Methods and systems for configuring one or more decoder optimization parameters for a decoder

Abstract

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. Disclosed is a method (800) for configuring one or more decoder optimization parameters for a decoder in a wireless communication system. The method includes obtaining (802), at a network element, one or more input parameters and generating (804) the one or more decoder optimization parameters. Furthermore, the method includes predicting (806) a decoding success probability for each of the one or more decoder optimization parameters. Moreover, the method includes selecting (808), based on the predicted decoding success probability, at least one parameter having a decoding success probability greater than a predefined threshold. Further, the method includes transmitting (810), selectively from the network element (202) to another network element (204), an indication of the selected at least one parameter and configuring (812), at the another (204) network element, the decoder based on the selected at least one parameter for decoding a received codeword.

Description

METHODS AND SYSTEMS FOR CONFIGURING ONE OR MORE DECODER OPTIMIZATION PARAMETERS FOR A DECODER

[0001] The present disclosure relates to wireless communication systems, and more particularly relates to methods and systems for configuring one or more decoder optimization parameters for a decoder.

[0002] 5G mobile communication technologies define broad frequency bands such that high transmission rates and new services are possible, and can be implemented not only in "Sub 6GHz" bands such as 3.5GHz, but also in "Above 6GHz" bands referred to as mmWave including 28GHz and 39GHz. In addition, it has been considered to implement 6G mobile communication technologies (referred to as Beyond 5G systems) in terahertz bands (for example, 95GHz to 3THz bands) in order to accomplish transmission rates fifty times faster than 5G mobile communication technologies and ultra-low latencies one-tenth of 5G mobile communication technologies.

[0003] At the beginning of the development of 5G mobile communication technologies, in order to support services and to satisfy performance requirements in connection with enhanced Mobile BroadBand (eMBB), Ultra Reliable Low Latency Communications (URLLC), and massive Machine-Type Communications (mMTC), there has been ongoing standardization regarding beamforming and massive MIMO for mitigating radio-wave path loss and increasing radio-wave transmission distances in mmWave, supporting numerologies (for example, operating multiple subcarrier spacings) for efficiently utilizing mmWave resources and dynamic operation of slot formats, initial access technologies for supporting multi-beam transmission and broadbands, definition and operation of BWP (BandWidth Part), new channel coding methods such as a LDPC (Low Density Parity Check) code for large amount of data transmission and a polar code for highly reliable transmission of control information, L2 pre-processing, and network slicing for providing a dedicated network specialized to a specific service.

[0004] Currently, there are ongoing discussions regarding improvement and performance enhancement of initial 5G mobile communication technologies in view of services to be supported by 5G mobile communication technologies, and there has been physical layer standardization regarding technologies such as V2X (Vehicle-to-everything) for aiding driving determination by autonomous vehicles based on information regarding positions and states of vehicles transmitted by the vehicles and for enhancing user convenience, NR-U (New Radio Unlicensed) aimed at system operations conforming to various regulation-related requirements in unlicensed bands, NR UE Power Saving, Non-Terrestrial Network (NTN) which is UE-satellite direct communication for providing coverage in an area in which communication with terrestrial networks is unavailable, and positioning.

[0005] Moreover, there has been ongoing standardization in air interface architecture / protocol regarding technologies such as Industrial Internet of Things (IIoT) for supporting new services through interworking and convergence with other industries, IAB (Integrated Access and Backhaul) for providing a node for network service area expansion by supporting a wireless backhaul link and an access link in an integrated manner, mobility enhancement including conditional handover and DAPS (Dual Active Protocol Stack) handover, and two-step random access for simplifying random access procedures (2-step RACH for NR). There also has been ongoing standardization in system architecture / service regarding a 5G baseline architecture (for example, service based architecture or service based interface) for combining Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) technologies, and Mobile Edge Computing (MEC) for receiving services based on UE positions.

[0006] As 5G mobile communication systems are commercialized, connected devices that have been exponentially increasing will be connected to communication networks, and it is accordingly expected that enhanced functions and performances of 5G mobile communication systems and integrated operations of connected devices will be necessary. To this end, new research is scheduled in connection with eXtended Reality (XR) for efficiently supporting AR (Augmented Reality), VR (Virtual Reality), MR (Mixed Reality) and the like, 5G performance improvement and complexity reduction by utilizing Artificial Intelligence (AI) and Machine Learning (ML), AI service support, metaverse service support, and drone communication.

[0007] Furthermore, such development of 5G mobile communication systems will serve as a basis for developing not only new waveforms for providing coverage in terahertz bands of 6G mobile communication technologies, multi-antenna transmission technologies such as Full Dimensional MIMO (FD-MIMO), array antennas and large-scale antennas, metamaterial-based lenses and antennas for improving coverage of terahertz band signals, high-dimensional space multiplexing technology using OAM (Orbital Angular Momentum), and RIS (Reconfigurable Intelligent Surface), but also full-duplex technology for increasing frequency efficiency of 6G mobile communication technologies and improving system networks, AI-based communication technology for implementing system optimization by utilizing satellites and AI (Artificial Intelligence) from the design stage and internalizing end-to-end AI support functions, and next-generation distributed computing technology for implementing services at levels of complexity exceeding the limit of UE operation capability by utilizing ultra-high-performance communication and computing resources.

[0008] Sustainability has been identified as one of the International Mobile Telecommunications (IMT)-2030 requirements by the International Telecommunication Union Radiocommunication Sector (ITU-R) for sixth generation (6G) wireless communication systems. Both standard-based and implementation-based solutions are being extensively investigated in the cellular industry for reducing energy consumption at both a network side, such as a base station (BS), and a user side, such as a user equipment (UE). In particular, implementation-based methods that achieve power efficiency with minimal signaling or information exchange are of significant interest for 6G and beyond.

[0009] Channel encoding is a technique used for controlling errors in data transmission over unreliable or noisy communication channels by introducing redundancy into information bits. Polar codes, first proposed in 2010, are capacity-achieving error-correcting codes based on the principle of channel polarization. Polar codes have been adopted by the Third Generation Partnership Project (3GPP) and are used in Fifth Generation New Radio (5G NR) systems for control and broadcast channels, including Downlink Control Information (DCI) transmitted via a Physical Downlink Control Channel (PDCCH), a Master Information Block (MIB) transmitted via a Physical Broadcast Channel (PBCH), and Uplink Control Information (UCI) transmitted via a Physical Uplink Control Channel (PUCCH) or a Physical Uplink Shared Channel (PUSCH).

[0010] To decode polar-encoded codewords, Successive Cancellation List (SCL) decoders are widely employed. In SCL decoding, a list size parameter determines the maximum number of candidate decoding paths that may be explored during the decoding process. An SCL decoder with a list size L may be viewed as utilizing L Successive Cancellation (SC) decoder cores that operate in parallel, each core independently processing received channel log-likelihood ratios (LLRs) to generate a candidate codeword corresponding to a different decoding path.

[0011] Figure 1 illustrates an example block diagram of decoder core utilization in a conventional SCL decoder, in accordance with an existing art. As shown, a plurality of SC decoder cores, namely SC decoder core 1 through SC decoder core LM, where LM denotes a maximum supported list size, operate concurrently on a common set of channel LLRs. Each SC decoder core produces a corresponding candidate codeword. All candidate codewords, up to LM in number, are provided to a cyclic redundancy check (CRC) evaluation block, which performs CRC checking to identify a valid decoded codeword, if any, from among the candidate codewords.

[0012] The introduction of SCL decoding addresses error propagation inherent in basic SC decoding by retaining multiple hypothesis paths at each information bit position, irrespective of the instantaneous LLR. At each leaf node of the decoding tree, both possible bit decisions are considered, resulting in an exponential growth in the number of decoding paths. To manage complexity, an SCL decoder with list size L limits the number of retained paths to L by discarding less-likely paths based on a path metric. The path metric represents an accumulated penalty associated with incorrect bit decisions derived from the LLRs, and a codeword corresponding to a path with a lowest path metric is selected as a candidate decoded output.

[0013] In practical 5G NR systems, polar codes are concatenated with an outer CRC and decoded using CRC-Aided SCL (CA-SCL) decoding in order to further improve error-correction performance for a limited list size. In CA-SCL decoding, CRC evaluation is used to reliably identify a correct codeword from among the L candidate codewords generated by the SCL decoder. Owing to performance requirements, a fixed list size of 32 is typically employed in both network-side and UE-side decoders.

[0014] However, the use of a fixed list size does not account for variations in channel conditions or transmission parameters. For achieving a target block error rate (BLER), smaller list sizes may be sufficient under high signal-to-noise ratio (SNR) conditions, whereas larger list sizes may be required under low SNR conditions. The optimal list size for meeting a desired BLER depends on factors such as code rate, codeword length, modulation and coding scheme, and time-varying and frequency-selective channel characteristics.

[0015] Moreover, since list size is directly proportional to the number of active decoder cores, increased list sizes result in increased decoding complexity, higher decoding latency, and elevated power consumption. Consequently, always operating the decoder at a maximum list size leads to inefficient utilization of computational and energy resources, particularly under favorable channel conditions.

[0016] Existing approaches for optimizing decoder parameters suffer from several limitations. Certain techniques attempt to modify the list size dynamically during the decoding process by activating or deactivating decoder cores on-the-fly, which is impractical for implementation due to tight timing constraints and hardware complexity. Other approaches fail to consider key coding-related parameters, such as code rate or code length, resulting in inaccurate or sub-optimal list size selection.

[0017] Hence, there is a need for a solution that overcomes the above-mentioned and other related problems.

[0018] This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention nor is it intended to determine the scope of the invention.

[0019] According to an embodiment of the present disclosure, a method for configuring one or more decoder optimization parameters for a decoder in a wireless communication system. The method includes obtaining, at a network element, one or more input parameters associated with at least one of: channel conditions, network configuration parameters, service type, and user equipment type. Further, the method includes generating, by an artificial intelligence-based model, the one or more decoder optimization parameters based on the one or more input parameters. Each decoder optimization parameter is associated with the decoder and comprises at least one of: a list size, a decoding depth level, and an iteration number. Furthermore, the method includes predicting, by the ML model, a decoding success probability for each of the one or more decoder optimization parameters. Moreover, the method includes selecting, based on the predicted decoding success probability, at least one parameter among the one or more decoder optimization parameters having a decoding success probability greater than a predefined threshold. The predefined threshold is determined based on at least one of: a network configuration parameter, a service type requirement, the user equipment type, prevailing channel conditions, and historical decoding performance metrics. Further, the method includes transmitting, selectively from the network element to another network element, an indication of the selected at least one parameter. Furthermore, the method includes configuring, at the another network element, the decoder based on the selected at least one parameter for decoding a received codeword.

