Method of advanced retransmission signaling with semantic data

Abstract

The present disclosure describes a novel method of using the pre-configured transmission data types with semantic and non-semantic data in wireless mobile communication system including base station (e.g., gNB, TRP, TN, NTN) and mobile station (e.g., UE). Existing systems often lack the flexibility to handle different types of data (semantic and non-semantic) efficiently. With semantic data transmission applied to radio access network, it is necessary to adapt its (re-)transmission strategies based on the actual needs of the receiving application. Therefore, semantic data operation can be set up between network and UE by providing a unified approach that accommodates both semantic and non-semantic data types.

Description

[0001] 202407114

[0002] - 1 -

[0003] TITLE

[0004] Method of advanced retransmission signaling with semantic data

[0005] TECHNNICAL FIELD

[0006] The present disclosure relates to the field of semantic data communication with retransmission request types, where techniques for configuring transmission of semantic and non-semantic data applicable to radio access network are presented.

[0007] BACKGROUND

[0008] In 3GPP (Third Generation Partnership Project), one of the selected study items as the approved Release 18 package of 5G Advanced is AI / ML (artificial intelligence / machine learning) as described in the related document (RP-213599) addressed in 3GPP TSG (Technical Specification Group) RAN (Radio Access Network) meeting #94e. The official title of AI / ML study item is “Study on AI / ML for NR Air Interface”. The goal of this study item is to identify a common AI / ML framework and areas of obtaining gains using AI / ML based techniques with use cases. According to 3GPP, the main objective of this study item is to study AI / ML framework for air-interface with target use cases by considering performance, complexity, and potential specification impact.

[0009] In particular, AI / ML model, terminology and description to identify common and specific characteristics for framework are included as one of key work scopes. Regarding AI / ML framework, various aspects are under consideration for investigation and one of key items is about lifecycle management of AI / ML model where multiple stages are included as mandatory for model training, model deployment, model inference, model monitoring, model updating etc. Also in 3GPP, two-sided (AI / ML) model is defined as a paired AI / ML model(s) over which joint inference is performed, where joint inference comprises AI / ML Inference whose inference is performed jointly across the UE and the network. Also for one-sided (AI / ML) model, UE-side (AI / ML) model is defined as an AI / ML model whose inference 202407114

[0010] - 2 - is performed entirely at the UE and network-side (AI / ML) model is defined as an AI / ML model whose inference is performed entirely at the network. Currently, AI / ML specification work is at the stage of work item discussion for Release 19. Earlier, in 3GPP TR 37.817 for Release 17, titled as Study on enhancement for Data Collection for NR and EN-DC, UE (user equipment) mobility was also considered as one of AI / ML use cases and one of scenarios for model training / inference is that both functions are located within RAN node. Followingly, in Release 18 the new work item of “Artificial Intelligence (AI)ZMachine Learning (ML) for NG-RAN” was initiated to specify data collection enhancements and signaling support within existing NG-RAN interfaces and architecture. L1 / L2 signaling refers to the control information carried on physical layer and MAC (medium access control) layer, respectively, while RRC (radio resource control) signaling is used for higher-level control of the radio connection. For the above active standardization works, RAN-based AI / ML model is considered very significant for both network and UE to meet any desired model operations (e.g., model training, inference, selection, switching, update, monitoring, etc.). Model information can be signaled to pair both network-side and UE-side models for various lifecycle management (LCM) operations.

[0011] However, signaling overhead indicating model information can be very high especially when model based LCM is processed between base station (BS / gNB) and multiple UEs. In LCM, model training is one of the most important parts for model deployment and currently there is no specification defined for signaling methods and network-UE behaviors so as to identify the required dataset when model updating / re- training as any activated model can be also impacted due to model / data drift. When ML condition changes, the enabled AI / ML model(s) can be impacted for model performance due to data / model drift. In this case, model re-training / updating can be executed. Along with 5G Advanced AI / ML standardization activities as described above, 6G study is scheduled to start in the upcoming release stage as the evolution of wireless communication technologies towards 6G has led to an unprecedented increase in data traffic and diversity of applications across RANs.

[0012] Traditional communication paradigms, which focus on transmitting raw data without considering its semantic content or importance, are becoming increasingly inefficient 202407114

[0013] - 3 - in meeting the demands of emerging applications such as autonomous vehicles, extended reality (XR), and industrial Internet of Things (loT). As 6G networks aim to support these diverse applications with stringent requirements, there is a growing need for more efficient and context-aware communication systems. Semantic communication has emerged as a promising approach to address these challenges by focusing on the meaning and importance of information rather than raw bit transmission. This paradigm shift aims to improve spectral efficiency, reduce latency, and enhance overall system performance. Along with the recent advances in 3GPP, AI / ML technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI / ML entities. These advancements impose new requirements on future 6G mobile network architectures, necessitating the integration of communication, computation, control, and intelligence. The concept of Al-native 6G systems, specifically tailored for semantic and goal-oriented communications, has gained traction. These systems aim to go beyond the established limits of current sense-compute-connect-control models and transition toward semantic communication-based Al architectures, protocols, and services.

