[시장보고서]세계의 AI 및 RAN 트래픽 최적화

세계의 AI 및 RAN 트래픽 최적화 - 기술 및 시장

AI and RAN Traffic Optimization - Technologies and Markets

상품코드 : 1499759

리서치사 : Insight Research Corporation

발행일 : 2024년 06월

페이지 정보 : 영문 136 Pages

라이선스 & 가격 (부가세 별도)

한글목차

샘플 요청 목록에 추가

AI는 RAN에 결정적인 진출을 이뤄 트래픽 관리와 최적화 방법을 변화시켰습니다. 몇년전부터 예측되고 있었지만, AI는 현재 RAN에 적극적으로 진입하고 그 구조와 기능을 재구성하고 있습니다.

AI는 효율성 향상, 지연 감소 및 네트워크 리소스 최적화를 통해 RAN 트래픽 관리를 강화합니다. 이러한 변화는 RAN이 단단한 모놀리식 구조에서 보다 세분화된 민첩하고 개방적인 시스템으로 이동함으로써 촉진됩니다. Software-Defined Networking(SDN), Network Functions Virtualization(NFV), Cloud-Native Functions(CNF), Open RAN(O-RAN)의 역할은 RAN 트래픽 최적화에 AI 영향을 미치는 데 중요합니다. 입니다. 이러한 기술의 발전은 AI가 RAN 내 트래픽을 최적화하기 위한 기반이 되어 네트워크 성능을 크게 향상시킬 수 있습니다.

이 보고서는 AI 및 RAN 트래픽 최적화에 대한 조사 분석을 통해 이 변화의 길을 파악하고 트래픽 최적화에 주목한 중요한 고찰과 시장 예측을 제공합니다.

AI
머신러닝(ML)
- 교사 있음 머신러닝
- 교사 없음 머신러닝
- 강화 머신러닝
- K근방법
딥러닝 신경망(DLNN)
주목해야 할 ML/DL 알고리즘
- 이상 감지
- 인공 신경망(ANN)
- 가방드 트리즈
- CART, SVM 알고리즘
- 클러스터링
- 조건부 변분 오토엔코더
- CNN
- 상관과 클러스터링
- 진화적 알고리즘과 분산 학습
- 피드 포워드 신경망
- 그래프 신경망
- 하이브리드 인지 엔진(HCE)
- 칼만 필터
- 마르코프 결정 과정
- 다층 퍼셉트론
- 나이브 베이즈
- 방사 기저 함수
- 랜덤 포레스트
- 리커런트 신경망
- 강화 신경망
- SOM 알고리즘
- 스파스 베이지안 학습

제3장 RAN 가상화

RAN과 그 진화
- E-UTRAN의 상세
- 5G-NR, NSA, SA
- MEC
- 리지드 CPRI
RAN에서 vRAN으로의 진화
VM 기반, 컨테이너 기반 vRAN 비교
- NFV 아키텍처
- 컨테이너의 필요성
- 마이크로서비스
- 컨테이너 형태
- 컨테이너 전개 방법
- 스테이트풀 컨테이너, 스테이트리스 컨테이너
- 어드밴티지 컨테이너
- 컨테이너가 직면하는 과제
RAN 가상화, 얼라이언스의 스토리
- O-RAN 아키텍처 개요
- O-RAN의 역사
- O-RAN의 작업 그룹
- 오픈 vRAN(O-vRAN)
- 통신 인프라 프로젝트(TIP) OpenRAN

제4장 AI 및 RAN 트래픽 최적화

O-RAN과 AI
- 소개
- RIC, xApps, rApps
- WG2와 ML
AI 이용 사례 - 트래픽 최적화
- 배경
- 방법론과 과제
- AI 기반 접근법

제5장 RAN용 AI에 관한 벤더의 대처

소개
주목해야 할 고찰
기업과 조직의 개요
Aira Channel Prediction xApp
Aira Dynamic Radio Network Management rApp
AirHop Auptim
Aspire Anomaly Detection rApp
Cisco Ultra Traffic Optimization
Capgemini RIC
Cohere MU-MIMO Scheduler
DeepSig OmniSig
Deepsig OmniPHY
Ericsson Radio System
Ericsson RIC
Fujitsu Open RAN Compliant RUs
HCL iDES rApp
Huawei PowerStar
Juniper RIC/Rakuten Symphony Symworld
Mavenir mMIMO 64TRX
Mavenir RIC
Net AI xUPscaler Traffic Predictor xApp
Nokia RAN Intelligent Controller
Nokia AVA
Nokia ReefShark Soc
Nvidia AI-on-5G platform
Opanga Networks
PI Works Intelligent PCI Collision and Confusion Detection rApp
Qualcomm RIC
Qualcomm Cellwize CHIME
Qualcomm Traffic Management Solutions
Rimedo Policy-controlled Traffic Steering xApp
Samsung Network Slice Manager
ZTE PowerPilot
VMware RIC