[0020] According to an embodiment of the present disclosure, disclosed herein is a system for configuring one or more decoder optimization parameters for a decoder in a wireless communication system. The system includes one or more processors and a memory coupled with the one or more processors. The one or more processors are configured to obtain, at a network element, one or more input parameters associated with at least one of: channel conditions, network configuration parameters, service type, and user equipment type. Further, the one or more processors are configured to generate, by an artificial intelligence (AI) based model, the one or more decoder optimization parameters based on the one or more input parameters. Each decoder optimization parameter is associated with the decoder and comprises at least one of: a list size, a decoding depth level, and an iteration number. Furthermore, the one or more processors are configured to predict, by the AI-based model, a decoding success probability for each of the one or more decoder optimization parameters. Moreover, the one or more processors are configured to select, based on the predicted decoding success probability, at least one parameter among the one or more decoder optimization parameters having a decoding success probability greater than a predefined threshold. The predefined threshold is determined based on at least one of: a network configuration parameter, a service type requirement, the user equipment type, prevailing channel conditions, and historical decoding performance metrics. Further, the one or more processors are configured to transmit, selectively from the network element to another network element, an indication of the selected at least one parameter. Moreover, the one or more processors are configured to configure, at the another network element, the decoder based on the selected at least one parameter for decoding a received codeword.

[0021] To further clarify the advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawing. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting its scope. The invention will be described and explained with additional specificity and detail in the accompanying drawings.

[0022] For the purpose of promoting an understanding of the principles of the present disclosure, reference will now be made to the various embodiments, and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the present disclosure is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the present disclosure as illustrated therein, being contemplated as would normally occur to one skilled in the art to which the present disclosure relates.

[0023] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the present disclosure and are not intended to be restrictive thereof.

[0024] These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

[0025] Figure 1 illustrates an example block diagram of decoder core utilization in a conventional SCL decoder, in accordance with an existing art;

[0026] Figure 2 illustrates a block diagram depicting an environment for configuring one or more decoder optimization parameters for a decoder, in accordance with an embodiment of the present disclosure;

[0027] Figure 3 illustrates a block diagram of a system for configuring the one or more decoder optimization parameters for the decoder, in accordance with an embodiment of the present disclosure;

[0028] Figure 4 illustrates a block diagram of an Artificial Intelligence (AI)-based model integrated with a Successive Cancellation List (SCL) decoder system to identify the list size, in accordance with an embodiment of the present disclosure;

[0029] Figure 5 illustrates a Deep Neural Network (DNN) architecture for adaptive list size prediction, in accordance with an embodiment of the present disclosure;

[0030] Figure 6 illustrates an exemplary process flow for decoding a received codeword based on the configured one or more decoder optimization parameters, in accordance with the present disclosure;

[0031] Figure 7 illustrates an Open Radio Access Network (O-RAN) 7-x split architecture with AI-based model, in accordance with an embodiment of the present disclosure; and

[0032] Figure 8 illustrates a process flow of a method for configuring the one or more decoder optimization parameters for the decoder, in accordance with an embodiment of the present disclosure.

[0033] Figure 9 is a block diagram of a terminal or user equipment (UE) 900 according to an embodiment of the disclosure.

[0034] Figure 10 is a block diagram of a base station (BS) 1000 according to an embodiment of the disclosure.

[0035] Figure 11 is a block diagram of a network entity 1100 according to an embodiment of the disclosure.

[0036] Further, skilled artisans will appreciate that those elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help improve understanding of aspects of the present invention. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

[0037] Hereinafter, embodiments of the disclosure will be described in detail with reference to the accompanying drawings.

[0038] In describing the embodiments, while numerous details are set forth for the purpose of illustration, it is understood that some aspects of the disclosure may be practiced with less than all of these details. Numerous variations and alternatives to the details provided herein are possible and are considered within the scope of the disclosure. In some instances, descriptions related to technical contents well-known in the art may be omitted so as to not obscure an understanding of the disclosure, and such omitted descriptions are understood to be within the scope of the disclosure.

[0039] For the same reason, in the accompanying drawings, some elements may be exaggerated, omitted, or schematically illustrated. Further, the size of each element does not completely reflect the actual size. In the drawings, identical or corresponding elements are provided with identical reference numerals or different reference numerals.

[0040] The advantages and features of the disclosure and ways to achieve them will be apparent by making reference to embodiments as described herein in detail in conjunction with the accompanying drawings. However, the disclosure is not limited to the embodiments set forth herein, but may be implemented in various different forms. Other features, aspects, and advantages of the subject matter described herein will become apparent from the disclosure. The following embodiments are merely examples to aid in an understanding of the disclosure and should not be construed to narrow the scope or spirit of the subject matter described herein in any way, but on the contrary, the disclosure covers all modifications, equivalents and alternatives falling within the spirit and scope of the subject matter as defined by the appended claims and equivalents thereof. Throughout the specification, the same or like reference numerals designate the same or like elements. Furthermore, terms which will be described herein are terms defined in consideration of the functions in the disclosure, and may be different according to users, intentions of the operators, or customs. Therefore, the definitions of the terms should be made based on the contents throughout the specification.

[0041]

[0042] Herein, it will be understood that each block of flowchart illustrations, and combinations of blocks in the flowchart illustrations, may be performed based on computer program instructions. These computer program instructions may be loaded collectively onto at least one processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which perform through any one of, or in any combination of, the at least one processor of the computer or other programmable data processing apparatus, create means for performing the functions specified in the flowchart block(s). These computer program instructions may also be stored in a non-transitory computer usable or computer-readable memory that may direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer usable or computer-readable memory produce an article of manufacture including instruction means that perform the function specified in the flowchart block(s). The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable data processing apparatus to produce a computer executed process such that the instructions that perform on the computer or other programmable data processing apparatus provide steps for executing the functions specified in the flowchart block(s).

[0043] Further, each block may represent a module, segment, or portion of code, which includes one or more executable instructions for executing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks may occur out of the order. For example, two blocks (or functions) shown in succession may in fact be performed substantially concurrently or the blocks may sometimes be performed in the reverse order, depending upon the functionality involved.

[0044] As used in embodiments of the disclosure, a "~unit / module" may refer to a software element or a hardware element, such as a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC), which performs a predetermined function. However, the term including the word "~unit / module" does not always have a meaning limited to software or hardware. The "~unit / module" may be constructed either to be stored in an addressable storage medium or to execute one or more processors. Therefore, the "~unit / module" includes, for example, software elements, object-oriented software elements, components such as class elements and task elements, processes, functions, properties, procedures, sub-routines, segments of a program code, drivers, firmware, micro-codes, circuits, data, database, data structures, tables, arrays, and parameters. The components and functions provided by the "~unit / module" may be either combined into a smaller number of components and a "~unit / module," or divided into additional components and a "~unit / module." Moreover, the components and "~units / modules" may be implemented to reproduce one or more central processing units (CPUs) within a device or a security multimedia card. Further, in the embodiments, the "~unit / module" may include one or more processors.

[0045]

[0046] The entirety of the one or more computer programs may be stored in a single memory device or the one or more computer programs may be divided with different portions stored in different multiple memory devices.

[0047] Any of the functions or operations described herein can be processed by one processor or a combination of processors. The one processor or the combination of processors is circuitry performing processing and includes circuitry like an application processor (AP, e.g. a CPU), a communication processor (CP, e.g., a modem), a graphics processing unit (GPU), a neural processing unit (NPU) (e.g., an artificial intelligence (AI) chip), a Wi-Fi chip, a Bluetooth® chip, a global positioning system (GPS) chip, a near field communication (NFC) chip, connectivity chips, a sensor controller, a touch controller, a finger-print sensor controller, a display driver integrated circuit (IC), an audio CODEC chip, a universal serial bus (USB) controller, a camera controller, an image processing IC, microprocessors, microcontrollers, digital　signal processors, FPGA, ASIC, a microprocessor unit (MPU), a system on chip (SoC), an IC, or the like. The one processor or the combination of processors executes instructions that can be stored in a memory, such as the　operating system, in order to control the overall operation of the device.　Also, the one processor or the combination of processors is also capable of executing other processes and programs　resident in the memory, such as processes for the disclosure.

[0048] It will be appreciated that various embodiments of the disclosure according to the claims and description in the specification can be realized in the form of hardware, software or a combination of hardware and software.

[0049] Any such software may be stored in non-transitory computer readable storage media. The non-transitory computer readable storage media store one or more computer programs (software modules), the one or more computer programs include computer-executable instructions that, when executed by one or more processors of an electronic device individually or collectively, cause the electronic device to perform a method of the disclosure. Additionally, or alternatively, such software may be a computer program [product] comprising instructions which, when executed by one or more processors of an electronic device individually or collectively, cause the electronic device to perform a method of the disclosure.

[0050] Any such software may be stored in the form of volatile or non-volatile storage such as, for example, a storage device like read only memory (ROM), whether erasable or rewritable or not, or in the form of memory such as, for example, random access memory (RAM), memory chips, device or integrated circuits or on an optically or magnetically readable medium such as, for example, a compact disk (CD), digital versatile disc (DVD), magnetic disk or magnetic tape or the like. It will be appreciated that the storage devices and storage media are various embodiments of non-transitory machine-readable storage that are suitable for storing a computer program or computer programs comprising instructions that, when executed, implement various embodiments of the disclosure. Accordingly, various embodiments of the present disclosure may provide a program comprising code for implementing apparatus or a method as claimed in any one of the claims of this specification and a non-transitory machine-readable storage storing such a program.

[0051] Hereinafter, the determination of priority between A and B in the present disclosure may refer to various actions such as selecting the one having a higher priority based on a predefined priority rule and performing an operation corresponding thereto, or omitting or dropping an operation corresponding to the one having a lower priority.

[0052] Hereinafter, "A or B" as described in the present disclosure may be understood as "A and / or B," which may include A, or B, or both A and B.

[0053] In addition, "at least one of A, B, and C" as described in the present disclosure may be understood to include A, or B, or C, or any combination of A, B, and C.

[0054] In addition, "at least one of A, B, or C" as described in the present disclosure may be understood to include A, or B, or C, or any combination of A, B, and C.

[0055] Furthermore, "A / B" as described in the present disclosure may be understood as "A and / or B," which may include A, or B, or both A and B.

[0056] Furthermore, "A, B" as described in the present disclosure may be understood as "A and / or B," which may include A, or B, or both A and B.

[0057] Furthermore, "A and B" as described in the present disclosure may be understood as "A and / or B," which may include A, or B, or both A and B.

[0058] Furthermore, "if condition A and condition B are satisfied," as described in the present disclosure, may not be limited to a case where both condition A and condition B are satisfied, but may be understood to include a case where either condition A or condition B is individually satisfied, both condition A and condition B are satisfied, or one or more additional conditions are satisfied in combination.