[0014] However, current 3GPP specifications lack mechanisms to differentiate between data types based on their semantic importance, leading to inefficient use of network resources. Moreover, existing error correction and retransmission schemes do not consider the semantic nature of the data, resulting in unnecessary retransmissions and increased latency. To address these challenges, novel approaches such as goal- oriented semantic communication frameworks are being explored. These frameworks incorporate both semantic and effectiveness levels for various tasks with diverse data types, aiming to facilitate information exchange between intelligent agents in a more relevant, effective, and timely manner. The development of semantic communication protocols for 6G involves various levels of sophistication, from task-oriented neural protocols to language-oriented semantic protocols harnessing large language models (LLMs) and generative models. This evolution in protocol design aims to address the non-stationary tasks expected in 6G systems and offer the ability to tailor signaling messages for specific tasks. In aspect of these advancements and challenges, there is a significant need for innovative methods and systems that can enable efficient semantic data transmission in 6G wireless networks. These solutions should 202407114

[0015] - 4 - incorporate AI / ML techniques to enhance semantic understanding, optimize resource allocation, and improve overall system performance while meeting the diverse requirements of emerging applications.

[0016] The ITU IMT-2030 framework identifies “Al and Communication” and “Integrated Sensing and Communication (ISAC)” as key usage scenarios for 6G, while 3GPP has initiated studies on AI / ML integration in the NR air interface (e.g., TR 38.843) and semantic communication concepts for 6G. These developments aim to enable Al- native RAN that optimize resource allocation, reliability, and latency by leveraging semantic information rather than raw data alone. The present invention addresses the gap for efficient semantic-aware transmission and retransmission, enabling dynamic adaptation of coding, segmentation, and retransmission strategies. These features are designed to be compatible with current NR architecture while providing a forward-compatible foundation for Al-native 6G RAN.

[0017] US2023216932A1 relates to a method for filtering data traffic based on user information and areas of interest by optimizing how data is handled in communication systems by focusing on user preferences and specific data needs, ensuring that only relevant traffic is processed or transmitted.

[0018] US2023199746A1 describes a method aimed at enhancing communication systems, particularly in data processing and transmission by optimizing the flow of data across networks, ensuring more efficient handling of complex data interactions.

[0019] US2023291497A1 describes a method or system related to optimizing data processing and transmission in communication networks by improving how devices, particularly in wireless communication environments, handle complex data flows.

[0020] US2023412709A1 involves a method focused on optimizing data transmission and communication efficiency by improving how devices manage, transmit, and process data, potentially using techniques like enhanced encoding or optimized data flow to handle complex data interactions. 202407114

[0021] - 5 -

[0022] WO20231 13302A1 focuses on advancements in semantic communication systems designed to enhance transmission efficiency for transmitting semantic-related information instead of raw data, reducing the amount of data sent over communication networks.

[0023] WO2024038926A1 focuses on a method and device for optimizing wireless communication transmission, efficiently transmitting data in communication systems by improving the way signals are structured and sent between devices.

[0024] BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is an exemplary data format structure of supporting semantic data type.

[0026] Figure 2 is an exemplary data payload of supporting semantic and non-semantic data segments.

[0027] Figure 3 is an exemplary retransmission request types for semantic data.

[0028] Figure 4 is an exemplary signaling flow of supporting semantic data retransmission.

[0029] Figure 5 is an exemplary flow chart of supporting semantic data transmission at network side.

[0030] Figure 6 is an exemplary flow chart of supporting semantic data transmission at UE side.

[0031] DETAILED DESCRIPTION

[0032] The detailed description set forth below, with reference to annexed drawings, is intended as a description of various configurations and is not intended to represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of the various concepts. However, it will be apparent to those skilled in 202407114

[0033] - 6 - the art that these concepts may be practiced without these specific details. In particular, although terminology from 3GPP 5G NR may be used in this disclosure to exemplify embodiments herein, this should not be seen as limiting the scope of the invention.

[0034] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Other embodiments, however, are contained within the scope of the subject matter disclosed herein, the disclosed subject matter should not be construed as limited to only the embodiments set forth herein; rather, these embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0035] Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and / or is implied from the context in which it is used. All references to a / an / the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and / or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa. Other objectives, features and advantages of the enclosed embodiments will be apparent from the following description.

[0036] In some embodiments, a more general term “network node” may be used and may correspond to any type of radio network node or any network node, which communicates with a UE (directly or via another node) and / or with another network node. Examples of network nodes are NodeB, MeNB, ENB, a network node belonging to MCG or SCG, base station (BS), multi-standard radio (MSR) radio node such as MSR BS, eNodeB, gNodeB, network controller, radio network controller (RNC), base station controller (BSC), relay, donor node controlling relay, base 202407114

[0037] - 7 - transceiver station (BTS), access point (AP), transmission points, transmission nodes, RRU, RRH, nodes in distributed antenna system (DAS), core network node (e.g. Mobile Switching Center (MSC), Mobility Management Entity (MME), etc.), Operations & Maintenance (O&M), Operations Support System (OSS), Self Optimized Network (SON), positioning node (e.g. Evolved- Serving Mobile Location Centre (E-SMLC)), Minimization of Drive Tests (MDT), test equipment (physical node or software), etc.