제6장 RAN용 AI에 관한 통신 사업자의 대처

소개
주목해야 할 고찰
기업과 조직의 개요
AT&T Inc
Axiata Group Berhad
Bharti Airtel
China Mobile
China Telecom
China Unicom
CK Hutchison Holdings
Deutsche Telekom
Etisalat
Globe Telecom Inc
NTT DoCoMo
MTN Group
Ooredoo
Orange
PLDT Inc
Rakuten Mobile
Reliance Jio
Saudi Telecom Company
Singtel
SK Telecom
Softbank
Telefonica
Telenor
Telkomsel
T-Mobile US
Verizon
Viettel Group
Vodafone

제7장 정량 분석과 예측

조사 방법
정량 예측
- 시장 전체
- 휴대폰 통신의 세대
- 지리적 지역

BJH

영문 목차

영문목차

AI has made a decisive entry into the RAN, transforming how traffic is managed and optimized. After years of anticipation, AI is now actively present in RAN, reshaping its structure and capabilities. Our comprehensive new report, "AI and RAN Traffic Optimization- Technologies and Markets," delves into this transformative journey, offering key insights and market forecasts focused on traffic optimization.

AI is enhancing RAN traffic management by improving efficiency, reducing latency, and optimizing network resources. This transformation is facilitated by the transition of RAN from a rigid, monolithic structure to a more disaggregated, agile, and open system. The roles of Software-Defined Networking (SDN), Network Functions Virtualization (NFV), Cloud-Native Functions (CNF), and Open RAN (O-RAN) are crucial in enabling AI's impact on RAN traffic optimization. These technological advancements provide the foundation for AI to optimize traffic within RAN, leading to significant improvements in network performance.

Highlights:

Insight Research breaks down the market for AI in RAN traffic optimization two criteria- mobility generation and geographical regions.
Insight Research considers two mobility generations- 5G and others; and four geographical regions- NA, EMEA, APAC and CALA.

1. Executive Summary

1.1. Key observations
1.2. Quantitative Forecast Taxonomy
1.3. Report Organization

2. AI/ML/DL Key Concepts Explainer

2.1. Artificial Intelligence
2.2. Machine Learning (ML)
- 2.2.1. Supervised Machine Learning
- 2.2.2. Unsupervised Machine Learning
- 2.2.3. Reinforced Machine Learning
- 2.2.4. K-Nearest Neighbor
2.3. Deep Learning Neural Network (DLNN)
2.4. Noteworthy ML and DL Algorithms
- 2.4.1. Anomaly Detection
- 2.4.2. Artificial Neural Networks (ANN)
- 2.4.3. Bagged Trees
- 2.4.4. CART and SVM Algorithms
- 2.4.5. Clustering
- 2.4.6. Conditional Variational Autoencoder
- 2.4.7. Convolutional Neural Network
- 2.4.8. Correlation and Clustering
- 2.4.9. Evolutionary Algorithms and Distributed Learning
- 2.4.10. Feed Forward Neural Network
- 2.4.11. Graph Neural Networks
- 2.4.12. Hybrid Cognitive Engine (HCE)
- 2.4.13. Kalman Filter
- 2.4.14. Markov Decision Processes
- 2.4.15. Multilayer Perceptron
- 2.4.16. Naive Bayes
- 2.4.17. Radial Basis Function
- 2.4.18. Random Forest
- 2.4.19. Recurrent Neural Network
- 2.4.20. Reinforced Neural Network
- 2.4.21. SOM Algorithm
- 2.4.22. Sparse Bayesian Learning

3. Virtualization of the RAN

3.1. The RAN and its Evolution
- 3.1.1. Closer Look at E-UTRAN
- 3.1.2. 5G- NR, NSA and SA
- 3.1.3. MEC
- 3.1.4. The Rigid CPRI
3.2. The Progression of the RAN to the vRAN
3.3. How VM-based and Container-based vRANs Compare?
- 3.3.1. NFV architecture
- 3.3.2. The Need for Containers
- 3.3.3. Microservices
- 3.3.4. Container Morphology
- 3.3.5. Container Deployment Methodologies
- 3.3.6. Stateful and Stateless Containers
- 3.3.7. Advantage Containers
- 3.3.8. Challenges Confronting Containers
3.4. RAN Virtualization A Story of Alliances
- 3.4.1. O-RAN Architecture Overview
- 3.4.2. History of O-RAN
- 3.4.3. Workgroups of O-RAN
- 3.4.4. Open vRAN (O-vRAN)
- 3.4.5. Telecom Infra Project (TIP) OpenRAN

4. AI and RAN Traffic Optimization

4.1. O-RAN and AI
- 4.1.1. Introduction
- 4.1.2. RIC, xApps and rApps
- 4.1.3. WG2 and ML
4.2. AI Use-Case - Traffic Optimization
- 4.2.1. Background
- 4.2.2. Methodologies and Challenges
- 4.2.3. AI-based Approaches