[0059] Furthermore, throughout this disclosure, ordinal terms such as "first," "second," "third," etc., (and similar qualifiers) are used merely to distinguish between different instances, occurrences, configurations, messages, stages, elements or aspects of elements, operations, or information as described herein. Unless the context clearly dictates otherwise, the use of such ordinal terms does not itself require that the elements, operations, or information distinguished by these terms be structurally different, numerically distinct, or substantively dissimilar. For example, a "first signal" and a "second signal" may refer to instances of the same signal transmitted at different times or containing the same core information despite minor variations, or they may refer to signals with different content or characteristics, depending on the specific context. Similarly, a "first value" and a "second value" may represent the same magnitude but measured or applied in different circumstances, or they may represent different magnitudes. The interpretation should be guided by the specific technical context, function, and relationship described in the relevant portion of the specification and claims.

[0060] Furthermore, the terms "first ~", "second ~", etc., as described in the present disclosure with respect to various elements (e.g., information, objects, operation, sequences, or the like), should not limit those elements. These terms may only be intended to distinguish one element from another, and may not be intended to indicate a specific order. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element.

[0061] Furthermore, even if "first ~" and "second ~" are described in the present disclosure, it may be understood that element(s) referred to by "first ~" and "second ~" may be the same or different. For example, in case of element(s) being information, first information and second information may both be same information and, in some cases, are separate and different information.

[0062] In addition, the terms "if ~" and "in case that ~" as used in the disclosure or claims may be interpreted to include the meanings of "when (or upon) ~," "in response to ~," "based on ~," or "according to ~," and may be used interchangeably with these expressions. In addition, expressions other than those exemplified herein may also be used, as long as they have substantially the same meaning and do not impair the technical features of the present disclosure. If a method step (e.g. transmit a signal) is performed according to the disclosure of the application in connection with one of the above terms (such as "in case that ~" or the like), it may be interpreted to include the meanings (disclosure) of a prior determination that a feature has a specific state "~" (e.g. a bit length is above X), and then perform the method step in response to said determination.

[0063] For example, the physical layer signaling may be referred to as Layer 1 (L1) signaling and may include downlink control information (DCI). In addition, the higher layer signaling may include a medium access control (MAC) control message, a radio resource control (RRC) signaling message, a non-access stratum (NAS) signaling message, or an application layer message. The RRC signaling message may be referred to as L3 (layer 3) signaling. It should be noted, however, that the higher layer signaling is not limited to the aforementioned examples.

[0064] In addition, the term "not perform" as used in the present disclosure or claims may, in context, be understood to mean that the corresponding step is omitted or skipped. Such a term may be replaced with other terms having the same or substantially equivalent meaning.

[0065] In addition, "transmitting a message including A and B" as described in the present disclosure, may be understood as encompassing both (i) transmitting A and B in a single message, and (ii) transmitting A and B separately via multiple messages (e.g., transmitting a first message including A and a second message including B). This interpretation may also apply to messages that include two or more items (e.g., A, B, C), transmitted either together or separately.

[0066] In addition, "transmitting a message including A and transmitting a message including B" may also be interpreted as transmitting a message including A and B in a single message.

[0067] In the embodiments of the present disclosure described herein, terms or components included in the disclosure may be expressed in singular or plural form depending on the specific embodiments presented. However, such singular or plural expressions are selected appropriately for convenience of description, and the present disclosure is not limited to a singular or plural number of components. A component expressed in the plural form may be implemented as a single component, and a component expressed in the singular form may be implemented as multiple components.

[0068] The drawings or flowcharts described herein illustrate example methods that may be implemented according to the principles of the present disclosure, and various modifications may be made to the methods illustrated in the flowcharts of the present disclosure. For example, although illustrated as a series of steps, various steps in each drawing or flowchart may overlap, occur in parallel, occur in a different order, or be repeated. In other examples, any step may be omitted or replaced with another step.

[0069] The process of the flowchart may be performed by a device. One or more of the steps of the　flowchart can be implemented by one or more processors / computer programs executing　instructions to perform the noted functions.

[0070] The methods and apparatuses proposed in the embodiments of the present disclosure may be disclosed in connection with drawings disclosing flowcharts to illustrate example methods that may be implemented according to the principles of the present disclosure. Such flowcharts may contain different branches and / or sub-branches. It is understood that the principles of the present disclosure do not only contain the combination of all branches / sub-branches disclosed in the embodiment, but the present disclosure also contains at least one isolated branch / isolated sub-branch, in particular to a single branch / single sub-branch.

[0071] The methods and apparatuses proposed in the embodiments of the present disclosure are not limited to each embodiment individually, but may also be applied in combination of all or some of the embodiments proposed in the disclosure. Therefore, the embodiments of the present disclosure may be modified and applied without significantly departing from the scope of the present disclosure, as would be understood by those skilled in the art.

[0072] In this case, even if certain wordings are described differently across embodiments, they may be used interchangeably or in substitution or in combination if their underlying concepts are equivalent. For example, for the same or equivalent concept, even if one embodiment uses the expression "A" and another embodiment uses the expression "B", such expressions may be understood interchangeably, in substitution, or in combination.

[0073] The terms used in the following description to refer to access nodes, network entities, messages, interfaces between network entities, various types of identification information, and the like, are provided merely for the convenience of explanation by way of example. Therefore, the present disclosure is not limited to the terms describedherein, and other terms having equivalent technical meanings may also be used. Such terms may also be interchangeable with terms defined in any 3rd generation partnership project　(3GPP) technical specifications (TS) or similar technical specifications, e.g., from the European telecommunications standards institute (ETSI), where appropriate.

[0074] Hereinafter, a base station (BS) is an entity that allocates resources to terminals, and may be at least one of a gNode B, an eNode B, a Node B, a wireless access unit, a BS controller, or a node on a network.

[0075] Furthermore, the base station of the present disclosure may include a split architecture comprising a central unit (CU) and a distributed unit (DU). In this structure, the CU is configured to process the higher layers of the control and user planes, while the DU is configured to process lower-layer radio resource functions. The embodiments of the present disclosure may be equally applicable to 5th generation (5G) base station architectures in which such CU and DU functional splits are implemented.

[0076] A terminal may include a user equipment (UE), a mobile station (MS), a cellular phone, a smartphone, a computer, a tablet, a wearable device, an Internet of Things (IoT) device, or any other device / system capable of performing communication functions.

[0077] In the disclosure, a downlink (DL) refers to a radio link through which a BS transmits a signal to a terminal, and an uplink (UL) refers to a radio link through which a terminal transmits a signal to a BS.

[0078] Furthermore, hereinafter, 5G mobile communication technologies (e.g., 5G new radio (NR)), 6th generation (6G) mobile communication technologies may be described by way of example, but the embodiments of the present disclosure may also be applied to other communication systems having similar technical backgrounds or channel types. For example, newly evolved mobile communication systems developed after 5G and 6G may be included. Furthermore, based on determinations by those skilled in the art, the embodiments of the present disclosure may also be applied to other communication systems (e.g., Wi-Fi systems) through some modifications without significantly departing from the scope of the present disclosure

[0079] In the following description, the terms physical channel and signal may be used interchangeably with data or control signal. For example, the term physical downlink shared channel (PDSCH) refers to a physical channel through which data is transmitted, but the term PDSCH may also be used to refer to the data itself. That is, in the present disclosure, the expression "transmit a physical channel" may be interpreted as being equivalent to the expression "transmit data or a signal via a physical channel."

[0080] Hereinafter, in the context of the present disclosure, higher layer signaling may refer to signaling corresponding to at least one or any combination of the following: master information block (MIB), system information block (SIB) or SIB M (M = 1, 2, ...), RRC, or MAC control element (CE), or a non-access stratum (NAS) signaling message, or an application layer message. The RRC signaling message may be referred to as Layer 3 (L3) signaling.

[0081] In addition, L1 signaling may refer to signaling corresponding to at least one or any combination of signaling techniques using the at least one or any combination of the following physical layer channels or signaling: physical downlink control channel (PDCCH), DCI, UE-specific DCI, group-common DCI, common DCI, scheduling DCI (e.g., DCI used for scheduling downlink or uplink data), non-scheduling DCI (e.g., DCI not used for scheduling downlink or uplink data) physical uplink control channel (PUCCH), or uplink control information (UCI). The L1 signaling message may be referred to as a physical layer signaling.

[0082] Hereinafter, the expression that information is configured by the BS, as used in the present disclosure or claims, may, in context, be understood to mean that the terminal receives the corresponding information from the BS via a physical layer signaling or a higher layer signaling. Such an expression may be replaced with other terms having the same or substantially equivalent meaning.

[0083] Hereinafter, the operational principle of the present disclosure will be described in detail with reference to the accompanying drawings.

[0084]

[0085] Whether or not a certain feature or element was limited to being used only once, it may still be referred to as “one or more features” or “one or more elements,” “at least one feature,” or “at least one element.” Furthermore, the use of the terms “one or more” or “at least one” feature or element does not preclude there being none of that feature or element, unless otherwise specified by limiting language, including, but not limited to, “there needs to be one or more…” or “one or more elements are required.”

[0086] Reference is made herein to some “embodiments.” It should be understood that an embodiment is an example of a possible implementation of any features and / or elements of the present disclosure. Some embodiments have been described for the purpose of explaining one or more of the potential ways in which the specific features and / or elements of the proposed disclosure fulfill the requirements of uniqueness, utility, and non-obviousness.

[0087] Use of the phrases and / or terms including, but not limited to, “a first embodiment,” “a further embodiment,” “an alternate embodiment,” “one embodiment,” “an embodiment,” “multiple embodiments,” “some embodiments,” “other embodiments,” “further embodiment”, “furthermore embodiment”, “additional embodiment” or other variants thereof do not necessarily refer to the same embodiments. Unless otherwise specified, one or more particular features and / or elements described in connection with one or more embodiments may be found in one embodiment, or may be found in more than one embodiment, or may be found in all embodiments, or may be found in no embodiments. Although one or more features and / or elements may be described herein in the context of only a single embodiment, or in the context of more than one embodiment, or in the context of all embodiments, the features and / or elements may instead be provided separately or in any appropriate combination or not at all. Conversely, any features and / or elements described in the context of separate embodiments may alternatively be realized as existing together in the context of a single embodiment.

[0088] Any particular and all details set forth herein are used in the context of some embodiments and therefore should not necessarily be taken as limiting factors to the proposed disclosure.

[0089] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by “comprises... a” does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

[0090] The term “couple” and the derivatives thereof refer to any direct or indirect communication between two or more elements, whether or not those elements are in physical contact with each other. The terms “transmit”, “receive”, and “communicate”, as well as the derivatives thereof, encompass both direct and indirect communication. The term “or” is an inclusive term meaning “and / or”. The phrase “associated with,” as well as derivatives thereof, refer to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, have a relationship to or with, or the like. The term “controller” refers to any device, system, or part thereof that controls at least one operation. The functionality associated with any particular controller may be centralized or distributed, whether locally or remotely. The phrase “at least one of,” when used with a list of items, means that different combinations of one or more of the listed items may be used, and only one item in the list may be needed. For example, “at least one of A, B, and C” includes any of the following combinations: A, B, C, A and B, A and C, B and C, and A and B and C, and any variations thereof. As an additional example, the expression “at least one of a, b, or c” may indicate only a, only b, only c, both a and b, both a and c, both b and c, all of a, b, and c, or variations thereof. Similarly, the term “set” means one or more. Accordingly, the set of items may be a single item or a collection of two or more items.