[0038] In some embodiments, the non-limiting term user equipment (UE) or wireless device may be used and may refer to any type of wireless device communicating with a network node and / or with another UE in a cellular or mobile communication system. Examples of UE are target device, device to device (D2D) UE, machine type UE or UE capable of machine to machine (M2M) communication, PDA, PAD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, UE category Ml, UE category M2, ProSe UE, V2V UE, V2X UE, etc.

[0039] Additionally, terminologies such as base station / gNodeB and UE should be considered non-limiting and do in particular not imply a certain hierarchical relation between the two; in general, “gNodeB” could be considered as device 1 and “UE” could be considered as device 2 and these two devices communicate with each other over some radio channel. And in the following the transmitter or receiver could be either gNodeB (gNB), or UE.

[0040] As will be appreciated by one skilled in the art, aspects of the embodiments may be embodied as a system, apparatus, method, or program product. Accordingly, embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects.

[0041] For example, the disclosed embodiments may be implemented as a hardware circuit comprising custom very-large-scale integration (“VLSI”) circuits or gate arrays, off- the-shelf semiconductors such as logic chips, transistors, or other discrete 202407114

[0042] - 8 - components. The disclosed embodiments may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, or the like. As another example, the disclosed embodiments may include one or more physical or logical blocks of executable code which may, for instance, be organized as an object, procedure, or function.

[0043] Furthermore, embodiments may take the form of a program product embodied in one or more computer readable storage devices storing machine readable code, computer readable code, and / or program code, referred hereafter as code. The storage devices may be tangible, non- transitory, and / or non-transmission. The storage devices may not embody signals. In a certain embodiment, the storage devices only employ signals for accessing code.

[0044] Any combination of one or more computer readable medium may be utilized. The computer readable medium may be a computer readable storage medium. The computer readable storage medium may be a storage device storing the code. The storage device may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.

[0045] More specific examples (a non-exhaustive list) of the storage device would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random-access memory (“RAM”), a read-only memory (“ROM”), an erasable programmable read-only memory (“EPROM” or Flash memory), a portable compact disc readonly memory (“CD-ROM”), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0046] Code for carrying out operations for embodiments may be any number of lines and may be written in any combination of one or more programming languages including 202407114

[0047] - 9 - an object- oriented programming language such as Python, Ruby, Java, Smalltalk, C++, or the like, and conventional procedural programming languages, such as the “C” programming language, or the like, and / or machine languages such as assembly languages. The code may execute entirely on the user’s computer, partly on the user’s computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user’s computer through any type of network, including a local area network (“LAN”), wireless LAN (“WLAN”), or a wide area network (“WAN”), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider (“ISP”)).

[0048] Furthermore, the described features, structures, or characteristics of the embodiments may be combined in any suitable manner. In the following description, numerous specific details are provided, such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., to provide a thorough understanding of embodiments. One skilled in the relevant art will recognize, however, that embodiments may be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of an embodiment. Reference throughout this specification to “one embodiment,” “an embodiment,” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment, but mean “one or more but not all embodiments” unless expressly specified otherwise. The terms “including,” “comprising,” “having,” and variations thereof mean “including but not limited to,” unless expressly specified otherwise. An enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. The terms “a,” “an,” and “the” also refer to “one or more” unless expressly specified otherwise. 202407114

[0049] - 10 -

[0050] Aspects of the embodiments are described below with reference to schematic flowchart diagrams and / or schematic block diagrams of methods, apparatuses, systems, and program products according to embodiments. It will be understood that each block of the schematic flowchart diagrams and / or schematic block diagrams, and combinations of blocks in the schematic flowchart diagrams and / or schematic block diagrams, can be implemented by code. This code may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart diagrams and / or block diagrams.

[0051] The code may also be stored in a storage device that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the storage device produce an article of manufacture including instructions which implement the function / act specified in the flowchart diagrams and / or block diagrams.

[0052] The code may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other devices to produce a computer implemented process such that the code which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart diagrams and / or block diagrams.

[0053] The flowchart diagrams and / or block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of apparatuses, systems, methods, and program products according to various embodiments. In this regard, each block in the flowchart diagrams and / or block diagrams may represent a module, segment, or portion of code, which includes one or more executable instructions of the code for implementing the specified logical function(s). 202407114

[0054] - 11 -

[0055] It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. Other steps and methods may be conceived that are equivalent in function, logic, or effect to one or more blocks, or portions thereof, of the illustrated figures.

[0056] Although various arrow types and line types may be employed in the flowchart and / or block diagrams, they are understood not to limit the scope of the corresponding embodiments. Indeed, some arrows or other connectors may be used to indicate only the logical flow of the depicted embodiment. For instance, an arrow may indicate a waiting or monitoring period of unspecified duration between enumerated steps of the depicted embodiment. It will also be noted that each block of the block diagrams and / or flowchart diagrams, and combinations of blocks in the block diagrams and / or flowchart diagrams, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and code.

[0057] The description of elements in each figure may refer to elements of proceeding figures. Like numbers refer to like elements in all figures, including alternate embodiments of like elements.

