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AI和RAN缓存－技术与市场
市场调查报告书
商品编码
1499760

AI和RAN缓存－技术与市场

AI and RAN Caching - Technologies and Markets
出版日期: | 出版商: Insight Research Corporation | 英文 136 Pages | 商品交期: 最快1-2个工作天内

价格
简介目录

本报告调查了 AI 和 RAN 缓存，并对主要 AI 方法及其在 RAN 快取中的使用进行了全面分析。

目录

第1章 摘要整理

  • 主要的见解
  • 定量预测分类
  • 报告内容架构

第2章 AI/ML/DL的主要的概念的说明

  • AI
  • 机器学习（ML）
    • 监督式机器学习
    • 无监督机器学习
    • 强化机器学习
    • K近邻法
  • 深度学习神经网（DLNN）
  • 值得注意的ML/DL演算法
    • 异常侦测
    • 人工神经网（ANN）
    • Bagged Trees
    • CART，SVM演算法
    • 丛集
    • 条件变分自动编码器
    • CNN
    • 相关和丛集
    • 演化演算法与分散式学习
    • 前馈神经网
    • 图神经网
    • 混合认知引擎（HCE）
    • 卡尔曼滤波
    • Markov决策过程
    • 多层感知器
    • 单纯贝氏
    • 径向基底函数
    • 随机森林
    • 循环神经网路
    • 强化神经网路
    • SOM演算法
    • 稀疏贝氏学习

第3章 RAN的虚拟化

  • RAN和演变
    • E-UTRAN详细内容
    • 5G-NR，NSA，SA
    • MEC
    • 硬性CPRI
  • 从RAN到vRAN的演变
  • VM为基础的，容器为基础的vRAN的比较
    • NFV架构
    • 容器的必要性
    • 微服务
    • 容器的形态
    • 容器部署方法
    • 有状态容器、无状态容器
    • 优势容器
    • 容器所面临的课题
  • RAN虚拟化，联盟的案例
    • O-RAN架构概要
    • O-RAN的历史
    • O-RAN的工作组
    • 开放? RAN（O-vRAN）
    • 通讯基础设施计划（TIP）OpenRAN

第4章 AI和RAN快取

  • O-RAN和AI
    • 简介
    • RIC，xApps，rApps
    • WG2和ML
  • AI使用案例－快取
    • 背景
    • 方法论与课题
    • AI为基础的方法

第5章 RANAI相关供应商的配合措施

  • 简介
  • 值得注意的考察
  • 企业组织与概要
  • 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
  • P.I. 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

第6章 RANAI相关通讯业者的配合措施

  • 简介
  • 值得注意的考察
  • 企业组织与概要
  • 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

第7章 定量分析与预测

  • 调查手法
  • 定量预测
    • 市场全体
    • 行动电话通讯的世代
    • 地理地区
简介目录

The report offers a thorough analysis of key AI methodologies and their applications in RAN caching, covering essential concepts such as Machine Learning (ML) and Deep Learning (DL). It highlights the use of AI in predicting data access patterns, optimizing cache placement, and improving overall network efficiency. By leveraging AI, RAN caching can dynamically adapt to changing network conditions, enhancing user experience and operational efficiency.

Market forecasts included in the report provide valuable insights into the addressable market size for AI-driven RAN caching solutions, segmented by mobile telephony generations and geographical regions. The report also profiles leading vendors and their AI solutions for RAN caching, offering a comprehensive view of the competitive landscape and emerging opportunities.

Unlock the full potential of AI and RAN Caching. Dive into our report for strategic insights and stay ahead in this rapidly evolving field.

Highlights:

  • Insight Research breaks down the market for AI in RAN caching 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
    • 2.4.14. 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 Caching

  • 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 - Caching
    • 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 G2
  • 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 Caching End-Application in Mobile RAN for AI and Related Technologies 2023-2028 ($ million)
  • Table 7-2: Addressable Market in Caching 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 Caching End- Application in Mobile RAN for AI and Related Technologies; by Mobile Telephony Generation 2023-2028
  • Table 7-3: Addressable Market in Caching End-Application in Mobile RAN for AI and Related Technologies; by Geographical Region 2023-2028 ($ million)
  • Figure 7-2: Share of Addressable Market in Caching End-Application in Mobile RAN for AI and Related Technologies; by Geographical Region 2023-2028