[0091] Moreover, multiple functions described below may be implemented or supported by one or more computer programs, each of which is formed from computer-readable program code and embodied in a computer-readable medium. The terms “application” and “program” refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, related data, or a portion thereof adapted for implementation in a suitable computer-readable program code. The phrase “computer-readable program code” includes any type of computer code, including source code, object code, and executable code. The phrase “computer-readable medium” includes any type of medium capable of being accessed by a computer, such as Read Only Memory (ROM), Random Access Memory (RAM), a hard disk drive, a Compact Disc (CD), a Digital Video Disc (DVD), or any other type of memory. A “non-transitory” computer-readable medium excludes wired, wireless, optical, or other communication links that transport transitory electrical or other signals. A non-transitory computer-readable medium includes media where data may be permanently stored and media where data may be stored and later overwritten, such as a rewritable optical disc or an erasable memory device.

[0092] Any particular and all details set forth herein are used in the context of some embodiments and therefore should NOT be necessarily taken as limiting factors to the attached claims. The attached claims and their legal equivalents can be realized in the context of embodiments other than the ones used as illustrative examples in the description below.

[0093] Further, skilled artisans will appreciate those elements in the drawings that are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

[0094] For the sake of clarity, the first digit of a reference numeral of each component of the present disclosure is indicative of the Figure number, in which the corresponding component is shown. For example, reference numerals starting with digit “1” are shown at least in Figure 1. Similarly, reference numerals starting with digit “2” are shown at least in Figure 2. Further, similar reference numerals have been used to represent similar components in the Figures.

[0095] It should be noted that the terms “evidence” and “at least one proof of evidence” have been used interchangeably throughout the description and the drawings. Further, the terms “policy” and “one or more policy configurations” have been used interchangeably throughout the description and the drawings.

[0096] Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.

[0097] It is an object of the invention to provide a system and a method that overcome the limitations found in prior art related to wireless communication systems.

[0098] It is another object of the invention to provide a method and system for adaptively configuring one or more decoder optimization parameters for channel decoding in a wireless communication system.

[0099] It is another object of the invention is to determine the one or more decoder optimization parameters for a decoder using an Artificial Intelligence (AI)-based model based on channel conditions and coding-related parameters.

[0100] It is yet another object of the invention to provide the AI-assisted decoder optimization framework that is applicable to multiple decoder types and code families, including polar codes, Cyclic Redundancy Check (CRC)-aided polar codes, Polarization-Adjusted Convolutional (PAC) codes, Low Density Parity Check (LDPC) codes, and turbo codes, and that supports integration across centralized, distributed, and Open Radio Access Network (O-RAN) split architectures.

[0101]

[0102] Figure 2 illustrates a block diagram depicting an environment 200 for configuring one or more decoder optimization parameters for the decoder, in accordance with an embodiment of the present disclosure.

[0103] Referring to Figure 2, the environment 200 depicts an implementation of a system 208 for configuring one or more decoder optimization parameters in a decoder. In one implementation, the system 208 may be implemented at a network element, such as a base station, a distributed unit (DU), or a radio unit (RU). In an alternate implementation, the system 208 may be implemented within an electronic device, such as a user equipment (UE). In yet another implementation, the system 208 may be partially implemented across a plurality of network elements, and one or more functional blocks of the system 208 may be distributed between a transmitting network element and a receiving network element.

[0104] In a non-limiting example, the decoder may be a channel decoder configured for decoding control channels. The control channels may include one or more of a Physical Downlink Control Channel (PDCCH), a Physical Uplink Control Channel (PUCCH), and a Physical Broadcast Channel (PBCH). In another non-limiting example, the decoder may be a channel decoder configured for decoding data channels. The data channels may include one or more of a Physical Downlink Shared Channel (PDSCH) and a Physical Uplink Shared Channel (PUSCH).

[0105] In an embodiment, the system 208 may be operatively coupled to a network element 202 and another network element 204 communicating over a wireless communication channel 206. In an embodiment, the network element 202 and the another network element 204 may be selected from a group consisting of the BS, the UE, the RU, and the DU. For example, the network element 202 may be the BS and the another network element 204 may be the UE in a downlink communication scenario. In another example, the network element 202 may be a UE and the another network element 204 may be a BS in an uplink communication scenario. In yet another example, the network element 202 may be an RU and the another network element 204 may be a DU in an Open Radio Access Network (O-RAN) architecture.

[0106] In an embodiment, the system 208 may comprise at least one of: software, hardware, or a combination of software and hardware. The system 208 may be implemented at the network element 202 and configured to execute on one or more processors (not shown) of the network element 202, or to operate in communication with the network element 202 via a network interface (not shown). In an embodiment, the system 208 may be accessible to the network element 202 via a server (not shown). In a non-limiting example, the server may include a cloud-based server, such that the system 208 may be available for remote operation by the network element 202.

[0107] In an embodiment where the system 208 may be located external to the network element 202, the network interface may be configured to provide network connectivity and enable communication between the system 208, the network element 202, and the another network element 204 over the wireless communication channel 106. The network connectivity may be provided via at least one of: a wireless connection or a wired connection. By way of example and not limitation, the network connectivity may be provided using cellular communication technologies comprising third generation (3G), fourth generation (4G), fifth generation (5G), pre-5G, and sixth generation (6G), or short-range and local communication technologies comprising Bluetooth®, a Local Area Network (LAN), Wi-Fi, a cable-based connection, or any other wired or wireless communication technology.

[0108] In an embodiment, the system 208 may be configured to obtain, at the network element 202, one or more input parameters associated with at least one of: channel conditions, network configuration parameters, service type, and user equipment type. In a non-limiting example, the one or more input parameters may be selected from a group consisting of: channel estimate, signal-to-noise ratio (SNR), reference signal based SNR estimates comprising demodulation reference signal (DMRS) SNR, channel state information (CSI-RS) and synchronization signal block (SSB), modulation and coding scheme (MCS) index, code rate, codeword length, bandwidth, resource blocks, number of OFDM symbols, aggregation level, cyclic redundancy check (CRC) length, frozen bit pattern, polar sequence, the user equipment type, service type, and link abstraction metrics comprising received bit information rate (RBIR), mean mutual information bit (MMIB), and mean mutual information symbol (MMIS).

[0109] In an embodiment, the system 208 may be configured to generate, by an artificial intelligence (AI) based model, the one or more decoder optimization parameters 210 based on the one or more input parameters. In a non-limiting example, each decoder optimization parameter 210 may be associated with the decoder and include at least one of: a list size, a decoding depth level, and an iteration number. For example, for a Successive Cancellation List (SCL) decoder, the decoder optimization parameter may include a list size. For an iterative decoder, such as a Low Density Parity Check (LDPC) decoder or a turbo decoder, the decoder optimization parameter may include an iteration number. For a Successive Cancellation (SC) decoder, the decoder optimization parameter may include a decoding depth level.

[0110] In an embodiment, the AI-based model may be trained using supervised learning on labelled datasets comprising tuples of the one or more input parameters, decoder optimization parameter values, and decoding outcomes. The labelled datasets may be generated through offline simulations and / or field measurements under diverse channel conditions, code configurations, and service requirements, and the decoding outcomes may indicate successful decoding or decoding failure based on at least one predefined performance metric, such as block error rate (BLER) or frame error rate (FER).

[0111] In an embodiment, the supervised learning process may enable the AI-based model to learn a mapping between the one or more input parameters and an expected decoding performance corresponding to candidate decoder optimization parameters.

[0112] In an embodiment, the AI-based model may be further fine-tuned using online learning based on feedback obtained during live or real-time decoding operations. The feedback may comprise information indicative of decoding success or decoding failure observed at the decoder during actual data transmission and reception. Such online learning may enable continuous adaptation of the AI-based model to time-varying channel conditions, hardware impairments, and deployment-specific characteristics, thereby improving prediction accuracy and robustness over time. In a non-limiting example, the fine-tuning may be performed by updating model parameters incrementally based on newly observed decoding outcomes without interrupting normal decoding operations.

[0113] In an embodiment, the system 208 may be configured to predict, by the AI-based model, a decoding success probability for each of the one or more decoder optimization parameters.

[0114] In an embodiment, the system 208 may be configured to select, based on the predicted decoding success probability, at least one parameter among the one or more decoder optimization parameters having a decoding success probability greater than a predefined threshold. In a non-limiting example, the predefined threshold may be determined based on at least one of: a network configuration parameter, a service type requirement, the user equipment type, prevailing channel conditions, and historical decoding performance metrics.

[0115] In an embodiment, the system 208 may be configured to transmit, selectively from the network element to another network element, an indication of the selected at least one parameter. In a non-limiting example, the network element 202 and the another network element 204 may be selected from a group consisting of the BS, the UE, the RU, and the DU. In a non-limiting example, the indication of the selected at least one parameter may be transmitted selectively via at least one of: a downlink control information (DCI) message associated with at least one of: PDCCH and PBCH, a medium access control element (MAC-CE), a radio resource control (RRC) configuration message, and an uplink control information (UCI) message transmitted on a physical uplink control channel (PUCCH) and a physical uplink shared channel (PUSCH).

[0116] In an embodiment, the system 208 may be configured to configure, at the another network element 204, the decoder based on the selected at least one parameter for decoding a received codeword. In a non-limiting example, the received codeword may be encoded using at least one of a polar code, a parity aided polar code, a concatenated polar code, a turbo code, and a low density parity check (LDPC) code.

[0117] In an embodiment, the system 208 may be configured to adjust one or more internal resources of the decoder to optimize decoding performance based on the selected at least one parameter. In a non-limiting example, the one or more internal resources may include at least one of a number of decoder cores, an iteration control parameter, and a memory allocation parameter.

[0118]

[0119] Figure 3 illustrates a block diagram of the system 208 for configuring the one or more decoder optimization parameters for the decoder, in accordance with an embodiment of the present disclosure.

[0120] In an embodiment, the system 208 may include at least a processor 302, a memory 304, a plurality of modules 306, and a data unit 308. The processor 302, the memory 304, the plurality of modules 306, and the data unit 308 are communicably coupled with each other.

[0121] In an embodiment, the at least one processor 302 may be in communication with the memory 304. The at least one processor 302 may be a single processing unit or several units, all of which could include multiple computing units. The at least one processor 302 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and / or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 302 may be configured to fetch and execute computer-readable instructions and data stored in the memory 304.

[0122] In an embodiment, the memory 304 may include any non-transitory computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and / or non-volatile memory, such as read-only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.

