Finally AI/ML is getting into 3GPP -:). This note is not about what is AI/ML. I have a whole list of section about AI/ML itself and 3GPP also posted a short article about AI/ML. This note is more of how and where AI/ML will be used in 3GPP framework.

• 3GPP AI/ML Framework
• Principles of 3GPP AI/ML
• Workflow of 5G AL/ML
• What kind of data to be collected for training ?
• What is it used for in 5G ?
• Network Energy Saving
• Load Balancing
• Mobility Optimization
• Beam Management
• Implementation Examples
• Qualcomm X65
• Qualcomm X70
• Qualcomm X75
• SamSung
• Reference
• 3GPP TR/TS/General
• 3GPP TDocs
• General Reading

3GPP AI/ML Framework

As of now (Dec 2021), it is not clearly decided exactly what kind of AI/ML framework will be used in terms of 5G/NR network. One possible high level view is as shown below (this is based on TR 38.817 V1.0.0 (2021-12) - Figure 4.2-1: Functional Framework for RAN Intelligence).

Who is doing the data preparation (data pre-processing and cleaning, formatting, and transformation) ?

: Data Collection component just collect the raw data and does not process it for other component to use it. The data processing / preparation is done by the component that comsumes the data. For example, if the collected data is for training model,  Data Collection module just transfer the raw data to Model Training component and the Model Training component preprocess the data. If if the collected data is for model inference,  Data Collection module just transfer the raw data to Model Inference component and the Model Inference component prepare the data.

Principles of 3GPP AI/ML

When I was thinking of applying AI/ML in cellular technoloty (especially on RAN side), I had so many questions poping up in my head. What kind of neural network model will be used ? How it will be trained ? Who (gNB or Corenetwork) will train the network ? What kind of data will be used for training the network ? What kind of use cases will there be ? and so on.. and on ... and on. As of now (Dec 2021), I don't have clear answer to these questions... I would need to wait until the 3GPP specification of AI/ML is finalized (Rel 18). But at least I can get a glimpse of general principles of AI/ML implmentation from TR 38.817 - 4.1. It is stated as follows (Just reading these bullets gives me pretty good idea).

• The detailed AI/ML algorithms and models for use cases are implementation specific and out of RAN3 scope.
• The study focuses on AI/ML functionality and corresponding types of inputs/outputs.
• The input/output and the location of the Model Training and Model Inference function should be studied case by case.
• The study focuses on the analysis of data needed at the Model Training function from Data Collection, while the aspects of how the Model Training function uses inputs to train a model are out of RAN3 scope.
• The study focuses on the analysis of data needed at the Model Inference function from Data Collection, while the aspects of how the Model Inference function uses inputs to derive outputs are out of RAN3 scope.
• Where AI/ML functionality resides within the current RAN architecture, depends on deployment and on the specific use cases.
• The Model Training and Model Inference functions should be able to request, if needed, specific information to be used to train or execute the AI/ML algorithm and to avoid reception of unnecessary information. The nature of such information depends on the use case and on the AI/ML algorithm.   
• The Model Inference function should signal the outputs of the model only to nodes that have explicitly requested them (e.g. via subscription), or nodes that are subject to actions based on the output from Model Inference.
• An AI/ML model used in a Model Inference function has to be initially trained, validated and tested before deployment.
• The generalized workflow should not prevent to “think beyond” the workflow if the use case requires so.
• User data privacy and anonymisation should be respected during AI/ML operation.

In short, these can be summarized as

• 3GPP would not define exact neural network definition / implementation. It would define only the interface and type of input/output of the neural network(Model).
• The whole AI/ML functionality would be comprised of several different compoments (e.g, Data Collection, Model Training, Model Interence, Actor). Where these components will be located will vary depending on specific use cases.

Workflow of 5G AL/ML

< 5G AI/ML workflow based on TR 28.908 V1.2.0 - 4.3 AI/ML workflow for 5GS >

What kind of data to be collected for training ?

As most of you already understand, in order to train a AI/ML system. You need to have clear answer to followings :

• What kind of data will be used ?
• How the data would be aquired ?
• In what type of format the data should be prepared ?

