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AI 和 RAN：它们的移动速度有多快？
市场调查报告书
商品编码
1448840

AI 和 RAN：它们的移动速度有多快？

AI and RAN - How Fast Will They Run?
出版日期: | 出版商: Insight Research Corporation | 英文 169 Pages | 商品交期: 最快1-2个工作天内

价格
简介目录

本报告探讨了人工智慧 (AI) 和无线存取网路 (RAN) 的融合，强调了它们不断发展的关係并预测了它们未来的发展轨迹。

范例视图 图 1-1：AI 在 RAN 的最终使用：收入份额趋势
来源：Insight Research

近年来，人工智慧已成为推动各产业创新的关键力量。 特别是在电信领域，我们正在见证人工智慧整合到 RAN 架构中所产生的变革性影响。 本报告探讨了这种协同作用，并揭示了它们融合背后的动态及其对电信业的影响。

分析概述

  • 从 2023 年到 2028 年，RAN 人工智慧的潜在市场将以每年 45% 的惊人速度成长。
  • 5G RAN 中人工智慧的潜在市场将比前几代电话技术成长得更快。
  • 亚太地区将成为 RAN 快取应用人工智慧的最大市场。

目录

第 1 章执行摘要

第 2 章 AI/ML/DL：关键概念解释

  • 人工智慧 (AI)
  • 机器学习 (ML)
    • 监督式机器学习
    • 无监督机器学习
    • 增强的机器学习
    • k-最近邻法
  • 深度学习神经网路 (DLNN)
  • 着名的机器学习/深度学习演算法
    • 异常检测
    • 人工神经网路 (ANN)
    • 有缺陷的决策树
    • CART/SVM演算法
    • 聚类
    • 条件变分自动编码器
    • 卷积神经网络
    • 关联/聚类
    • 演化演算法与分散式学习
    • 前馈神经网路 (FNN)
    • 图神经网路 (GNN)
    • 混合认知引擎 (HCE)
    • 卡尔曼滤波器
    • 马可夫决策过程
    • 多层感知器 (MLP)
    • 朴素贝叶斯
    • 径向基底函数
    • 随机森林
    • 循环神经网络
    • 强化神经网络
    • SOM演算法
    • 稀疏贝叶斯学习

第 3 章 RAN 虚拟化

  • RAN 及其演变
    • E-UTRAN 详细信息
    • 5G-NR、NSA、SA
    • MEC
    • 刚性 CPRI
  • 从 RAN 到 vRAN 的演进
  • 如何比较基于虚拟机器的 vRAN 和基于容器的 vRAN？
    • NFV 架构
    • 需要容器
    • 微服务
    • 容器形式
    • 如何安装容器
    • 有状态和无状态容器
    • 优势容器
    • 容器面临的挑战
  • RAN 虚拟化：联盟故事
    • O-RAN 架构概述
    • O-RAN 的历史
    • O-RAN 工作组
    • 开放 vRAN (O-vRAN)
    • TIP（通讯基础设施项目）OpenRAN

第 4 章 AI 在 RAN 的最终使用

  • O-RAN 和人工智慧
    • 简介
    • RIC、xApp、rApp
    • WG2 和 ML
  • 人工智慧用例：流量优化
    • 背景
    • 方法论与问题
    • 基于人工智慧的方法
  • 人工智慧用例：快取
  • 人工智慧用例：能源管理
  • 人工智慧用例：编码

第 5 章 RAN 中的人工智慧：供应商的努力

  • 简介
  • 主要分析结果
  • 公司/组织概览
  • 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 RAN 中的人工智慧：电信公司的努力

  • 简介
  • 主要分析结果
  • 公司/组织概览
  • 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

  • 分析法
  • 预测的分类方法
  • 全球市场
    • 整个市场
    • 手机世代
    • 按地区
  • 流量优化
    • 整个市场
    • 手机世代
    • 按地区
  • 快取
    • 整个市场
    • 手机世代
    • 按地区
  • 能源管理
    • 整个市场
    • 手机世代
    • 按地区
  • 编码
    • 整个市场
    • 手机世代
    • 按地区
简介目录

This report delves into the intersection of Artificial Intelligence (AI) and Radio Access Network (RAN), shedding light on their evolving relationship and forecasting their future trajectories.

SAMPLE VIEW

Figure 1-1: Progression of revenue shares of AI end-applications in the RAN

Source: Insight Research

In recent years, AI has emerged as a pivotal force driving innovation across industries. The telecom sector, in particular, has witnessed a transformative impact with the integration of AI into RAN architecture. The report explores this synergy, uncovering the dynamics behind their convergence and the implications for the telecommunication landscape.

"At Insight Research, we recognize the seismic shifts occurring within the telecommunications industry, and our latest report elucidates the symbiotic relationship between AI and RAN," remarked Kaustubha Parkhi, Principal Analyst at Insight Research. "We're witnessing a paradigm shift in RAN architecture, with AI playing a pivotal role in driving efficiency, agility, and performance."

Key Highlights from the Report:

  • The addressable market for AI in RAN will grow by an impressive 45% annually during 2023-2028
  • The addressable market for AI in 5G RAN will grow faster than earlier telephony generations
  • The APAC region will be the largest market for AI in RAN caching applications

With a meticulous breakdown of the market by application, region, and telephony generations, the report offers unparalleled quantitative insights, empowering stakeholders to make informed decisions in a rapidly evolving landscape.