[0058] The detailed description set forth below, with reference to the figures, is intended as a description of various configurations and is not intended to represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of the various concepts. However, it will be apparent to those skilled in the art that these concepts may be practiced without these specific details. For instance, although 3GPP terminology, from e.g., 5G NR, may be used in this disclosure to exemplify embodiments herein, this should not be seen as limiting the scope of the present disclosure. 202407114

[0059] - 12 -

[0060] The disclosure is related to wireless communication system, which may be for example a 5G NR wireless communication system. More specifically, it represents a RAN of the wireless communication system, which is used exchange data with UEs via radio signals. For example, the RAN may send data to the UEs (downlink, DL), for instance data received from a core network (CN). The RAN may also receive data from the UEs (uplink, UL), which data may be forwarded to the CN.

[0061] In the examples illustrated, the RAN comprises one base station, BS. Of course, the RAN may comprise more than one BS to increase the coverage of the wireless communication system. Each of these BSs may be referred to as NB, eNodeB (or eNB), gNodeB (or gNB, in the case of a 5G NR wireless communication system), an access point or the like, depending on the wireless communication standard(s) implemented.

[0062] The UEs are located in a coverage of the BS. The coverage of the BS corresponds for example to the area in which UEs can decode a PDCCH transmitted by the BS.

[0063] An example of a wireless device suitable for implementing any method, discussed in the present disclosure, performed at a UE corresponds to an apparatus that provides wireless connectivity with the RAN of the wireless communication system, and that can be used to exchange data with said RAN. Such a wireless device may be included in a UE. The UE may for instance be a cellular phone, a wireless modem, a wireless communication device, a handheld device, a laptop computer, or the like. The UE may also be an Internet of Things (loT) equipment, like a wireless camera, a smart sensor, a smart meter, smart glasses, a vehicle (manned or unmanned), a global positioning system device, etc., or any other equipment that may run applications that need to exchange data with remote recipients, via the wireless device.

[0064] The wireless device comprises one or more processors and one or more memories. The one or more processors may include for instance a central processing unit (CPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), an 202407114

[0065] - 13 - application specific integrated circuit (ASIC), etc. The one or more memories may include any type of computer readable volatile and non-volatile memories (magnetic hard disk, solid-state disk, optical disk, electronic memory, etc.). The one or more memories may store a computer program product, in the form of a set of programcode instructions to be executed by the one or more processors to implement all or part of the steps of a method for exchanging data, performed at a UE’s side, according to any one of the embodiments disclosed herein.

[0066] The wireless device can comprise also a main radio, MR, unit. The MR unit corresponds to a main wireless communication unit of the wireless device, used for exchanging data with BSs of the RAN using radio signals. The MR unit may implement one or more wireless communication protocols, and may for instance be a 3G, 4G, 5G, NR, WiFi, WiMax, etc. transceiver or the like. In preferred embodiments, the MR unit corresponds to a 5G NR wireless communication unit.

[0067] AI / ML Model is a data driven algorithm that applies AI / ML techniques to generate set of outputs based on set of inputs.

[0068] AI / ML model delivery is a generic term referring to delivery of an AI / ML model from one entity to another entity in any manner. Note is An entity could mean network node / function (e.g., gNB, LMF, etc.), UE, proprietary server, etc.

[0069] AI / ML model Inference is a process of using trained AI / ML model to produce set of outputs based on set of inputs.

[0070] AI / ML model testing is a subprocess of training, to evaluate the performance of final AI / ML model using dataset different from one used for model training and validation. Differently from AI / ML model validation, testing does not assume subsequent tuning of the model.

[0071] AI / ML model training is a process to train an AI / ML Model [by learning the input / output relationship] in data driven manner and obtain the trained AI / ML Model for inference. 202407114

[0072] - 14 -

[0073] AI / ML model transfer is a delivery of an AI / ML model over the air interface in manner that is not transparent to 3GPP signaling, either parameters of model structure known at the receiving end or new model with parameters. Delivery may contain full model or partial model.

[0074] AI / ML model validation is a subprocess of training, to evaluate the quality of an AI / ML model using dataset different from one used for model training, that helps selecting model parameters that generalize beyond the dataset used for model training.

[0075] Data collection is a process of collecting data by the network nodes, management entity, or UE for the purpose of AI / ML model training, data analytics and inference.

[0076] Federated learning I federated training is a machine learning technique that trains an AI / ML model across multiple decentralized edge nodes e.g., UEs, gNBs each performing local model training using local data samples. The technique requires multiple interactions of the model, but no exchange of local data samples.

[0077] Functionality identification is a process / method of identifying an AI / ML functionality for the common understanding between the NW and the UE. Note is Information regarding the AI / ML functionality may be shared during functionality identification. Where AI / ML functionality resides depends on the specific use cases and sub use cases.

[0078] Model activation means enable an AI / ML model for specific AI / ML-enabled feature.

[0079] Model deactivation means disable an AI / ML model for specific AI / ML-enabled feature.

[0080] Model download means Model transfer from the network to UE.

[0081] Model identification is A process / method of identifying an AI / ML model for the common understanding between the NW and the UE. The process / method of model 202407114

[0082] - 15 - identification may or may not be applicable and regarding the AI / ML model may be shared during model identification.

[0083] Model monitoring is A procedure that monitors the inference performance of the AI / ML model.

[0084] Model parameter update is Process of updating the model parameters of model. Model selection is the process of selecting an AI / ML model for activation among multiple models for the same AI / ML enabled feature. Model selection may or may not be carried out simultaneously with model activation.

