AI and RAN Caching - Technologies and Markets

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Report
136 Pages
June 2024
Region: Global
Insight Research
ID: 5979834

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 the report for strategic insights and stay ahead in this rapidly evolving field.

Report Highlights

The report breaks down the market for AI in RAN caching two criteria- mobility generation and geographical regions.
The report 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 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. 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

Figures & Tables

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 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

Samples

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Executive Summary

The chapter “AI/ML/DL- Key Concepts Explainer” lays down the basic ground for what constitutes AI, ML and DL. While these concepts are covered extensively in contemporary times, it is important to unambiguously define them to put into perspective, their role in the cellular mobile network architecture. However, the crux of the chapter is the explanation of the several use-cases that this report sizes and forecasts the market fore. This chapter enunciates in appropriate detail the applications of AI, ML and DL in the operations of 4G and 5G cores.

The chapter “Virtualization of the RAN” places virtualization in the realms of the RAN network function. In the context of AI, if there is any development that has as OpenRAN are a direct result of the ground laid by SDN and NFV technologies. The chapter traces the evolution of the RAN and its progressive virtualization.

The chapter “AI and RAN Caching” details the import of AI to the RAN. It dives into the role played by O-RAN in providing a pathway for deeper integration with AI. The chapter then details caching, where AI is providing or likely to provide seminal contributions.

The chapter “Vendor Initiatives for AI in the RAN” identifies, covers and analyzes key vendors and their solutions related to AI in the RAN. More importantly, the chapter uncovers the seminal impact that AI is engineering on the RAN vendor landscape.

The chapter “Telco Initiatives for AI in the RAN” details the approaches and initiatives of leading telcos in context of AI in the RAN. It should be remembered that it was the telcos that championed the NFV movement. A fallout of this movement is the gradual induction of AI in the RAN architecture. The RIC through the O-RAN initiative has been a major step in that initiative. This chapter chronicles the telco initiatives and their outcomes.

The chapter “Quantitative Analysis and Forecasts” presents the quantitative forecast of the market for AI in cellular mobile RAN caching. The market is broken down based on several different criteria such as mobile telephony generations and geographical regions.

Companies Mentioned

Aira
AirHop
Aspire
AT&T Inc
Axiata Group Berhad
Bharti Airtel
Capgemini
Cisco
China Mobile
China Telecom
China Unicom
CK Hutchison Holdings
DeepSig
Deutsche Telekom
Ericcson
Etisalat
Fujitsu
Globe Telecom Inc
HCL
Huawei
Juniper
NTT DoCoMo
Mavenir
MTN Group
Net AI
Nokia
Nvidia
Ooredoo
Opanga Networks
Orange
PLDT Inc
Qualcomm
Rakuten Mobile
Reliance Jio
Rimendo
Samsung
Saudi Telecom Company
Singtel
SK Telecom
Softbank
Telefonica
Telenor
Telkomsel
T-Mobile US
Verizon
Viettel Group
Vodafone
ZTE
VMWare

Report Highlights

Table of Contents

Samples

Executive Summary

Companies Mentioned

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