Insight Research's "AI and RAN - How Fast Will They Run?" report is essential reading for telecom operators, technology providers, policymakers, and investors seeking to navigate the evolving landscape of AI-driven telecommunications infrastructure.

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. Naïve 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. End-applications for AI in the RAN

  • 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
  • 4.3. AI Use-Case - Caching
    • 4.3.1. Background
    • 4.3.2. Methodologies and Challenges
    • 4.3.3. AI-based Approaches
  • 4.4. AI Use-Case - Energy Management
    • 4.4.1. Background
    • 4.4.2. Methodologies and Challenges
    • 4.4.3. AI-based Approaches
  • 4.5. AI Use-Case - Coding
    • 4.5.2. 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. Forecast Taxonomy
  • 7.3. Global Market
    • 7.3.1. Overall Market
    • 7.3.2. Mobile Telephony Generations
    • 7.3.3. Geographical Regions
  • 7.4. Traffic Optimization
    • 7.4.1. Overall Market
    • 7.4.2. Mobile Telephony Generations
    • 7.4.3. Geographical Regions
  • 7.5. Caching
    • 7.5.1. Overall Market
    • 7.5.2. Mobile Telephony Generations
    • 7.5.3. Geographical Regions
  • 7.6. Energy Management
    • 7.6.1. Overall Market
    • 7.6.2. Mobile Telephony Generations
    • 7.6.3. Geographical Regions
  • 7.7. Coding
    • 7.7.1. Overall Market
    • 7.7.2. Mobile Telephony Generations
    • 7.7.3. Geographical Regions

Figures & Tables

  • Figure 1-1: Progression of revenue shares of AI end-applications in the RAN
  • 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
  • Figure 7-1: AI in the RAN Market Forecast Taxonomy
  • Table 7-1: Addressable Market in Mobile RAN for AI and Related Technologies 2023-2028 ($ million)
  • Table 7-2: Addressable Market in Mobile RAN for AI and Related Technologies; by Mobile Telephony Generation 2023-2028 ($ million)
  • Figure 7-2: Share of Addressable Market in Mobile RAN for AI and Related Technologies; by Mobile Telephony Generation 2023-2028
  • Table 7-3: Addressable Market in Mobile RAN for AI and Related Technologies; by Geographical Region 2023-2028 ($ million)
  • Figure 7-3: Share of Addressable Market in Mobile RAN for AI and Related Technologies; by Geographical Region 2023-2028
  • Table 7-4: Addressable Market in Traffic Optimization End-Application in Mobile RAN for AI and Related Technologies 2023-2028 ($ million)
  • Table 7-5: Addressable Market in Traffic Optimization Application in Mobile RAN for AI and Related Technologies; by Mobile Telephony Generation 2023-2028 ($ million)
  • Figure 7-4: Share of Addressable Market in Traffic Optimization End-Application in Mobile RAN for AI and Related Technologies; by Mobile Telephony Generation 2023-2028
  • Table 7-6: Addressable Market in Traffic Optimization End-Application Mobile RAN for AI and Related Technologies; by Geographical Region 2023-2028 ($ million)
  • Figure 7-5: Share of Addressable Market in Traffic Optimization End-Application Mobile RAN for AI and Related Technologies; by Geographical Region 2023-2028
  • Table 7-7: Addressable Market in Caching End-Application in Mobile RAN for AI and Related Technologies 2023-2028 ($ million)
  • Table 7-8: Addressable Market in Caching End-Application in Mobile RAN for AI and Related Technologies; by Mobile Telephony Generation 2023-2028 ($ million)
  • Figure 7-6: Share of Addressable Market in Caching End-Application in Mobile RAN for AI and Related Technologies; by Mobile Telephony Generation 2023-2028
  • Table 7-9: Addressable Market in Caching End-Application in Mobile RAN for AI and Related Technologies; by Geographical Region 2023-2028 ($ million)
  • Figure 7-7: Share of Addressable Market in Caching End-Application in Mobile RAN for AI and Related Technologies; by Geographical Region 2023-2028
  • Table 7-10: Addressable Market in Energy Management End-Application in Mobile RAN for AI and Related Technologies 2023-2028 ($ million)
  • Table 7-11: Addressable Market in Energy Management End-Application in Mobile RAN for AI and Related Technologies; by Mobile Telephony Generation 2023-2028 ($ million)
  • Figure 7-8: Share of Addressable Market in Energy Management End-Application in Mobile RAN for AI and Related Technologies; by Mobile Telephony Generation 2023-2028
  • Table 7-12: Addressable Market in Energy Management End-Application in Mobile RAN for AI and Related Technologies; by Geographical Region 2023-2028 ($ million)
  • Figure 7-9: Share of Addressable Market in Energy Management End-Application in Mobile RAN for AI and Related Technologies; by Geographical Region 2023-2028
  • Table 7-13: Addressable Market in Coding End-Application in Mobile RAN for AI and Related Technologies 2023-2028 ($ million)
  • Table 7-14: Addressable Market in Coding End-Application in Mobile RAN for AI and Related Technologies; by Mobile Telephony Generation 2023-2028 ($ million)
  • Figure 7-10: Share of Addressable Market in Coding End-Application in Mobile RAN for AI and Related Technologies; by Mobile Telephony Generation 2023-2028
  • Table 7-15: Addressable Market in Coding End-Application in Mobile RAN for AI and Related Technologies; by Geographical Region 2023-2028 ($ million)
  • Figure 7-11: Share of Addressable Market in Coding End-Application in Mobile RAN for AI and Related Technologies; by Geographical Region 2023-2028