Answers to the first two questions are described in TR 28.908 (v1.2.0) - 5.1.1 Event data for ML training and I think the answer to the third question will be described in various TS.  In short, according to TR 28.908, the data is called 'Event data' implying that those data are to indicates a certain type of events and collected by specific event handler.

Followings are some of the example of these events

< TR28.908 (v1.2.0) - Table 5.1.1.4-1: Examples of potential Metric Threshold Crossing events >

Event - source Function : Network Components that collects the data

MOI :  Managed Object Instance. A part of a network that can be managed independently. This could be a physical component, such as a cell in a cellular network, or a logical component, such as a service or a process.

< TR28.908 (v1.2.0) - Table 5.1.1.4-2: Examples of potential Object-Status Change events >

What is it used for in 5G ?

Following use cases are based on TR 38.817 (V17.0.0 (2022-04)). As you would notice, it seems that 3GPP is targetting mostly for Network side optimization for now.

Network Energy Saving

The informations to be collected are as follows :

• From local node:
• UE mobility/trajectory prediction
• Current/Predicted Energy efficiency
• Current/Predicted resource status
• From the UE:
• UE location information (e.g., coordinates, serving cell ID, moving velocity) interpreted by gNB implementation when available
• UE measurement report (e.g., UE RSRP, RSRQ, SINR measurement, etc), including cell level and beam level UE measurements
• From neighbouring NG-RAN nodes:
• Current/Predicted energy efficiency
• Current/Predicted resource status
• Current energy state (e.g., active, high, low, inactive)

Load Balancing

The informations to be collected are as follows :

• From the local node:
• Current and predicted own resource status
• UE trajectory prediction
• Current and predicted UE traffic
• Predicted resource status information of neighbouring NG-RAN node(s)
• From the UE:
• UE location information (e.g., coordinates, serving cell ID, moving velocity) interpreted by gNB implementation when available
• UE Mobility History Information
• UE measurement report (e.g., UE RSRP, RSRQ, SINR measurement, etc), including cell level and beam level UE measurements
• From neighbouring NG-RAN Nodes:
• Current and predicted resource status
• UE performance measurement at traffic offloaded neighbouring cell

Mobility Optimization

The informations to be collected are as follows :

• From the UE:
• UE location information (e.g., coordinates, serving cell ID, moving velocity) interpreted by gNB implementation when available.
• Radio measurements related to serving cell and neighbouring cells associated with UE location information, e.g., RSRP, RSRQ, SINR.
• UE Mobility History Information.
• From the neighbouring RAN nodes:
• UE’s history information from neighbour
• Position, QoS parameters and the performance information of historical HO-ed UE (e.g., loss rate, delay, etc.)
• Current/predicted resource status
• UE handovers in the past that were successful and unsuccessful, including too-early, too-late, or handover to wrong (sub-optimal) cell, based on existing SON/RLF report mechanism.
• From the local node:
• UE trajectory prediction
• Current/predicted resource status
• Current/predicted UE traffic

Beam Management

The Release 18 RAN1 study on AI/ML for NR Air Interface explores the benefits of augmenting the air interface with features enabling improved support of AI/ML based algorithms for enhanced performance and/or reduced complexity or overhead.

Following is based on R1-2204060. Refer to the original document for further details.

• CSI feedback enhancement : Enhancements to CSI, such as frequency domain compression, have been agreed upon, with other enhancements like time-domain prediction still under consideration (e.g., overhead reduction, improved accuracy, prediction)
• Beam management : Use AI and ML techniques to predict the best beam to use for a given user device at a given time and place, accounting for various factors such as user location, movement, and network conditions (i.e, beam prediction in time, and/or spatial domain for overhead and latency reduction, beam selection accuracy improvement)
• Positioning accuracy enhancements for different scenarios including : Compared to traditional positioning algorithms, machine learning-assisted positioning can require less additional feedback overhead, and positioning estimates can be made directly inside the radio access network (RAN) and handle the situation with heavy NLOS conditions .

NOTE : I am writing a few separte notes about AI/ML on PHY and Air Interface.