[0123] In an embodiment, the plurality of modules 306 may be configured to configure the one or more decoder optimization parameters for the decoder.

[0124] In some embodiments, the plurality of modules 306 may include a set of instructions that can be executed to cause the system 306 to perform any one or more of the methods disclosed. The system 306 may operate as a standalone device or may be connected, e.g., using a network, to other computer systems or peripheral devices. Further, while a single processing unit is illustrated, the term “processing unit” shall also be taken to include any collection of processing units, implemented across the system 306 that individually or jointly execute a set, or multiple sets, of instructions to perform one or more computer functions.

[0125] In an embodiment, the plurality of modules 306 may be implemented using one or more artificial intelligence (AI) units that may include a plurality of neural network layers. Examples of neural networks include, but are not limited to, Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Restricted Boltzmann Machine (RBM). Further, 'learning' may be referred to in the disclosure as a method for training a predetermined target device (for example, a robot) using a plurality of learning data to cause, allow, or control the target device to make a determination or prediction. Examples of learning techniques include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. At least one of a plurality of CNN, DNN, RNN, RMB models and the like may be implemented to thereby achieve execution of the present subject matter's mechanism through an AI model. A function associated with an AI unit may be performed through the non-volatile memory, the volatile memory, and the processor. The processor 302 may include one or a plurality of processors. At this time, one or a plurality of processors may be a general-purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and / or an AI-dedicated processor, such as a neural processing unit (NPU). One or a plurality of processors control the processing of the input data in accordance with a predefined operating rule or artificial intelligence (AI) model stored in the non-volatile memory and the volatile memory. The predefined operating rule or artificial intelligence model is provided through training or learning.

[0126] In an embodiment, the data unit 308, amongst other things, includes routines, programs, objects, components, data structures, and the like, which perform tasks or implement data types. The data unit 308 may also be implemented as signal processor(s), state machine(s), logic circuitries, and / or any other device or component that manipulates signals based on operational instructions. Further, the data unit 308 may be implemented in hardware, instructions executed by a processing unit, or by a combination thereof. The processing unit may comprise a processor, such as the at least one processor 302, a state machine, a logic array, or any other suitable device capable of processing instructions. The processing unit may be a general-purpose processor that executes instructions to cause the general-purpose processor to perform the required tasks, or the processing unit can be dedicated to performing the required functions. In another embodiment of the present disclosure, the data unit 308 may be machine-readable instructions (software) that, when executed by the processor 302, perform any of the described functionalities.

[0127]

[0128] Figure 4 illustrates a block diagram 400 of an AI-based model integrated with an SCL decoder system to identify the list size, in accordance with an embodiment of the present disclosure.

[0129] As shown, the input parameters 402, including code rate, channel LLRs, RBIR, and effective or received SNR, are being fed into the AI-based model 404, which is an adaptive list size identifier block. The AI-based model 404 may output an adaptive list size LAdap. Further, in AI-based model 404, there may be two parallel paths: an upper path with L decoder cores all active, and a lower path with LAdap decoder cores active and the remaining decoder cores turned off. Both parallel paths may output LAdap codewords, which may then pass through a CRC check block to produce decoded information bits.

[0130] In an embodiment, the AI-based model 404 may identify the required list size for decoding a codeword adaptively based on one or more inputs 202 comprising varying channel conditions, LLRs, code rate, modulation order, code length, and MCS index. In an alternate embodiment, the adaptive list size may be found using Machine Learning (ML) classification models such as logistic regression, Naive Bayes, K-nearest neighbors, or decision trees. Further, Deep Learning (DL) based models based on Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), Transformers, Convolutional Neural Network (CNN) backbones, or a combination thereof may also be used. The models may be trained offline through simulated data, online through field data, or a mixture of both.

[0131]

[0132] Figure 5 illustrates a DNN architecture 500 for adaptive list size prediction, in accordance with an embodiment of the present disclosure.

[0133] As shown, the DNN architecture 500 may receive as input an estimated SNR on DMRS, denoted as Y1 through YD where D equals the number of DMRS tones, along with a code rate parameter R. The input layer contains NDMRS plus 1 neuron. The network may comprise two hidden layers, a first hidden layer may contain 32 neurons and employ a Rectified Linear Unit (ReLU) activation function; and a second hidden layer may contain 16 neurons and may also use the ReLU activation function. An output layer may contain M neurons corresponding to M possible list size values, with outputs labeled as Y1 through YM, and utilize a sigmoid activation function to produce probability values p1 through pM. The output probabilities may represent a likelihood of decoding success for each candidate list size value. A probability threshold comparison operation may be performed using threshold parameters α and to determine the final predicted adaptive list size LAdap.

[0134] In an embodiment, for training the AI-based model 404, for a given input vector X, the AI-based model 404 may predict an output vector Y of length M, corresponding to the probability of observing CRC-success when a corresponding list size is used. The input vector X may comprise the code rate and SNR corresponding to DMRS. The output Y of length M may correspond to the success probability of each list size from L1, L2, ..., LM. For example, for NDMRS equals 18, M equals 5, {L1, L2, L3, L4, L5} equals {2, 4, 8, 16, 32}, and if CRC-success is observed with L equals 16 and 32, then the input X equals [y1, y2, ..., y18, R] and the output Y equals [0, 0, 0, 1, 1].

[0135]

[0136] Figure 6 illustrates an exemplary process flow for decoding a received codeword based on the configured one or more decoder optimization parameters, in accordance with the present disclosure.

[0137] As illustrated in Figure 6, the one or more input parameters 402 are obtained at the network element and are associated with at least one of channel conditions, network configuration parameters, service type, and user equipment type, consistent with the claims. The channel LLRs 602 may explicitly denote log-likelihood ratios associated with the received codeword and form a part of the one or more input parameters 402 used in downstream decoder configuration and decoding operations.

[0138] In an embodiment, the AI-based model 404 may receive the input parameters 402 and generate the one or more decoder optimization parameters 210 based thereon. As shown, the generated decoder optimization parameter 210 may include the list size 406, corresponding to the predicted optimal adaptive list size L_Adap for the SCL decoder or the CA-SCL decoder. The AI-based model 404 may further predict a decoding success probability for the plurality of candidate list sizes, such as {2, 4, 8, 16, 32}, and select the list size 406 equal to L_Adap based on the predicted decoding success probability exceeding the predefined threshold. The predefined threshold may be determined based on at least one of the network configuration parameters 210, the service type requirement, the user equipment type, prevailing channel conditions, and historical decoding performance metrics.

[0139] Further, in response to the selection of the list size 406 equal to L_Adap, a decoder such as an SCL / CA-SCL decoder block comprising a plurality of SC decoder cores, may configure corresponding internal operation such that only L_Adap SC decoder cores remain active, while SCL Decoder Core L_Adap+1 through SCL Decoder Core L are deactivated. Such configuration may correspond to configuring the decoder based on the selected at least one decoder optimization parameters 210 and further adjusting one or more internal decoder resources, including the number of active decoder cores, so as to optimize decoding performance and power efficiency. Under the configured operation, the SCL / CA-SCL decoder may process the channel LLRs 602 using the active L_Adap SC decoder cores to generate L_Adap candidate codewords. The generated L_Adap candidate codewords may be provided to the CRC check 608 block. The CRC check 608 block may evaluate each of the candidate codewords and identify a valid decoded codeword by determining whether a corresponding candidate satisfies a CRC. An output of the CRC check 608 block may comprise decoded information bits 610, representing information bits associated with the candidate codeword that successfully passes the CRC.

[0140] In an embodiment, in an AI-supported cellular communication system, such as sixth generation (6G) wireless communication systems, an AI-based model 404 may be implemented at the network element, for example, the BS, to obtain one or more decoder optimization parameters 210 for configuring the decoder. In an embodiment, there may be a need to communicate the selected at least one parameter from the BS to the UE for decoding the received codeword. Accordingly, an indication of the selected at least one parameter may be transmitted selectively from the BS to the UE using one or more signaling mechanisms.

[0141] In an alternate or additional embodiment, the UE may comprise own AI-based model 404 and may estimate the one or more decoder optimization parameters 210.

[0142] In an embodiment, a capability indication phase may be provided, in which the UE may indicate the capability of supporting multiple list sizes, iteration numbers, and / or decoding depth levels through UE capability signaling. Such capability information may enable the BS to more accurately select the at least one decoder optimization parameter and to generate an appropriate indication thereof.

[0143] In an embodiment, the list size 406 may be indicated dynamically from the BS to the UE via DCI for decoding one or more of PDCCH and PBCH. Such dynamic indication may be advantageous in scenarios where channel conditions are time-varying, enabling the BS to convey the list size for upcoming DCI receptions and PBCH receptions through a current DCI transmission.

[0144] In an embodiment, the list size 406 may be indicated using reserved bits of an existing DCI format, or through a new DCI format created to support AI-assisted transmissions. For example, a list size indicator field may occupy log₂(NL) bits, where NL denotes a number of supported list sizes. In a non-limiting example, where NL = 5 and the supported list sizes comprise {2, 4, 8, 16, 32}, the list size indicator may be 2 bits, with different bit combinations mapping to different list sizes, and the absence of the field mapping to a default list size.

[0145] In an embodiment, the BS may indicate the list size 406 to the UE using a Medium Access Control Control Element (MAC-CE) for both PDCCH and PBCH upcoming decodes. The MAC-CE may comprise a bitmap of list sizes, with each bit representing a preferred status of a corresponding list size based on channel quality.

[0146] In a non-limiting example, if multiple list sizes are possible for PDCCH and PBCH decoding, the MAC-CE may indicate one preferred list size among the supported set. The MAC-CE may be transmitted as an octet, in which a portion of the octet may be used for PDCCH list size indication, and another portion may be used for PBCH list size indication. In an embodiment, the absence of the MAC-CE may be mapped to a default list size for both PDCCH and PBCH decoding.

[0147] In an embodiment, the list size 406 may be configured via RRC signaling for common channels and dedicated channels. The UE may indicate its capability to support different list sizes for decoding PDCCH and PBCH using an SCL decoder, and, based on such capability, the BS may indicate an appropriate list size through RRC configuration.

[0148] For PDCCH decoding, the RRC indication may be beneficial for reducing power consumption associated with blind decoding, and the list size configuration may be provided through PDCCH-Config or PDCCH-ConfigCommon. For PBCH decoding, the RRC indication may be provided through ServingCellConfigCommon or ServingCellConfigCommonSIB, in conjunction with other PBCH decoder parameters, such as transmit power, periodicity, and time-domain pattern.

[0149] In an embodiment, the list size may be indicated dynamically via UCI transmitted on PUCCH or PUSCH, enabling decoding of upcoming UCI at a base station. Such dynamic indication may facilitate adaptive configuration of the decoder, particularly in scenarios where prevailing channel conditions are time-varying, thereby allowing a user equipment (UE) to convey an indication of a list size to be applied for subsequent UCI receptions through a current UCI transmission.

