查看購物車
  • Simplified Chinese
  • English
  • Japanese
  • Korean
AI和RAN緩存－技術與市場
市場調查報告書
商品編碼
1499760

AI和RAN緩存－技術與市場

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個工作天內

價格
簡介目錄

本報告調查了 AI 和 RAN 緩存，並對主要 AI 方法及其在 RAN 快取中的使用進行了全面分析。

目錄

第1章 摘要整理

  • 主要的見解
  • 定量預測分類
  • 報告內容架構

第2章 AI/ML/DL的主要的概念的說明

  • AI
  • 機器學習（ML）
    • 監督式機器學習
    • 無監督機器學習
    • 強化機器學習
    • K近鄰法
  • 深度學習神經網（DLNN）
  • 值得注意的ML/DL演算法
    • 異常偵測
    • 人工神經網（ANN）
    • Bagged Trees
    • CART，SVM演算法
    • 叢集
    • 條件變分自動編碼器
    • CNN
    • 相關和叢集
    • 演化演算法與分散式學習
    • 前饋神經網
    • 圖神經網
    • 混合認知引擎（HCE）
    • 卡爾曼濾波
    • Markov決策過程
    • 多層感知器
    • 單純貝氏
    • 徑向基底函數
    • 隨機森林
    • 循環神經網路
    • 強化神經網路
    • SOM演算法
    • 稀疏貝氏學習

第3章 RAN的虛擬化

  • RAN和演變
    • E-UTRAN詳細內容
    • 5G-NR，NSA，SA
    • MEC
    • 硬性CPRI
  • 從RAN到vRAN的演變
  • VM為基礎的，容器為基礎的vRAN的比較
    • NFV架構
    • 容器的必要性
    • 微服務
    • 容器的形態
    • 容器部署方法
    • 有狀態容器、無狀態容器
    • 優勢容器
    • 容器所面臨的課題
  • RAN虛擬化，聯盟的案例
    • O-RAN架構概要
    • O-RAN的歷史
    • O-RAN的工作組
    • 開放? RAN（O-vRAN）
    • 通訊基礎設施計劃（TIP）OpenRAN

第4章 AI和RAN快取

  • O-RAN和AI
    • 簡介
    • RIC，xApps，rApps
    • WG2和ML
  • AI使用案例－快取
    • 背景
    • 方法論與課題
    • AI為基礎的方法

第5章 RANAI相關供應商的配合措施

  • 簡介
  • 值得注意的考察
  • 企業組織與概要
  • 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相關通訊業者的配合措施

  • 簡介
  • 值得注意的考察
  • 企業組織與概要
  • 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 定量分析與預測

  • 調查手法
  • 定量預測
    • 市場全體
    • 行動電話通訊的世代
    • 地理地區
簡介目錄

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