[0085] Model switching is deactivating currently active AI / ML model and activating different AI / ML model for specific AI / ML-enabled feature.

[0086] Model update is Process of updating the model parameters and / or model structure of model.

[0087] Model upload is Model transfer from UE to the network.

[0088] Network-side (AI / ML) model is an AI / ML Model whose inference is performed entirely at the network.

[0089] Offline field data is the data collected from field and used for offline training of the AI / ML model.

[0090] Offline training is an AI / ML training process where the model is trained based on collected dataset, and where the trained model is later used or delivered for inference. Note is This definition only serves as guidance. There may be cases that may not exactly conform to this definition but could still be categorized as offline training by commonly accepted conventions.

[0091] Online field data is the data collected from field and used for online training of the AI / ML model. 202407114

[0092] - 16 -

[0093] Online training is an AI / ML training process where the model being used for inference) is (typically continuously) trained in (near) real-time with the arrival of new training samples. Note is the notion of (near) real-time vs. non real-time is context- dependent and is relative to the inference time-scale. This definition only serves as guidance.

[0094] There may be cases that may not exactly conform to this definition but could still be categorized as online training by commonly accepted conventions. Note is Fine- tuning / re-training may be done via online or offline training. This note could be removed when we define the term fine-tuning.

[0095] Reinforcement Learning (RL) is a process of training an AI / ML model from input (a.k.a. state) and feedback signal (a.k.a. reward) resulting from the model’s output (a.k.a. action) in an environment the model is interacting with.

[0096] Semi-supervised learning is a process of training model with mix of labelled data and unlabeled data.

[0097] Supervised learning is a process of training model from input and its corresponding labels.

[0098] Two-sided (AI / ML) model is a paired AI / ML Model(s) over which joint inference is performed, where joint inference comprises AI / ML Inference whose inference is performed jointly across the UE and the network, i.e, the first part of inference is firstly performed by UE and then the remaining part is performed by gNB, or vice versa.

[0099] UE-side (AI / ML) model is an AI / ML Model whose inference is performed entirely at the UE.

[0100] Unsupervised learning is a process of training model without labelled data. 202407114

[0101] - 17 -

[0102] Proprietary-format models is ML models of vendor-Zdevice-specific proprietary format, from 3GPP perspective. They are not mutually recognizable across vendors and hide model design information from other vendors when shared.

[0103] Open-format models is ML models of specified format that are mutually recognizable across vendors and allow interoperability, from 3GPP perspective. They are mutually recognizable between vendors and do not hide model design information from other vendors when shared.

[0104] The present invention relates to a novel method and system for semantic-aware data transmission in 6G networks, specifically focusing on transmission data types and retransmission request types. AI / ML based techniques are currently applied to many different applications and 3GPP also started to work on its technical investigation to apply to multiple use cases based on the observed potential gains. AI / ML lifecycle can be split into several stages such as data collection / pre-processing, model training, model testing / validation, model deployment / update, model monitoring etc., where each stage is equally important to achieve target performance with any specific model(s). In applying AI / ML model for any use case or application, one of the challenging issues is to manage the lifecycle of AI / ML model. It is mainly because the data / model drift occurs during model deployment / inference and it results in performance degradation of AI / ML model.

[0105] Fundamentally, the dataset statistical changes occur after model is deployed and model inference capability is also impacted with unseen data as input. In a similar aspect, the statistical property of dataset and the relationship between input and output for the trained model can be changed with drift occurrence. In this context, model training or re-training is one of key issues for model performance maintenance as model performance such as inferencing and / or training is dependent on different model execution environment with varying configuration parameters. To handle this issue, collaboration between UE and gNB is highly important to track model performance and re-configure model corresponding to different environments. AI / ML model needs model monitoring after deployment because model performance cannot be maintained continuously due to drift and update feedback is then provided to re- 202407114

[0106] - 18 - train / update the model or select alternative model. When AI / ML model enabled wireless communication network is deployed, it is then important to consider how to handle AI / ML model in activation with re-configuration for wireless devices under operations such as model training, inference, updating, etc. For determining information about model identification (e.g., model ID), frequent model ID assignment / re-assignment processes might occur due to model drift related to model performance variation and / or model applicable condition change. In addition, if all identified models at UE side are to be assessed and monitored, the associated signaling overhead and computing power demand can increase significantly. And UEs often have a list of active / inactive ML models, each assigned a unique identifier. And the network and UEs may have to take frequent signaling exchanges to determine the related configuration. This can lead to increased signaling overhead and reduced radio resource efficiency.

[0107] For example, more than one ML models are in active between gNB and UE where multiple UEs have their own on-device models in operation. In addition, each models might be in different LCM phases such as data collection, training, inferencing or monitoring, resulting in high signaling overhead for model-related resource transfers (e.g., datasets, model parameters, architectures, hyperparameters) via L1 / L2 or RRC signaling where L1 / L2 signaling refers to the control information carried on physical layer and MAC (medium access control) layer, respectively, while RRC (radio resource control) signaling is used for higher-level control of the radio connection.

