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Machine Learning (ML) and Artificial Intelligence (AI) concepts are gradually being integrated in modern 3GPP standardized cellular networks. The process is initiated by introducing AI/ML functions in the 5G core network (5G CN) and from there, it is expanding towards AI-enabled 5G radio access network (RAN). AI/ML is currently at the early stage of design within 3GPP standardisation and many pathways for its impact are still open. In this talk, we present an overview of AI/ML integration in 5G and Beyond. Following the 3GPP perspective, we start from 5G CN and move towards 5G RAN, focusing on AI/ML impact on 5G and B5G physical layer design. We also cover complementary work done by Open RAN (O-RAN) Alliance. The tutorial is extended with demonstration of several use cases and applications of AI/ML integration in 5G/B5G.
- Integration of AI/ML in 5G Core Network
- Integration of AI/ML in 5G Radio Access Network
- AI/ML for the 5G Physical Layer
- AI/ML in 5G Services and Applications
- AI/ML for 5G Network Management and Orchestration
- Examples of AI/ML in 5G/B5G Use Cases Throughout the Tutorial
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Private 5G: The future of industrial wireless (IEEE Industrial Electronics Magazine)
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AI-Driven Provisioning in the 5G Core (IEEE Internet Computing)
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Scaling Network Slices with a 5G Testbed: A Resource Consumption Study (WCNC)
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Design and Implementation of Network Data Analytics Function in 5G (ICTC)
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A distributed collaborative learning approach in 5G+ core networks (IEEE Network)
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Mobility prediction for 5G core networks (IEEE Communications Standards Magazine)
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End-to-end data analytics framework for 5G architecture (IEEE Access)
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TS 29.520, “5G System; Network Data Analytics Services; Stage 3”
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TR23.700-80 Study on 5G System Support for AI/ML-based Services
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TR23.700-81 Study of Enablers for Network Automation for the 5G System (5GS)
- Democratizing the Network Edge (ACM SIGCOMM Computer Communication Review)
- An Overview and Solution for Democratizing AI Workflows at the Network Edge (Journal of Network and Computer Applications)
- A Representation Learning Approach to Feature Drift Detection in Wireless Networks
- The carbon impact of artificial intelligence (Nature Machine Intelligence)
- Energy consumption in data centres and broadband communication networks in the EU (Publications Office of the European Union)
- The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks
- MRM3: Machine Readable ML Model Metadata
- Understanding O-RAN: Architecture, interfaces, algorithms, security, and research challenges (IEEE Communications Surveys & Tutorials)
- Intelligent O-RAN beyond 5g: Architecture, use cases, challenges, and opportunities (IEEE Access)
- Enabling Real-Time AI-Based Open RAN Control
- Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis (IEEE Globecom Workshops)
- On the Implementation of a Reinforcement Learning-based Capacity Sharing Algorithm in O-RAN (IEEE Globecom Workshops)
- Prototyping next-generation O-RAN research testbeds with SDRs
- ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms (IEEE Trans. Mobile Computing)
- AI-RAN Alliance Vision and Mission White Paper
- TS 38.401, “NG-RAN Architecture Description”
- TR 37.817, “Study on enhancement for data collection for NR and EN-DC”
- TR 38.843, “Study on artificial intelligence (AI)/machine learning (ML) for NR air interface”
- A survey of wireless path loss prediction and coverage mapping methods (IEEE Communications Surveys & Tutorials)
- Scientific discovery in the age of artificial intelligence (Nature)
- Towards Automated and Interpretable Pathloss Approximation Methods (AI4WCN)
- Automatic detection of wireless transmissions (IEEE Access)
- Learning approximate neural estimators for wireless channel state information (MLSP)
- Detection of Impaired OFDM Waveforms Using Deep Learning Receiver (IEEE SPAWC 2022)
- Deep learning-based packet detection and carrier frequency offset estimation in IEEE 802.11 ah (IEEE Access)
- Power of deep learning for channel estimation and signal detection in OFDM systems (IEEE Wireless Communications Letters)
- Deep learning-based channel estimation (IEEE Communications Letters)
- Deep learning for channel estimation: Interpretation, performance, and comparison (IEEE Transactions on Wireless Communications)
- Deep Neural Network Augmented Wireless Channel Estimation for Preamble-Based OFDM PHY on Zynq System on Chip (IEEE Transactions on VLSI)
- An introduction to deep learning for the physical layer (IEEE Transactions on Cognitive Communications and Networking)
- Deep learning based communication over the air (IEEE Journal of Selected Topics in Signal Processing)
- Autoencoder-Based Unequal Error Protection Codes (IEEE Communications Letters)
- Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability (IEEE ICC 2023)
- Deep learning methods for improved decoding of linear codes (IEEE Journal of Selected Topics in Signal Processing)
- Model-based deep learning
- Graph neural networks for channel decoding (GC Wkshps)
- Decoding Quantum LDPC Codes Using Graph Neural Networks
- Learning Linear Block Error Correction Codes
- Toward a 6G AI-native air interface (IEEE Communications Magazine)
- Machine learning for beam alignment in millimeter wave massive MIMO (IEEE Wireless Communications Letters)
- Deep active learning approach to adaptive beamforming for mmWave initial alignment (IEEE Journal on Selected Areas in Communications)
- Learning site-specific probing beams for fast mmWave beam alignment (IEEE Transactions on Wireless Communications)
- A Review of the State of the Art and Future Challenges of Deep Learning-Based Beamforming (IEEE Access)
- Machine Learning for Millimeter Wave and Terahertz Beam Management: A Survey and Open Challenges (IEEE Access)
- COMSPLIT: A Communication-Aware SPLIT Learning for Heterogeneous IoT Platforms (IEEE Internet of Things Journal)
- Artificial Intelligence in 3GPP 5G-Advanced: A Survey
- TR22.874, “Study on traffic characteristics and performance requirements for AI/ML model transfer”
- TS22.261, “Service requirements for the 5G system”
- Demonstrating Smart Scaling of AI-Services for Future Networks
- Enabling mobile AI agent in 6G era: Architecture and key technologies (IEEE Network)
- Intent-based management of next-generation networks: An LLM-centric approach (IEEE Network)
- Mobile-llama: Instruction fine-tuning open-source llm for network analysis in 5g networks (IEEE Network)
- NAOMI: Network AI Workflow Democratization
- O-RAN Gerrit
- MRM3: Machine Readable ML Model Metadata [Dataset]
- eCAL
- AutoPL