[0150] In an embodiment, the UCI payload may be appended with bits to indicate a selected list size from a supported set { , , ..., LNL}, wherein one of the supported list sizes, for example LNL, may correspond to a default list size in the absence of an explicit indication.

[0151]

[0152] Figure 7 illustrates an Open Radio Access Network (O-RAN) 7-x split architecture 700 with AI-based model, in accordance with an embodiment of the present disclosure.

[0153] As shown, the O-RAN 7-x split architecture 700 displays a Distributed Unit (DU) and Multi-Radio Unit (MMU) connected through the 7-x interface. Within the MMU, the channel estimation may produce DMRS SNR estimates. The estimates may feed into the AI-based model 404, which outputs list size, iteration number, depth level, and decoder optimization variable parameters. The AI-based model may send the output through the 7-x interface to the DU, where a demodulation and decoding block 702 may receive both the one or more optimization parameters 210 and the processed signal data.

[0154] In the O-RAN split 7-x architecture, the channel estimation may be placed at the RU or MMU, and demodulation and decoder may be placed in the DU, the AI-based list size or decoder optimization variable prediction block (or AI-based model 404) may be placed within the MMU, where channel estimation may be performed. The AI-based model may use the SNR estimates on DMRS symbols along with code rate and predict an optimal parameter, such as the list size for decoding the received PUCCH signal. The adaptive list size may be identified using the AI-based model 404 and transmitted from the MMU to the DU along with equalized symbols to be used in the decoder.

[0155]

[0156] Figure 8 illustrates a process flow of a method 800 configuring the one or more decoder optimization parameters 210 for the decoder, in accordance with an embodiment of the present disclosure. The method 800 may be a computer-implemented method executed, for example, by the system 208. For the sake of brevity, constructional and operational features of the system 208 that are already explained in the description of Figures 1-7 are not explained in detail in the description of Figure 8.

[0157] At step 802, the method 800 may include obtaining, at the network element 202, the one or more input parameters 402 associated with at least one of: the channel conditions, the network configuration parameters, the service type, and the user equipment type. In a non-limiting example, the one or more input parameters may be selected from a group consisting of: channel estimate, SNR, reference signal based SNR estimates comprising DMRS SNR, CSI-RS and SSB, MCS index, code rate, codeword length, bandwidth, resource blocks, number of OFDM symbols, aggregation level, CRC length, frozen bit pattern, polar sequence, the user equipment type, service type, and link abstraction metrics comprising RBIR, MMIB, and MMIS.

[0158] At step 804, the method 800 may include generating, by the AI-based model, the one or more decoder optimization parameters 210 based on the one or more input parameters. In a non-limiting example, each decoder optimization parameters 210 may be associated with the decoder and include at least one of the list size, the decoding depth level, and the iteration number.

[0159] In an embodiment, the AI-based model may be trained using supervised learning on labelled datasets comprising tuples of the one or more input parameters, decoder optimization parameter values, and decoding outcomes. The labelled datasets may be generated through offline simulations and / or field measurements under diverse channel conditions, code configurations, and service requirements, and the decoding outcomes may indicate successful decoding or decoding failure based on at least one predefined performance metric, such as BLER or FER.

[0160] In an embodiment, the supervised learning process may enable the AI-based model to learn a mapping between the one or more input parameters and an expected decoding performance corresponding to candidate decoder optimization parameters.

[0161] In an embodiment, the AI-based model may be further fine-tuned using online learning based on feedback obtained during live or real-time decoding operations. The feedback may comprise information indicative of decoding success or decoding failure observed at the decoder during actual data transmission and reception. Such online learning may enable continuous adaptation of the AI-based model to time-varying channel conditions, hardware impairments, and deployment-specific characteristics, thereby improving prediction accuracy and robustness over time. In a non-limiting example, the fine-tuning may be performed by updating model parameters incrementally based on newly observed decoding outcomes without interrupting normal decoding operations.

[0162] At step 806, the method 800 may include predicting, by the AI-based model, the decoding success probability for each of the one or more decoder optimization parameters 210.

[0163] At step 808, the method 800 may include selecting, based on the predicted decoding success probability, the at least one parameter among the one or more decoder optimization parameters having a decoding success probability greater than a predefined threshold. In a non-limiting example, the predefined threshold may be determined based on at least one of the network configuration parameter, the service type requirement, the user equipment type, prevailing channel conditions, and historical decoding performance metrics.

[0164] At step 810, the method 800 may include to transmitting, selectively from the network element 202 to another network element 204, the indication of the selected at least one parameter. In a non-limiting example, the network element 202 and the another network element 204 may be selected from the group consisting of the BS, the UE, the RU, and the DU. In a non-limiting example, the indication of the selected at least one parameter may be transmitted selectively via at least one of DCI message associated with at least one of PDCCH and PBCH, MAC-CE, RRC configuration message and UCI) message transmitted on PUCCH and PUSCH.

[0165] At step 812, the method 800 may include configuring, at the another network element 204, the decoder based on the selected at least one parameter for decoding a received codeword. In a non-limiting example, the received codeword may be encoded using at least one of the polar code, the parity aided polar code, the concatenated polar code, the turbo code, and the LDPC code.

[0166] In an embodiment, the method 800 may include adjusting the one or more internal resources of the decoder to optimize decoding performance based on the selected at least one parameter. In a non-limiting example, the one or more internal resources may include at least one of the number of decoder cores, the iteration control parameter, and the memory allocation parameter.

[0167] In an embodiment, the methods and systems for configuring one or more decoder optimization parameters, may be applied to early and / or adaptive decoding of polar-encoded and polar-variant codewords in beyond fifth generation (Beyond-5G) and 6G wireless communication systems. Such application may be realized across multiple physical and logical channel transmissions supported in cellular communication systems.

[0168] In a non-limiting embodiment, the methods and systems of the present disclosure may be employed for decoding DCI transmitted on PDCCH, where the decoder optimization parameters, including a list size, may be configured to satisfy stringent reliability requirements. For example, for a target BLER on the order of 0.1%, received signals may be modulated using Quadrature Phase Shift Keying (QPSK).

[0169] In another non-limiting embodiment, the methods and systems of the present disclosure may be applied for decoding downlink data transmissions conveyed on PDSCH, where the one or more decoder optimization parameters 210 may be selected to meet a target BLER of approximately 10%, supported modulation schemes may include QPSK and higher-order modulation schemes such as 16-Quadrature Amplitude Modulation (16-QAM), particularly for relatively smaller code block lengths.

[0170] In another non-limiting embodiment, the methods and systems of the present disclosure may be applied for decoding UCI transmitted on PUCCH, where the decoder optimization parameters may be adaptively configured to satisfy ultra-reliable detection requirements. For example, in a target BLER of approximately 0.1%, the transmitted UCI may be modulated using QPSK.

[0171] In an embodiment, the methods and systems of the present disclosure may be applied for decoding UCI or uplink data transmissions conveyed on PUSCH, where the decoder optimization parameters may be selected in accordance with a target BLER of approximately 10%, and modulation schemes may include QPSK and 16-QAM, subject to supporting corresponding block lengths and transmission configurations.

[0172] In an embodiment, the decoder optimization framework may further be extended to Polarization-Adjusted Convolutional (PAC) codes, which are variants of polar codes incorporating an additional convolutional transformation to improve decoding performance. In an embodiment, the AI-based adaptive list identifier disclosed herein may be employed in a list decoder for PAC codes, in place of, or in addition to, an SCL decoder, without departing from the scope of the present disclosure.

[0173] The present disclosure provides various advantages as mentioned below:

[0174] a) The present disclosure provides AI-based model that may enable adaptive configuration aligned with channel conditions, network configuration parameters, service type, and user equipment (UE) type.

[0175] b) The present disclosure provides power-efficiency improvements by adjusting internal decoder resources.

[0176] c) The present disclosure provides latency reduction by adaptively reducing list size or iteration count when predicted success remains high.

[0177] d) The present disclosure unifies optimization for both control channels and data channels.

[0178] e) The present disclosure provides support across channels such as PDCCH, PBCH, PUCCH, PUSCH, and PDSCH.

[0179] f) The present disclosure provides improved resource utilization by avoiding operation at maximum list size under favorable conditions.

[0180] g) The present disclosure reduces complexity and energy consumption, contributing to sustainability objectives.

[0181] h) The present disclosure may meet timing constraints and reduce hardware or firmware complexity.

[0182] As would be apparent to a person in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein. The drawings and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not necessarily limited to the manner described herein.

[0183] Moreover, the actions of any signal flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts.

[0184]

[0185] Figure 9 is a block diagram of a terminal or user equipment (UE) 900 according to an embodiment of the disclosure.

[0186] The terminal is an electronic device capable of wireless communication and having various form factors, examples of the terminal may include a UE, a mobile station (MS), a cellular phone, a smartphone, a computer, a tablet, a wearable device, an Internet of Things (IoT) device, or any other device / system capable of performing wireless communication with a base station (BS) and / or another terminal through a wireless channel.

[0187] Referring to Figure 9, the UE 900 may include at least one transceiver (hereinafter, referred to as simply "transceiver") 901, at least one processor (hereinafter, referred to as simply "processor") 902, and at least one memory (hereinafter, referred to as simply "memory") 903. According to at least one or a combination of methods corresponding to the embodiments described in the present disclosure, the transceiver 901, the processor 902, and the memory 903 of the UE 900 may operate. However, components of the UE 900 are not limited to the example components illustrated in Figure 9. In another embodiment, the UE 900 may further include additional components in addition to the above-mentioned components, or some components may be omitted. Further, in some embodiments, any combination of the transceiver 901, the processor 902, or the memory 903 may be integrated in the form of one component.

[0188] The transceiver 901 may be a communication circuit or communication circuitry that enables the UE 900 to perform wireless communication with a node or an entity of a network. For example, the transceiver 901 may enable the UE 900 to transmit or receive a signal to or from a BS through cellular communication, or to transmit or receive a signal to or from another UE through cellular communication. For example, the transceiver 901 may support at least one of various cellular communication technologies including 3rd generation (3G), 4thgeneration (4G), long term evolution (LTE), 5th generation (5G) NR, 6thgeneration (6G), and various cellular wireless communication technologies supported by the transceiver (901) may include all subsequent generations of evolved wireless communications.

[0189] According to an embodiment, the UE 900 may include a plurality of transceivers. For example, in the case of supporting evolved-universal terrestrial radio access-new radio (E-UTRA-NR) dual connectivity (EN-DC), the UE 900 may include a first transceiver supporting the 4G LTE wireless communication and a second transceiver supporting the 5G NR wireless communication. According to another embodiment, in the case of supporting NR-dual connectivity (NR-DC), the UE 900 may include a plurality of transceivers supporting the 5G NR wireless communication. According to still another embodiment, in the case of supporting near field wireless communication, the UE 900 may separately include a transceiver supporting at least one standard in the group of wireless communication protocol standards as defined in the protocol standards for Bluetooth®, wireless local area network (WLAN) network (including institute of electrical and electronics engineers (IEEE) 802.11-2016 standard or its amendments, e.g., 802.11ah, 802.11ad, 802.11ay, 802.11ax, 802.11az, 802.11ba, and 802.11be, without being limited thereto).