[0108] This application aims to enhance the efficiency, reliability, and adaptability of data transmission in next-generation wireless communication systems by providing a comprehensive framework for semantic-aware data transmission, addressing the challenges of efficient resource utilization, reduced latency, and improved accuracy in semantic data communication. In alignment with emerging 3GPP studies for 6G usage scenarios, the present invention introduces semantic-awareness as a native capability within the RAN. The proposed configuration of semantic data type, semantic importance level, and semantic data indicator provides machine- interpretable metadata at RRC, MAC, and PDCP layers, enabling Al-driven optimization of scheduling, retransmission, and error protection. These semantic 202407114

[0109] - 19 - parameters allow the network to dynamically prioritize critical semantic segments, apply adaptive coding profiles, and minimize retransmission overhead for applications such as XR, digital twins, and multimodal uplink bursts. Furthermore, the invention supports integration with AI / ML functionalities defined in 3GPP TR 38.843 and the associated AI / ML studies, by exposing new features for inference and enabling two-sided AI / ML model coordination between UE and network, providing a forward-compatible framework for Al-native 6G RAN, thereby improving resource efficiency, reliability, and semantic fidelity in next-generation wireless systems. In this method, transmission data type information is configured to indicate semantic and non-semantic data types using RRC message and / or MAC CE (control element). Specifically, a new RRC message for RRC-level configuration is set or an existing one (e.g., RRCReconfiguration) is expanded to include a new information element (e.g., semantic data configuration) so that this configuration allows the network to enable / disable semantic data processing for the UE and to specify which logical channels carry semantic and / or non-semantic data. For MAC CE / MAC subheader configuration for dynamic updates, a new MAC CE for semantic data type (SDT) is set to allow dynamic updates of data types without the overhead of RRC reconfiguration (so that this MAC CE can be used to update the semantic data type for one or more logical channels quickly without requiring a full RRC reconfiguration). Specifically, MAC CE format includes a field of SDT with 2 bits (e.g., 00: non- semantic, 01 : partially semantic, 10: fully semantic, 11 : reserved). MAC subheader extension is set to include a 1 -bit semantic data indicator (SDI) flag (e.g., 0: non- semantic, 1 : semantic) so that per-packet indication of semantic data is allowed. For example, the RRC configuration as the baseline for processing the configured transmission data types is used and any updates from the SDT in MAC CE are checked to apply.

[0110] For each received MAC PDU, it checks the SDI flag to determine the final processing method of the configured transmission data type by overriding the baseline configuration. For example, any number of code blocks from the transport block is configured to include semantic data based code block(s) depending on SDT indication. Specifically, the configured portion of semantic data based code blocks is set in association with SDT indication value. The CRC (cyclic redundancy check) 202407114

[0111] - 20 - attachment process is modified to include SDT information such that a modified CRC polynomial including the 2-bit SDT information for multiple code blocks (and the 2-bit SDT to the existing CRC is appended for single code block). The rate matching process is modified to prioritize semantic data such that stronger protection to the semantic portion is applied for partially semantic data (SDT = 01 ) and uniform strong protection is applied for fully semantic data (SDT = 10). The SDT information from the data format structure header or CRC is extracted so that the SDT is used to determine the effective code block size for de-segmentation and / or semantic-aware error correction techniques based on the SDT is applied. In data payload, it is allowed for segments based on the SDT in the header. In other words, the fully semantic data (e.g., SDT = 10) is one single semantic segment, and the partially semantic data (e.g., SDT = 01 ) consists of semantic segment and non-semantic segment along with segment boundary indication information. For non-semantic data (e.g., SDT = 00), it follows the traditional standard segmentation with non-semantic data. The segment boundary indication information can be included in the header (if it is not contained in data payload) as well if applicable. In addition, the semantic importance level (SIL) is configured to prioritize semantic data based on its importance among multiple data segments (e.g., selection from the group consisting of low importance, medium importance, high importance, and critical importance). Different error correction techniques are allowed to be applied to data segments based on the SDT and SIL. Feedback message from UE contains a new information about retransmission request type (RRT). RRT is configured at network side about the supported types that can be selected by UE. Specifically, Type-1 is used when the UE needs the exact same semantic data retransmission. Type-2 is used when the UE needs the semantic data as more important (or less important) than originally classified. Type-3 is used when the UE needs the data to be processed differently in terms of SDT. For example, semantic data segment is sent and it is retransmitted as non-semantic data or partial semantic data segment. Type-4 is used when the UE successfully receives the current data segment(s) but requests related semantic data segment. Type-5 is used when the UE only needs the most critical parts of the data segment to be resent. Type-6 is used when the UE needs additional context to properly interpret the received data with new data segment or UE needs new data when the current transmission is no longer relevant. Based on RRC configuration, 202407114

[0112] - 21 - semantic data transmission can be configured at network side with SDT / SDI / SIL / RRT parameters via system information or dedicated RRC signaling. After UE is configured to receive semantic data segments, processing of the received semantic data is activated. SDT and SIL information is allowed to be updated dynamically via L1 / L2 signaling if applicable. Or it can be also updated semi-statically via RRC reconfiguration message. Based on feedback message sent by UE, the received RRT indication information is referenced to determine the appropriate retransmission method at network side. Based on any specific RRT, semantic data is retransmitted to UE so that UE can process the retransmitted semantic data segment(s) together with or without any previously transmitted semantic data segment(s).