• AI/ML - PHY : Beam Management
• AI/ML - PHY : CSI
• AI/ML - PHY : Positioning
• AI/ML - Rx Block

Implementation Examples

Even before 3GPP specification process is done, some modem chipset starts coming out equipped with AI functionalities in radio stack.

Qualcomm X65

• AI-Enhanced Signal Boost (Adaptive antenna tuning solution)

Qualcomm X70

• AI-based channel-state feedback and dynamic optimization
• AI-based mmWave beam management for superior mobility and coverage robustness
• AI-based network selection for superior mobility and link robustness
• AI-based adaptive antenna tuning for up to 30% improved context detection for higher average speeds and coverage

Qualcomm X75

• RF system with an integrated AI tensor accelerator(Qualcomm® 5G AI Processor Gen 2)

SamSung

• @ 33:15 of 6G@UT Forum - KEYNOTE - Road to 6G: Radio Technology Evolution

Reference

• 3GPP TR/TS/General
• 3GPP - Finding AI in 3GPP
• 3GPP - RAN3 completes AI & ML support study
• 3GPP - AI/ML for NR Air Interface
• 3GPP TR 22.874 - Study on traffic characteristics and performance requirements for AI/ML model transfer in 5GS (Release 18)
• 3GPP TR 22.875 - Study on AI/MLModel Transfer Phase 2
• 3GPP TR 23.700-80 Study on 5G System Support for AI/ML-based Services (Release 18)
• 3GPP TR 23.700-91 Study on enablers for network automation for the 5G System (5GS); Phase 2 (Release 17)
• 3GPP TR 28.908 Study on Artificial Intelligence / Machine Learning (AI/ML) management (Release 18)
• 3GPP TR 33.852 - Study on traffic characteristics and performance requirements for AI/ML model transfer in 5G Systems (5GS)
• 3GPP TR 33.898 - Study on security and privacy of Artificial Intelligence/Machine Learning (AI/ML)-based services and applications in 5G
• 3GPP TR 37.817 - Study on enhancement for Data Collection for NR and EN-DC
• 3GPP TR 38.843 - Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR air interface
• 3GPP TS 28 105 Artificial Intelligence/ Machine Learning (AI/ML) management
• 3GPP TS 28.550 - Management and orchestration; Performance assurance
• 3GPP RP-221348 Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR Air Interface
• 3GPP R1-2204060 3GPP TSG RAN WG1  #109-e e-Meeting, Beam management with AI/ML
• 3GPP TSG RAN WG1 Meeting #109-e : Discussions on other potential use cases of AI/ML for NR air interface
• 3GPP TDocs
• R1-2401766 : Session notes for 9.1 (Artificial Intelligence (AI)/Machine Learning (ML) for NR Air Interface) - TSG RAN WG1 #116 – February 26th – March 1st, 2024
• R1-2401599 - FL summary #3 for AI/ML in management : 3GPP TSG RAN WG1 #116 - February 26th – March 1st, 2024
• R1-2401598 : FL summary #1 for AI/ML in management - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401597 : FL summary #1 for AI/ML in management - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401596 : FL summary #1 for AI/ML in management - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401573 : Final Summary for other aspects of AI/ML model and data - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401572 : Summary #4 for other aspects of AI/ML model and data - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401571 : Summary #3 for other aspects of AI/ML model and data - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401570 : Summary #2 for other aspects of AI/ML model and data - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401569 : Summary #2 for other aspects of AI/ML model and data - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401563 : Final summary for Additional study on AI/ML for NR air interface: CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401562 : Summary#6 for Additional study on AI/ML for NR air interface: CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401561 : Summary#5 for Additional study on AI/ML for NR air interface: CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401560 : Summary#4 for Additional study on AI/ML for NR air interface: CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401559 : Summary#3 for Additional study on AI/ML for NR air interface: CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401558 : Summary#2 for Additional study on AI/ML for NR air interface: CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401557 : Summary#1 for Additional study on AI/ML for NR air interface: CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401432 : Specification support for AI-ML-based positioning accuracy enhancement - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401431 : Specification support for AI-ML-based beam management - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401430 : Work plan for Rel-19 WI on AI and ML for NR air interface - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401225 : Discussion on other aspects of AI/ML model and data - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401224 : Discussion on AI/ML for CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401179 : Discussion on support for AI/ML beam management - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401155 : Discussion on Additional Study of AI/ML for CSI Compression- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401154 : Discussion on Specification Support of AI/ML for Positioning Accuracy Enhancement- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401153 : Discussion on Specification Support of AI/ML for Beam Management- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401152 : Discussion on study of AIML for CSI compression- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401152 : Discussion on study of AIML for CSI compression- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401151 : Discussion on study of AIML for CSI prediction- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401138 : View on AI/ML model and data- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401137 : Design for AI/ML based positioning- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401135 : Discussions on AI/ML for CSI feedback- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401134 : Discussions on AI/ML for beam management- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401111 : Discussion on other aspects of AI/ML model and data- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401110 : Discussion on the AI/ML for CSI compression- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401109 : Discussion on the AI/ML for CSI prediction- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401107 : Discussion on AI/ML for beam management- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401043 : Discussion on AI/ML based beam management- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401042 : Discussion on support for AIML positioning- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401038 : Discussion on other aspects for AI/ML for air interface- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401037 : Discussion on AI/ML for CSI compression- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401036 : Discussion on AI/ML for CSI prediction- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401006 : Discussion on other aspects of AI/ML model and data - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401005 : Discussion on CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401004 : Discussion on AI based CSI prediction - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2401002 : Discussion on AI/ML beam management - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400916 : Study on CSI compression  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400915 : Study on CSI prediction   - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400914 : Discussions on AI/ML for beam management   - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400910 : Discussion on other aspects of AI/ML model and data  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400909 : Discussion on AI/ML-based CSI compression  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400908 : Discussion on AI/ML-based CSI prediction  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400847 : Discussion on CSI compression  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400846 : Discussions on CSI prediction  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400845 : Discussions on AI/ML for positioning accuracy enhancement  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400844 : Discussions on AI/ML for beam management  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400842 : Discussion on AI/ML for CSI prediction  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400834 : On aspects of AI/ML model and data framework  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400833 : On AI/ML for CSI Compression  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400832 : On AI/ML for CSI prediction  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400831 : AI/ML specification support for beam management  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400797 : Other aspects of AI/ML Model and Data - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400796 : AI/ML for CSI Compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400795 : AI/ML for CSI Prediction - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400794 : AI/ML for Positioning Accuracy Enhancement - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400793 : AI/ML for Beam Management- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400781 : Discussion on specification support for AI/ML-based beam management- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400780 : Discussion on other aspects of AI/ML model and data- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400770 : Discussion on other aspects of AI/ML model and data- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400769 : Discussion on CSI compression with AI/ML- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400768 : Discussion on CSI prediction with AI/ML- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400767 : Discussions on specification support for AI/ML positioning accuracy enhancement - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400766 : Discussion on specification support on AI/ML for beam management - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400758 : On other aspects of AI/ML model and data - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400757 : Discussion on AI/ML-based positioning accuracy enhancement - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400724 : Discussion for further study on other aspects of AI/ML model and data - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400723 : Discussion for further study on AI/ML-based CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400722 : Discussion for further study on AI/ML-based CSI prediction - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400721 : Discussion for supporting AI/ML based positioning accuracy enhancement - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400720 : Discussion for supporting AI/ML based beam management - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400696 : Additional study on other aspects of AI model and data - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400695 : Additional study on AI-enabled CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400694 : Additional study on AI-enabled CSI prediction - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400693 : Specification support for AI-enabled positioning - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400692 : Specification support for AI-enabled beam management - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400653 : Discussion on CSI compression for AI/ML - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400621 : Additional study on AI/ML-based CSI compression  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400620 : Additional study on AI/ML-based CSI prediction  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400619 : On specification for AI/ML-based positioning accuracy enhancements  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400618 : On specification for AI/ML-based beam management  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400546 : Discussion on two-sided AI/ML model based CSI compression  