AI and RAN Caching - Technologies and Markets

Description

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.

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