[0113] Figure 1 shows an exemplary data format structure of supporting semantic data type. In this example, a new field in the data format structure header is configured to indicate the semantic data type (SDT) with 2 bits (e.g., 00: non-semantic, 01 : partially semantic, 10: fully semantic, 11 : reserved). This field allows the receiver to process the data based on the indicated type. Header might contain other traditional information such as the total block length including the header and payload and sequence number to indicate the order of transmission. In addition, 1 -bit semantic data indicator (SDI) flag (e.g., 0: non-semantic, 1 : semantic) is allowed to be included in the header if applicable. For example, any number of code blocks from the transport block is configured to include semantic data based code block(s) depending on SDT and / or SDI indication. Specifically, the configured portion of semantic data based code blocks is set in association with SDT indication value. The CRC attachment process is modified to include SDT information such that a modified CRC polynomial including the 2-bit SDT information for multiple code blocks (and the 2-bit SDT to the existing CRC is appended for single code block). The rate matching process is modified to prioritize semantic data such that stronger protection to the semantic portion is applied for partially semantic data (SDT = 01 ) and uniform strong protection is applied for fully semantic data (SDT = 10). The SDT information from the data format structure header or CRC is extracted so that the SDT is used to determine the effective code block size for de-segmentation and / or semantic-aware error correction techniques based on the SDT is applied. 202407114

[0114] - 22 -

[0115] Figure 2 shows an exemplary data payload of supporting semantic and non-semantic data segments. In this example, with data payload it is allowed for segments based on the SDT in the header. In other words, the fully semantic data (e.g., SDT = 10) is one single semantic segment, and the partially semantic data (e.g., SDT = 01 ) consists of semantic segment and non-semantic segment along with segment boundary indication information. For non-semantic data (e.g., SDT = 00), it follows the traditional standard segmentation with non-semantic data. The segment boundary indication information can be included in the header (if it is not contained in data payload) as well if applicable. In addition, the semantic importance level (SIL) is configured to prioritize semantic data based on its importance among multiple data segments. Different error correction techniques are allowed to be applied to data segments based on the SDT and SIL.

[0116] Figure 3 shows an exemplary retransmission request types for semantic data. In this example, feedback message from UE contains a new information about retransmission request type (RRT). RRT is configured at network side about the supported types that can be selected by UE. Specifically, Type-1 is used when the UE needs the exact same semantic data retransmission. Type-2 is used when the UE needs the semantic data as more important (or less important) than originally classified. Type-3 is used when the UE needs the data to be processed differently in terms of SDT. For example, semantic data segment is sent and it is retransmitted as non-semantic data or partial semantic data segment. Type-4 is used when the UE successfully receives the current data segment(s) but requests related semantic data segment. Type-5 is used when the UE only needs the most critical parts of the data segment to be resent. Type-6 is used when the UE needs additional context to properly interpret the received data with new data segment or UE needs new data when the current transmission is no longer relevant.

[0117] Figure 4 shows an exemplary signaling flow of supporting semantic data retransmission. In this example, semantic data transmission can be configured at network side with SDT / SDI / SIL / RRT parameters via system information or dedicated RRC signaling based on RRC configuration. After UE is configured to receive semantic data segments, processing of the received semantic data is activated. SDT 202407114

[0118] - 23 - and SIL information is allowed to be updated dynamically via L1 / L2 signaling. Or it can be also updated semi-statically via RRC re-configuration message. Based on feedback message sent by UE, the received RRT indication information is referenced to determine the appropriate retransmission method at network side. Based on any specific RRT, semantic data is retransmitted to UE so that UE can process the retransmitted semantic data segment(s) together with or without any previously transmitted semantic data segment(s).

[0119] Figure 5 shows an exemplary flow chart of supporting semantic data transmission at network side. In this example, semantic data transmission can be configured at network side with SDT / SDI / SIL / RRT parameters via system information or dedicated RRC signaling based on RRC configuration so that the configured information is sent to UE so as to receive / process semantic data. Based on feedback message sent by UE, the received RRT indication information is referenced to determine the appropriate retransmission method at network side. Based on any specific RRT, semantic data is retransmitted to UE so that UE can process the retransmitted semantic data segment(s) together with or without any previously transmitted semantic data segment(s).

[0120] Figure 6 shows an exemplary flow chart of supporting semantic data transmission at UE side. In this example, semantic data processing of the received semantic data is activated based on the received RRC configuration about semantic data transmission. SDT and SIL information is allowed to be updated dynamically via L1 / L2 signaling if applicable. Or it can be also updated semi-statically via RRC reconfiguration message. The appropriate RRT is selected at UE side (if applicable) so that the received RRT indication information is referenced to determine the appropriate retransmission method at network side via feedback message. By implementing the proposed method above, it is expected to achieve more efficient use of network resources via segmentation process and flexible data transmission via adapting to different types of data (non-semantic, partially semantic, and fully semantic) as well as better error protection via the semantic importance of the data, potentially improving overall transmission reliability. In addition, backward 202407114

[0121] - 24 - compatibility with existing systems with introducing semantic awareness is guaranteed.