5. Vendor Initiatives for AI in the RAN

5.1. Introduction
5.2. Salient Observations
5.3. Company and Organization Summary
5.4. Aira Channel Prediction xApp
5.5. Aira Dynamic Radio Network Management rApp
5.6. AirHop Auptim
5.7. Aspire Anomaly Detection rApp
5.8. Cisco Ultra Traffic Optimization
5.9. Capgemini RIC
5.10. Cohere MU-MIMO Scheduler
5.11. DeepSig OmniSig
5.12. Deepsig OmniPHY
5.13. Ericsson Radio System
5.14. Ericsson RIC
5.15. Fujitsu Open RAN Compliant RUs
5.16. HCL iDES rApp
5.17. Huawei PowerStar
5.18. Juniper RIC/Rakuten Symphony Symworld
5.19. Mavenir mMIMO 64TRX
5.20. Mavenir RIC
5.21. Net AI xUPscaler Traffic Predictor xApp
5.22. Nokia RAN Intelligent Controller
5.23. Nokia AVA
5.24. Nokia ReefShark Soc
5.25. Nvidia AI-on-5G platform
5.26. Opanga Networks
5.27. P.I. Works Intelligent PCI Collision and Confusion Detection rApp
5.28. Qualcomm RIC
5.29. Qualcomm Cellwize CHIME
5.30. Qualcomm Traffic Management Solutions
5.31. Rimedo Policy-controlled Traffic Steering xApp
5.32. Samsung Network Slice Manager
5.33. ZTE PowerPilot
5.34. VMware RIC

6. Telco Initiatives for AI in the RAN

6.1. Introduction
6.2. Salient Observations
6.3. Company and Organization Summary
6.4. AT&T Inc
6.5. Axiata Group Berhad
6.6. Bharti Airtel
6.7. China Mobile
6.8. China Telecom
6.9. China Unicom
6.10. CK Hutchison Holdings
6.11. Deutsche Telekom
6.12. Etisalat
6.13. Globe Telecom Inc
6.14. NTT DoCoMo
6.15. MTN Group
6.16. Ooredoo
6.17. Orange
6.18. PLDT Inc
6.19. Rakuten Mobile
6.20. Reliance Jio
6.21. Saudi Telecom Company
6.22. Singtel
6.23. SK Telecom
6.24. Softbank
6.25. Telefonica
6.26. Telenor
6.27. Telkomsel
6.28. T-Mobile US
6.29. Verizon
6.30. Viettel Group
6.31. Vodafone

7. Quantitative Analysis and Forecasts

7.1. Research Methodology
7.2. Quantitative Forecasts
- 7.2.1. Overall Market
- 7.2.2. Mobile Telephony Generations
- 7.2.3. Geographical Regions

Tables and Figures

Figure 3-1: VNF versus CNF Stacks
Figure 3-2: O-RAN High-Level Architecture
Figure 3-3: O-RAN High-Level Architecture
Figure 3-4: Architecture of vRAN Base Station as Visualized by TIP
Figure 4-1: Reinforcement learning model training and actor locations per O-RAN WG2
Figure 4-2: AI/ML Workflow in the O-RAN RIC as proposed O-RAN WG2
Figure 4-3: AI/ML deployment scenarios
Table 5-1: AI in RAN Product and Solution Vendor Summary
Figure 5-1: The Aira channel detection xApp functional blocks
Figure 5-2: Modules of the Aspire Anomaly Detection rApp
Figure 5-3: OmniPHY Module Drop in Typical vRAN Stack Overview
Figure 5-4: Ericsson IAP
Figure 5-5: HCL iDES rApp Architecture
Figure 5-6: Working of the Net Ai xUPscaler
Figure 5-7: Nokia RIC programmability via AI/ML and Customized Applications
Figure 5-8: Timesharing the GPU in Nvidia Aerial A100
Figure 5-8: Rimedo TS xApp in the O-RAN architecture
Figure 5-9: Rimedo TS xApp in the VMware RIC
Figure 5-10: PowerPilot Solution Evolution
Table 6-1: AI in RAN Telco Profile Snapshot
Table 7-1: Addressable Market in Traffic Optimization End-Application in Mobile RAN for AI and Related Technologies 2023-2028 ($ million)
Table 7-2: Addressable Market in Traffic Optimization Application in Mobile RAN for AI and Related Technologies; by Mobile Telephony Generation 2023-2028 ($ million)
Figure 7-1: Share of Addressable Market in Traffic Optimization End-Application in Mobile RAN for AI and Related Technologies; by Mobile Telephony Generation 2023-2028
Table 7-3: Addressable Market in Traffic Optimization End-Application Mobile RAN for AI and Related Technologies; by Geographical Region 2023-2028 ($ million)
Figure 7-2: Share of Addressable Market in Traffic Optimization End-Application Mobile RAN for AI and Related Technologies; by Geographical Region 2023-2028