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400545 : Discussion on one side AI/ML model based CSI prediction  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400544 : Discussion on AI/ML-based positioning accuracy enhancement  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400466 : Discussion on other aspects of AI/ML model and data  - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400465 : Discussion on specification support for beam management - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400464: Discussion on CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400463: Discussion on CSI prediction - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400422: Study on other aspects of AI/ML model and data - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400421: Study on AI/ML-based CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400420: Study on AI/ML-based CSI prediction - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400419: Discussion on AI/ML-based positioning accuracy enhancement - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400418: Discussion on AI/ML-based beam management - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400396: ML Model and Data - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400395: ML based CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400394: ML based CSI prediction - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400393: ML based Positioning - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400392: ML based Beam Management - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400380: Other study aspects of AI/ML for air interface - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400378: AI/ML for CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400377: Specification support for AI/ML for positioning accuracy enhancement - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400376: Specification support for AI/ML for beam management - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400320: Discussion on other aspects of AI/ML model and data - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400319: Discussion on AI/ML for CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400318: Discussion on AI/ML for CSI prediction - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400274: Specification support for AI/ML beam management - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400266: Discussion on study for other aspects of AI/ML model and data- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400264: Discussion on study for AI/ML CSI prediction - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400263: Discussion on specification support for AI/ML beam management - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400236: Other aspects of AI/ML model and data - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400235: Discussion on CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400234: Discussion on CSI prediction - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400172: Discussion on other aspects of AI/ML - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400171: AI/ML for beam management - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400169: Discussion on specification support for AI/ML positioning accuracy enhancement - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400166: AI/ML for CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400165: AI/ML for CSI prediction - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400150: Discussion on CSI compression for AI/ML - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400147: Discussion on other aspects of the additional study for AI/ML - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400146: Discussion on CSI prediction for AI/ML - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400145: Discussion on positioning accuracy enhancement for AI/ML - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400144: Discussion on beam management for AI/ML - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400095: Discussion on potential performance enhancements/overhead reduction with AI/ML-based CSI feedback compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400094: Discussion on other aspects of AI/ML model and data on AI/ML for NR air-interface - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400048: Discussion on other aspects of AI/ML model and data - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400047: Discussion on AIML for CSI compression - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400046: Discussion on AIML for CSI prediction - 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• R1-2400045: Discussion on AIML for beam management- 3GPP TSG RAN WG1 #116 February 26th – March 1st, 2024
• General Reading
• Artificial Intelligence in Open Radio Access Network (2021)
• The essential role of AI in the 5G future - Qualcomm (2021)
• New Snapdragon X70 Modem-RF Harnesses World’s First 5G AI Processor for Industry Leading Performance and Experiences - Qualcomm
• Qualcomm Announces Next-Generation 5G RF Front End Solutions, Featuring Use of Artificial Intelligence for Sleeker, High Performance 10 Gigabit 5G Devices - Qualcomm
• Fueling the Next Quantum Leap in Cellular Networks: Embracing AI in 5G Evolution towards 6G - Ericsson
• AI/ML for NR Air Interface - 3GPP (Nov 01, 2022)
• Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level Simulator - MDPI (2023)
• Snapdragon X75 5G Modem-RF System - Qualcomm (2023)

YouTube

• AI for Good
• AI/ML Empowering 5G Network Slicing (Oct 2020)
• ITU AI for Good/ML in 5G - Awards 2021 (RadioML) (Nov 2021)
• AI for 5G Advanced toward 6G - Oregon ComSoc (2023) // Strongly Recommended
• AI for 5G Advanced toward 6G (Jan 2023)
• 3GPP Release 18 Overview: A World of 5G-Advanced (Feb 2023)
• AI/ML Impact from 5.5G to 6G - IEEE Stanadards Association (Aug 2023)
• MWC 2024: Wireless AI interoperability in the multi-vendor AI world  - Qualcomm(Feb 2024)
• MWC 2024: How Qualcomm wireless AI unlocks 5G mmWave efficiency - Qualcomm(Feb 2024)

YouTube (Korean)

• 무선통신과 딥러닝의 만남 빔포밍 최적화 관점에서[전북대학교 박석환 교수] - KICS 한국통신학회 (2023)