Claims

202407114- 25 -CLAIMS1. A method of advanced retransmission signaling with semantic data by configuring transmission data type information in a wireless communication system, comprising:• Indicating semantic and non-semantic data types;• Determining a semantic data type (SDT), a semantic data indicator (SDI), a semantic importance level (SIL), and a retransmission request type (RRT);• Forming a transport block structure with semantic and non-semantic data types;• Retransmitting the data segment(s) based on feedback message.

2. The method according to previous claim 1 , wherein a new RRC message for RRC-level configuration or via RRC Reconfiguration is set to include a new information element, whereby a new information element represents semantic data configuration so that this configuration allows the network to enable / disable semantic data processing for the UE and to specify which logical channels carry semantic and / or non-semantic data.

3. The method according to one of the previous claims, wherein SDT is selected from the group consisting of non-semantic data, partially semantic data, and fully semantic data.

4. The method according to one of the previous claims, wherein MAC CE format includes a field of SDT with 2 bits (e.g., 00: non-semantic, 01 : partially semantic, 10: fully semantic, 11 : reserved) by allowing dynamic updates of data types.

5. The method according to one of the previous claims, wherein MAC subheader extension is set to include a 1 -bit SDI flag (e.g., 0: non-semantic, 1 : semantic) so that per-packet indication of semantic data is allowed.202407114- 26 -6. The method according to one of the previous claims, wherein the RRC configuration as the baseline for processing the configured transmission data types is used so that any updates from the SDT in MAC CE are checked to apply.

7. The method according to one of the previous claims, wherein each received MAC PDU checks the SDI flag to determine the final processing method of the configured transmission data type by overriding the baseline configuration.

8. The method according to one of the previous claims, wherein any number of code blocks from the transport block is configured to include semantic data based code block(s) depending on SDT indication.

9. The method according to one of the previous claims, wherein the configured portion of semantic data based code blocks is set in association with SDT indication value.

10. The method according to one of the previous claims, wherein the CRC attachment process is modified to include SDT information such that a modified CRC polynomial including the 2-bit SDT information for multiple code blocks (and the 2-bit SDT to the existing CRC is appended for single code block).11 .The method according to one of the previous claims, wherein the rate matching process is modified to prioritize semantic data such that stronger protection to the semantic portion is applied for partially semantic data (SDT = 01 ) and uniform strong protection is applied for fully semantic data (SDT = 10).

12. The method according to one of the previous claims, wherein the SDT information from the data format structure header or CRC is extracted so that the SDT is used to determine the effective code block size for de-segmentation and / or semantic- aware error correction techniques based on the SDT is applied.

13. The method according to one of the previous claims, wherein data payload is allowed for segments based on the SDT in the header such that the fully semantic202407114- 27 - data (e.g., SDT = 10) is one single semantic segment, and the partially semantic data (e.g., SDT = 01 ) consists of semantic segment and non-semantic segment along with segment boundary indication information.

14. The method according to one of the previous claims, wherein the semantic importance level (SIL) is configured to prioritize semantic data based on its importance among multiple data segments (e.g., selection from the group consisting of low importance, medium importance, high importance, and critical importance).

15. The method according to one of the previous claims, wherein feedback message from UE contains a new information about retransmission request type (RRT) such that RRT is configured at network side about the supported types that can be selected by UE.

16. The method according to one of the previous claims, wherein Type-1 in RRT is used when the UE needs the exact same semantic data retransmission.

17. The method according to one of the previous claims, wherein Type-2 in RRT is used when the UE needs the semantic data as more important (or less important) than originally classified.

18. The method according to one of the previous claims, wherein Type-3 in RRT is used when the UE needs the data to be processed differently in terms of SDT.

19. The method according to one of the previous claims, wherein Type-4 is used when the UE successfully receives the current data segment(s) but requests related semantic data segment.

20. The method according to one of the previous claims, wherein Type-5 is used when the UE only needs the most critical parts of the data segment to be resent.202407114- 28 -21 . The method according to one of the previous claims, wherein Type-6 is used when the UE needs additional context to properly interpret the received data with new data segment or UE needs new data when the current transmission is no longer relevant.

22. The method according to one of the previous claims, wherein semantic data transmission is configured at network side with SDT / SDI / SIL / RRT parameters via system information or dedicated RRC signaling.

23. The method according to one of the previous claims, wherein SDT and SIL information is allowed to be updated dynamically via L1 / L2 signaling or semi- statically via RRC re-configuration message if applicable.

24. The method according to one of the previous claims, wherein the received RRT indication information is referenced to determine the appropriate retransmission method at network side.

25. The method according to one of the previous claims, wherein semantic data is retransmitted to UE (based on any specific RRT) such that UE processes the retransmitted semantic data segment(s) together with or without any previously transmitted semantic data segment(s).

26. Apparatus for advanced retransmission signaling with semantic data by configuring transmission data type information in a wireless communication system, comprising, the apparatus comprising a wireless transceiver, a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of the claims 1 to 18.

27. User Equipment comprising an apparatus according to claim 26.

28. gNB comprising an apparatus according to claim 26.

29. Wireless communication system method of advanced retransmission signaling with semantic data by configuring transmission data type information, wherein the202407114- 29 - wireless communication systems comprises user equipment according to claim 27, gNB according to claim 28, whereby the user equipment and the gNB each comprises a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of the claims 1 to 25.

Images