[0190] According to an embodiment, the transceiver 901 may include various circuit structures used to transmit or receive signals to or from a BS through a wireless channel. The signals may include control information and data. For example, the transceiver 901 may include a radio frequency (RF) transmitter for up-converting and amplifying the frequency of a transmitted signal and an RF receiver for low-noise-amplifying a received signal and down-converting the frequency thereof. The transceiver 901 may output a signal received through a wireless channel to the processor 902 and may transmit, through a wireless channel, a signal output from the processor 902.

[0191] The processor 902 may control general operations of the UE 900 according to embodiments of the disclosure. The processor 902 may be implemented by one or more integrated circuit (or circuitry) (IC) chips and may execute various data processing operations. The processor 902 may include at least one electric circuit, and may execute instructions (or a program, codes, data, etc.) stored in the memory 903, individually, collectively or in any combination thereof. Further, the processor 902 may include a single-core processor or multi-core processor, and may include a processor assembly including a plurality of processing circuits (circuitry) according to a specific implementation scheme.

[0192] The processor 902 may be electrically, operatively, and / or communicatively coupled to the transceiver 901 to control the transceiver 901.

[0193] The processor 902 may include at least one processor (or processing circuitry), and the at least one processor may perform the following operations individually, collectively or in any combination thereof. For example, the processor 902 may include a communication processor (CP) configured to control communication operations and an application processor (AP) configured to control execution of an upper layer (for example, an application layer). In a specific embodiment, at least a part of the processor 902 may be included in one chip (or IC) and the other part of the processor 902 may be included in another chip (or IC). Otherwise, at least one processor may be included in another component, for example, the transceiver 901 or the memory 903.

[0194] The processor 902 may perform or control or cause an operation of the UE 900 for executing at least one or a combination of methods according to embodiments of the disclosure. For example, the processor 902 may control operations of the UE 900 for processing a downlink signal received from a BS or generating and transmitting an uplink signal to a BS. To this end, the processor 902 may execute a computer program, codes, or instructions stored in the memory 903, so as to control other components of the UE 900 to enable execution of various operations.

[0195] The memory 903 corresponds to a hardware storage device capable of temporarily or permanently storing information and may include one or more storage media. For example, the memory 903 may include a memory assembly including one or more storage media. For example, the one or more storage media may include permanent memory, such as a hard drive, flash memory, or read-only memory (ROM), semipermanent memory, such as random access memory (RAM), cache memory, or a combination thereof.

[0196] The memory 903 may be electrically, operatively, and / or communicatively coupled to the processor 902 and may be accessed by the processor 902.

[0197] The memory 903 may store a computer program, codes, or instructions executable by the processor 902. According to an embodiment, a computer program, codes, or instructions executable by the processor 902 may be either stored in a single memory device or separated and distributedly stored in two or more memory devices. By executing the instructions stored in the memory 903, the processor 902 may perform various functions according to an embodiment of the disclosure.

[0198] According to an embodiment of the disclosure, operations of the UE 900 may be caused to be performed based on execution of instructions (or a computer program or codes) stored in the memory 903 by at least one processor (or processing circuitry) configured to execute the same individually, collectively, or in any combination thereof, based on processing circuitry that is not configured to execute instructions, and / or based on components of processing circuitry that is not configured to execute instructions.

[0199]

[0200] Figure 10 is a block diagram of a base station (BS) 1000 according to an embodiment of the disclosure.

[0201] The BS 1000 may perform wireless communication with at least one user equipment (UE) located within the area of the BS 1000 through a wireless channel. The BS 1000 may perform communication with a node or an entity of a network through wired or wireless communication.

[0202] Referring to Figure 10, the BS 1000 may include at least one transceiver (hereinafter, referred to as simply "transceiver") 1001, at least one processor (hereinafter, referred to as simply "processor") 1002, and at least one memory (hereinafter, referred to as simply "memory") 1003. According to at least one or a combination of methods corresponding to the embodiments described in the present disclosure, the transceiver 1001, the processor 1002, and the memory 1003 of the BS 1000 may operate. However, components of the BS 1000 are not limited to the example components illustrated in Figure 10. In another embodiment, the BS 1000 may further include additional components in addition to the above-mentioned components, or some components may be omitted. Further, in some embodiments, any combination of the transceiver 1001, the processor 1002, or the memory 1003 may be integrated in the form of one component.

[0203] The transceiver 1001 may be a communication circuit or communication circuitry that enables the BS 1000 to perform wireless communication with a node or an entity of a network. For example, the transceiver 1001 may enable the BS 1000 to transmit or receive a signal to or from the UE X00 through cellular communication, or to transmit or receive a signal to or from another network entity through wireless communication. For example, the transceiver 1001 may support various cellular communication technologies including 3rd generation (3G), 4thgeneration (4G), long term evolution (LTE), 5th generation (5G) NR, 6thgeneration (6G), and various cellular wireless communication technologies supported by the transceiver (1001) may include all subsequent generations of evolved wireless communications. According to an embodiment, the transceiver 1001 may include various circuit structures used to transmit or receive signals to or from a UE through a wireless channel. The signals may include control information and data. For example, the transceiver 1001 may include a radio frequency (RF) transmitter for up-converting and amplifying the frequency of a transmitted signal and an RF receiver for low-noise-amplifying a received signal and down-converting the frequency thereof. The transceiver 1001 may output a signal received through a wireless channel to the processor 1002 and may transmit, through a wireless channel, a signal output from the processor 1002.

[0204] Meanwhile, according to an embodiment of the present disclosure, the BS 1000 may perform communication with a node or an entity of a network through wired or wireless communication. For example, the BS 1000 may perform wired or wireless communication with an adjacent BS, or a node or an entity of a core network through a backhaul network. Although not illustrated in Figure 10, when the BS 1000 performs wired communication, the BS 1000 may further include a separate network interface for wired communication in addition to the transceiver 1001. The network interface may be referred to as network interface circuitry or communication interface circuitry.

[0205] The processor 1002 may control general operations of the BS 1000 according to embodiments of the disclosure. The processor 1002 may be implemented by one or more integrated circuit (or circuitry) (IC) chips and may execute various data processing operations. The processor 1002 may include at least one electric circuit, and may execute instructions (or a program, codes, data, etc.) stored in the memory 1003, individually, collectively or in any combination thereof. Further, the processor 1002 may include a single-core processor or multi-core processor, and may include a processor assembly including a plurality of processing circuits (circuitry) according to a specific implementation scheme.

[0206] The processor 1002 may be electrically, operatively, and / or communicatively coupled to the transceiver 1001 to control the transceiver 1001.

[0207] The processor 1002 may include at least one processor (or processing circuitry), and the at least one processor may perform the following operations individually, collectively or in any combination thereof. In a specific embodiment, at least a part of the processor 1002 may be included in one chip (or IC) and the other part of the processor 1002 may be included in another chip (or IC). Otherwise, at least one processor may be included in another component, for example, the transceiver 1001 or the memory 1003.

[0208] The processor 1002 may perform or control or cause an operation of the BS 1000 for executing at least one or a combination of methods according to embodiments of the disclosure. For example, the processor 1002 may control operations of the BS 1000 for generating and transmitting a downlink signal to a UE or processing an uplink signal received from a UE. Otherwise, the BS 1000 may transmit or receive a signal to or from a neighboring BS, transfer a signal received from a UE to an upper node of the network, or transmit a signal transferred from an upper node of the network to a UE. To this end, the processor 1002 may execute a computer program, codes, or instructions stored in the memory 1003, so as to control other components of the BS 1000 to enable execution of various operations.

[0209] The memory 1003 corresponds to a hardware storage device capable of temporarily or permanently storing information and may include one or more storage media. For example, the memory 1003 may include a memory assembly including one or more storage media. For example, the one or more storage media may include permanent memory, such as a hard drive, flash memory, or read-only memory (ROM), semipermanent memory, such as random access memory (RAM), cache memory, or a combination thereof.

[0210] The memory 1003 may be electrically, operatively, and / or communicatively coupled to the processor 1002 and may be accessed by the processor 1002.

[0211] The memory 1003 may store a computer program, codes, or instructions executable by the processor 1002. According to an embodiment, a computer program, codes, or instructions executable by the processor 1002 may be either stored in a single memory device or separated and distributedly stored in two or more memory devices. By executing the instructions stored in the memory 1003, the processor 1002 may perform various functions according to an embodiment of the disclosure.

[0212] According to an embodiment of the disclosure, operations of the BS 1000 may be caused to be performed based on execution of instructions (or a computer program or codes) stored in the memory 1003 by at least one processor (or processing circuitry) configured to execute the same individually, collectively, or in any combination thereof, based on processing circuitry that is not configured to execute instructions, and / or based on components of processing circuitry that is not configured to execute instructions.

[0213]

[0214] The UE or the base station may perform various communication procedures related to the control plane or the user plane by cooperating with one or more network entities based on wireless communication. For example, the UE may communicate with a network entity (for example, an Access and Mobility Management Function (AMF), a Session Management Function (SMF), rtc.) via the base station, or the base station may perform at least one communication procedure by directly transmitting and receiving signals to / from, or relaying signals between, the network entities.

[0215] The structure of the above-described network entity will be described in more detail with reference to the drawings.

[0216] Figure 11 is a block diagram of a network entity 1100 according to an embodiment of the disclosure.

[0217] The network entity 1100 may include an entity (apparatus, device, or server, etc.) that performs one or more network functions (NFs) or a part of a network function constituting a core network (e.g., a 5th generation (5G) core (5GC)) in a communication system. In this case, multiple NFs may be implemented within a single network entity, or a single NF may be distributed and implemented across a plurality of network entities. In addition, when an NF is implemented within the network entity, the NF may be implemented in the form of software, and in such a case, a program for operating the NF may be stored in memory of the network entity 1100.

[0218] A single NF may be implemented by one or more instances, which may be deployed on the same network entity or distributed across multiple network entities to operate. The instance may be a software unit that logically executes a specific network function, and may be implemented in a form that is decoupled from physical hardware resources. Further, one or more NFs may be implemented in the form of one network slice to operate to satisfy specifications required by a particular service.

[0219] The NF may include at least one of an access and mobility management function (AMF), a session management function (SMF), a local session management function (L-SMF), a user plane function (UPF), a local user plane function (L-UPF), a policy control function (PCF), a unified data management (UDM), a unified data repository (UDR), a network exposure function (NEF), a network repository function (NRF), an application function (AF), a network slice selection function (NSSF), a network data analytics function (NWDAF), a network slice admission control function (NSACF), an authentication server function (AUSF), or a data network (DN), etc.

