AI and RAN Coding - Technologies and Markets

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 Coding

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