AI and RAN Energy Management - Technologies and Markets
상품코드:1499761
리서치사:Insight Research Corporation
발행일:2024년 06월
페이지 정보:영문 136 Pages
라이선스 & 가격 (부가세 별도)
한글목차
이 보고서는 AI와 무선 액세스 네트워크(RAN)의 에너지 관리 기술이 진화하는 상황을 조사했습니다. 주요 개념, 기술 발전, 시장 동향, 예측을 다루며, 특히 에너지 관리의 맥락에서 AI가 RAN 생태계에 미치는 중대한 영향을 파악합니다.
인공지능 기술이 계속 발전함에 따라 RAN 통합은 운영 측면에서 큰 이점을 제공하고 혁신과 성장의 새로운 길을 열어줍니다.
목차
제1장 주요 요약
주요 견해
정량 예측 분류
보고서의 구성
제2장 AI/ML/DL의 주요 개념 설명
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
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 RAN의 에너지 관리
O-RAN과 AI
소개
RIC, xApps, rApps
WG2와 ML
AI 이용 사례 - 에너지 관리
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
This comprehensive report explores the evolving landscape of Artificial Intelligence (AI) and Radio Access Network (RAN) energy management technologies. Covering key concepts, technological advancements, market trends, and future forecasts, this study delves into the significant impact of AI on the RAN ecosystem, particularly in the context of energy management.
As AI technologies continue to evolve, their integration into RAN will provide significant operational benefits and open new avenues for innovation and growth. This report offers valuable insights for network planners, vendors, and telecom operators looking to stay ahead in the evolving landscape of AI and RAN energy management. Get your copy today and lead the way in network innovation.
Highlights:
Insight Research breaks down the market for AI in RAN energy management 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.
Table of Contents
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
2.4.15. 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 Energy Management
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 - Energy Management
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
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 Energy Management End-Application in Mobile RAN for AI and Related Technologies 2023-2028 ($ million)
Table 7-2: Addressable Market in Energy Management End-Application in Mobile RAN for AI and Related Technologies; by Mobile Telephony Generation 2023-2028 ($ million)
Figure 7-1: Share of Addressable Market in Energy Management End-Application in Mobile RAN for AI and Related Technologies; by Mobile Telephony Generation 2023-2028
Table 7-3: Addressable Market in Energy Management End-Application in Mobile RAN for AI and Related Technologies; by Geographical Region 2023-2028 ($ million)
Figure 7-2: Share of Addressable Market in Energy Management End-Application in Mobile RAN for AI and Related Technologies; by Geographical Region 2023-2028