[0220] Referring to Figure 11, the network entity 1100 may include at least one network interface 1101, at least one processor 1102 (hereinafter, "processor"), and at least one memory 1103 (hereinafter, "memory"). As described above, a NF may be implemented in the form of a physical device such as the network entity 1100, or may be virtualized and executed in the form of an instance. When implemented as an instance, the NF need not necessarily include physical components as illustrated in Figure 11. In such a case, the instance may be logically represented as comprising one or more logical functional elements.

[0221] According to at least one or a combination of methods corresponding to the embodiments described in the present disclosure, the network interface 1101, the processor 1102, and the memory 1103 of the network entity 1100 may operate. However, components of the network entity 1100 are not limited to the example components illustrated in Figure 11. In another embodiment, the network entity 1100 may further include additional components in addition to the above-mentioned components, or some components may be omitted. Further, in an embodiment, the network interface 1101, the processor 1102, or the memory 1103 may be integrated in the form of one component.

[0222] The network interface 1101 is a collective term for a transmitter part of the network entity 1100 and a receiver part of the network entity 1100, and may be a communication circuit for transmitting or receiving a signal to or from a user equipment (UE), a base station (BS), or another network entity. Here, the communication circuit may include both a communication circuit for wireless communication and a communication circuit for a wired communication. For example, the network interface 1101 may include a circuit, logic, hardware, etc., configured to exchange a control plane message or a user plane message with a UE, a BS, or other core network entities through wireless communication or wired communication. The network interface 1101 may operate using various protocols (e.g., non-access stratum (NAS) protocol). The network interface 1101 may also be referred to, for convenience of description or depending on implementation, as communication circuitry, network interface circuitry, or a communication interface circuitry.

[0223] The processor 1102 may control general operations of the network entity 1100 according to embodiments of the disclosure. The processor 1102 may be implemented by one or more integrated circuit (or circuitry) (IC) chips and may execute various data processing operations. The processor 1102 may include at least one electric circuit, and may execute instructions (or a program, codes, data, etc.) stored in the memory 1103, individually, collectively or in any combination thereof. Further, the processor 1102 may include a single-core processor or multi-core processor, and may include a processor assembly including a plurality of processing circuits (circuitry) according to a specific implementation scheme. Further, it should be noted that, according to another embodiment, in a case where NF is implemented in the form of an instance, the network function may be not necessarily configured by physical hardware.

[0224] According to an embodiment, the processor 1102 may be electrically, operatively, and / or communicatively coupled to the network interface 1101 to control the network interface 1101.

[0225] The processor 1102 may include at least one processor (or processing circuitry), and the at least one processor may perform the following operations individually, collectively or in any combination thereof. In a specific embodiment, at least a part of the processor 1102 may be included in one chip (or IC) and the other part of the processor 1102 may be included in another chip (or IC). Otherwise, at least one processor may be included in another component, for example, the network interface 1101 or the memory 1103.

[0226] The processor 1102 may perform or control or cause an operation of the network entity 1100 for executing at least one or a combination of methods according to embodiments of the disclosure. For example, the processor 1102 may control operations of the network entity 1100 for exchanging a control plane message or a user plane message with a UE, a BS, or other core network entities through wireless or wired communication, using various protocols (e.g., NAS protocol). To this end, the processor 1102 may execute a computer program, codes, or instructions stored in the memory 1103, so as to control other components of the network entity 1100 to enable execution of various operations.

[0227] The memory 1103 corresponds to a hardware storage device capable of temporarily or permanently storing information and may include one or more storage media. For example, the memory 1103 may include a memory assembly including one or more storage media. For example, the one or more storage media may include permanent memory, such as a hard drive, flash memory, or read-only memory (ROM), semipermanent memory, such as random access memory (RAM), cache memory, or a combination thereof.

[0228] The memory 1103 may be electrically, operatively, and / or communicatively coupled to the processor 1102 and may be accessed by the processor 1102.

[0229] The memory 1103 may store a computer program, codes, or instructions executable by the processor 1102. According to an embodiment, a computer program, codes, or instructions executable by the processor 1102 may be either stored in a single memory device or separated and distributedly stored in two or more memory devices. By executing the instructions stored in the memory 1103, the processor 1102 may perform various functions according to an embodiment of the disclosure.

[0230] According to an embodiment of the disclosure, operations of the network entity 1100 may be caused to be performed based on execution of instructions (or a computer program or codes) stored in the memory 1103 by at least one processor (or processing circuitry) configured to execute the same individually, collectively, or in any combination thereof, based on processing circuitry that is not configured to execute instructions, and / or based on components of processing circuitry that is not configured to execute instructions.

[0231] Meanwhile, although specific embodiments of the present disclosure have been described in detail, various modifications may be made without departing from the scope of the present disclosure. Therefore, the scope of the present disclosure should not be limited to the described embodiments, but should be defined by the claims and equivalents thereof.

Claims

1.A method (800) for configuring one or more decoder optimization parameters for a decoder in a wireless communication system, the method comprising:obtaining (802), at a network element, one or more input parameters associated with at least one of: channel conditions, network configuration parameters, a service type and a user equipment type;generating (804), by an artificial intelligence (AI) based model, the one or more decoder optimization parameters based on the one or more input parameters,predicting (806), by the AI-based model, a decoding success probability for each of the one or more decoder optimization parameters;selecting (808), based on the predicted decoding success probability, at least one parameter among the one or more decoder optimization parameters having a decoding success probability greater than a predefined threshold,transmitting (810), selectively from the network element (202) to another network element (204), an indication of the selected at least one parameter; andconfiguring (812), at another network element (204), the decoder based on the selected at least one parameter for decoding a received codeword.2.The method (800) as claimed in claim 1,wherein each decoder optimization parameter is associated with the decoder and comprises at least one of: a list size, a decoding depth level, and an iteration number.3.The method (800) as claimed in claim 1,wherein the predefined threshold is determined based on at least one of: a network configuration parameter, a service type requirement, the user equipment type, prevailing channel conditions, and historical decoding performance metrics.4.The method (800) as claimed in claim 1, wherein configuring the decoder comprising:adjusting one or more internal resources of the decoder to optimize decoding performance based on the selected at least one parameter, wherein the one or more internal resources comprises at least one of: a number of decoder cores, an iteration control parameter, and a memory allocation parameter.5.The method (800) as claimed in claim 1, wherein the one or more input parameters is selected from a group consisting of: a channel estimate, a signal-to-noise ratio (SNR), a reference signal based SNR estimates comprising a demodulation reference signal (DMRS) SNR, channel state information (CSI-RS) and a synchronization signal block (SSB), a modulation and coding scheme (MCS) index, a code rate, a codeword length, a bandwidth, resource blocks, number of OFDM symbols, an aggregation level, a cyclic redundancy check (CRC) length, a frozen bit pattern, a polar sequence, the user equipment type, the service type, and link abstraction metrics comprising received bit information rate (RBIR), mean mutual information bit (MMIB), and mean mutual information symbol (MMIS).6.The method (800) as claimed in claim 1, wherein the received codeword is encoded using at least one of: a polar code, a parity aided polar code, a concatenated polar code, a turbo code, and a low density parity check (LDPC) code.7.The method (800) as claimed in claim 1, wherein the network element and the another network element are selected from a group consisting of: a base station, a user equipment (UE), a radio unit (RU), and a distributed unit (DU).8.The method (800) as claimed in claim 1, wherein the indication of the selected at least one parameter is transmitted selectively via at least one of: a downlink control information (DCI) message associated with at least one of: a physical downlink control channel (PDCCH) and physical broadcast channel (PBCH), a medium access control element (MAC-CE), a radio resource control (RRC) configuration message and an uplink control information (UCI) message transmitted on a physical uplink control channel (PUCCH) and a physical uplink shared channel (PUSCH).9.A system (208) for configuring one or more decoder optimization parameters for a decoder in a wireless communication system, the system (208) comprising:one or more processors (302);a memory (304) coupled with the one or more processors (302), wherein the one or more processors (302) are configured to:obtain, at a network element (202), one or more input parameters associated with at least one of: channel conditions, network configuration parameters, service type and user equipment type;generate, by an artificial intelligence (AI) based model (404), the one or more decoder optimization parameters (210) based on the one or more input parameters,predict, by the AI-based model (404), a decoding success probability for each of the one or more decoder optimization parameters;select, based on the predicted decoding success probability, at least one parameter among the one or more decoder optimization parameters having a decoding success probability greater than a predefined threshold,transmit, selectively from the network element (202) to another network element (204), an indication of the selected at least one parameter; andconfigure, at another network element (204), the decoder based on the selected at least one parameter for decoding a received codeword.10.The system (208) as claimed in claim 9,wherein each decoder optimization parameter is associated with the decoder and comprises at least one of: a list size, a decoding depth level, and an iteration number, andwherein the predefined threshold is determined based on at least one of: a network configuration parameter, a service type requirement, the user equipment type, prevailing channel conditions, and historical decoding performance metrics.11.The system (208) as claimed in claim 9, wherein the one or more processors (202) are further configured to:adjust one or more internal resources of the decoder to optimize decoding performance based on the selected at least one parameter, wherein the one or more internal resources comprises at least one of: a number of decoder cores, an iteration control parameter, and a memory allocation parameter.12.The system (208) as claimed in claim 9, wherein the one or more input parameters is selected from a group consisting of: a channel estimate, a signal-to-noise ratio (SNR), a reference signal based SNR estimates comprising a demodulation reference signal (DMRS) SNR, channel state information (CSI-RS) and a synchronization signal block (SSB), a modulation and coding scheme (MCS) index, a code rate, a codeword length, a bandwidth, resource blocks, number of OFDM symbols, an aggregation level, a cyclic redundancy check (CRC) length, a frozen bit pattern, a polar sequence, the user equipment type, the service type, and link abstraction metrics comprising received bit information rate (RBIR), mean mutual information bit (MMIB), and mean mutual information symbol (MMIS).13.The system (208) as claimed in claim 9, wherein the received codeword is encoded using at least one of: a polar code, a parity aided polar code, a concatenated polar code, a turbo code, and a low density parity check (LDPC) code.14.The system (208) as claimed in claim 9, wherein the network element (202) and the another network element (204) are selected from a group consisting of: a base station, a user equipment (UE), a radio unit (RU), and a distributed unit (DU).15.The system (208) as claimed in claim 9, wherein the indication of the selected at least one parameter is transmitted selectively via at least one of: a downlink control information (DCI) message associated with at least one of: a physical downlink control channel (PDCCH) and physical broadcast channel (PBCH), a medium access control element (MAC-CE), a radio resource control (RRC) configuration message and an uplink control information (UCI) message transmitted on a physical uplink control channel (PUCCH) and a physical uplink shared channel (PUSCH).

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