhog Technologies, Innovile, Net AI, Nokia, P.I. Works, Qualcomm, Rakuten Mobile and RIMEDO Labs
Strategic recommendations for RAN automation solution providers and mobile operators
Market analysis and forecasts from 2024 to 2030
Forecast Segmentation
Market forecasts are provided for each of the following submarkets and their subcategories:
Mobile Network Automation Submarkets
RAN
Mobile Core
xHaul (Fronthaul, Midhaul & Backhaul) Transport
RAN Automation Functional Areas
SON-Based Automation
RAN Vendor SON Solutions
Third Party C-SON Platforms
Open RAN Automation
Non-RT RIC & SMO
Near-RT RIC
rApps
xApps
Baseband-Integrated Intelligent RAN Applications
RAN Planning & Optimization Software
Test & Measurement Solutions
Access Technology Generation Submarkets
LTE
5G NR
6G
Regional Markets
North America
Asia Pacific
Europe
Middle East & Africa
Latin & Central America
Key Questions Answered:
The report provides answers to the following key questions:
How big is the RAN automation opportunity?
What trends, drivers and challenges are influencing its growth?
What will the market size be in 2027, and at what rate will it grow?
Which submarkets and regions will see the highest percentage of growth?
What are the practical and quantifiable benefits of RAN automation based on live commercial deployments?
What is the TCO reduction and cost savings potential of RAN automation?
What is the adoption status of traditional SON solutions and Open RAN specifications-compliant Near-RT RIC, Non-RT RIC, SMO, xApps and rApps?
How can brownfield operators capitalize on Open RAN automation to simplify the management and optimization of hybrid RAN environments?
In what way will automation and AI/ML facilitate network slicing, MIMO, beamforming, lower-layer optimization and other advanced RAN capabilities in the 5G era?
What are the application scenarios of operational AI/ML and Gen AI in the RAN automation market?
What opportunities exist for automation in the mobile core and xHaul transport domains?
How does RAN automation ease the deployment and operation of private 5G networks?
In what way does intelligent automation impact the role of RAN engineers?
Who are the key ecosystem players, and what are their strategies?
Which RAN automation platform and application vendors are leading the market?
What strategies should RAN automation solution providers and mobile operators adopt to remain competitive?
Table of Contents
1. Chapter 1: Introduction
1.1. Executive Summary
1.2. Topics Covered
1.3. Forecast Segmentation
1.4. Key Questions Answered
1.5. Key Findings
1.6. Methodology
1.7. Target Audience
2. Chapter 2: An Overview of RAN Automation
2.1. What is RAN Automation?
2.1.1. Automating Repetitive Manual Tasks
2.1.2. RAN Analytics & Data-Driven Decision Making
2.1.3. AI (Artificial Intelligence) & ML (Machine Learning) Integration
2.1.4. SMO (Service Management & Orchestration) Frameworks
2.2. Levels of Automation in Intelligent RAN Implementations
