Methods and systems for performing efficient HARQ decoding in c-ran systems

Abstract

The disclosure relates to a 5G communication system or a 6G communication system for supporting higher data rates beyond a 4G communication system such as long term evolution (LTE). Embodiments herein disclose methods and systems for performing efficient Hybrid Automatic Repeat Request (HARQ) decoding in Centralized-Radio Access Networks (C-RAN) systems, based on a decoding status predicted at a Remote Unit (RU) in a wireless network. Embodiments herein disclose a 1-bit feedback mechanism from the RU to a Distributed Unit (DU) to improve the power efficiency of operations at the DU. The RU predicts if a data packet received in the uplink from a UE is decodable or not, using an Effective SINR (ESINR) approach and intimates the same to the DU using a 1-bit feedback. The DU proceeds with the channel decoding operation based on the indication received.

Description

METHODS AND SYSTEMS FOR PERFORMING EFFICIENT HARQ DECODING IN C-RAN SYSTEMS

[0001] Embodiments disclosed herein relate to Centralized-Radio Access Networks (C-RAN) systems, and more particularly to managing Hybrid　Automatic Repeat Request　(HARQ)　decoding in C-RAN systems.

[0002] Considering the development of wireless communication from generation to generation, the technologies have been developed mainly for services targeting humans, such as voice calls, multimedia services, and data services. Following the commercialization of 5th-generation (5G) communication systems, it is expected that the number of connected devices will exponentially grow. Increasingly, these will be connected to communication networks. Examples of connected things may include vehicles, robots, drones, home appliances, displays, smart sensors connected to various infrastructures, construction machines, and factory equipment. Mobile devices are expected to evolve in various form-factors, such as augmented reality glasses, virtual reality headsets, and hologram devices. In order to provide various services by connecting hundreds of billions of devices and things in the 6th-generation (6G) era, there have been ongoing efforts to develop improved 6G communication systems. For these reasons, 6G communication systems are referred to as beyond-5G systems.

[0003] 6G communication systems, which are expected to be commercialized around 2030, will have a peak data rate of tera (1,000 giga)-level bps and a radio latency less than 100μsec, and thus will be 50 times as fast as 5G communication systems and have the 1 / 10 radio latency thereof.

[0004] In order to accomplish such a high data rate and an ultra-low latency, it has been considered to implement 6G communication systems in a terahertz band (for example, 95GHz to 3THz bands). It is expected that, due to severer path loss and atmospheric absorption in the terahertz bands than those in mmWave bands introduced in 5G, technologies capable of securing the signal transmission distance (that is, coverage) will become more crucial. It is necessary to develop, as major technologies for securing the coverage, radio frequency (RF) elements, antennas, novel waveforms having a better coverage than orthogonal frequency division multiplexing (OFDM), beamforming and massive multiple input multiple output (MIMO), full dimensional MIMO (FD-MIMO), array antennas, and multiantenna transmission technologies such as large-scale antennas. In addition, there has been ongoing discussion on new technologies for improving the coverage of terahertz-band signals, such as metamaterial-based lenses and antennas, orbital angular momentum (OAM), and reconfigurable intelligent surface (RIS).

[0005] Moreover, in order to improve the spectral efficiency and the overall network performances, the following technologies have been developed for 6G communication systems: a full-duplex technology for enabling an uplink transmission and a downlink transmission to simultaneously use the same frequency resource at the same time; a network technology for utilizing satellites, high-altitude platform stations (HAPS), and the like in an integrated manner; an improved network structure for supporting mobile base stations and the like and enabling network operation optimization and automation and the like; a dynamic spectrum sharing technology via collison avoidance based on a prediction of spectrum usage; an use of artificial intelligence (AI) in wireless communication for improvement of overall network operation by utilizing AI from a designing phase for developing 6G and internalizing end-to-end AI support functions; and a next-generation distributed computing technology for overcoming the limit of UE computing ability through reachable super-high-performance communication and computing resources (such as mobile edge computing (MEC), clouds, and the like) over the network. In addition, through designing new protocols to be used in 6G communication systems, developing mecahnisms for implementing a hardware-based security environment and safe use of data, and developing technologies for maintaining privacy, attempts to strengthen the connectivity between devices, optimize the network, promote softwarization of network entities, and increase the openness of wireless communications are continuing.

[0006] It is expected that research and development of 6G communication systems in hyper-connectivity, including person to machine (P2M) as well as machine to machine (M2M), will allow the next hyper-connected experience. Particularly, it is expected that services such as truly immersive extended reality (XR), high-fidelity mobile hologram, and digital replica could be provided through 6G communication systems. In addition, services such as remote surgery for security and reliability enhancement, industrial automation, and emergency response will be provided through the 6G communication system such that the technologies could be applied in various fields such as industry, medical care, automobiles, and home appliances.

[0007] According to an embodiment of the disclosure, a method for performing Hybrid Automatic Repeat Request (HARQ) decoding in a Centralized-Radio Access Network (C-RAN) system in a wireless network is provided. The method may include receiving, by a Remote Unit (RU), a data corresponding to at least one User Equipment (UE) in an Uplink (UL) transmission. The method may include estimating, by the RU, an Effective Signal-to-Interference-plus-Noise Ratio (ESINR) value per UE for the received data. The method may include predicting, by the RU, a decoding status for the received data, based on the estimated ESINR value per UE. The method may include communicating, by the RU, the predicted decoding status to a Distributed Unit (DU) in the wireless network. The predicted decoding status may indicate whether the channel decoding operations have to be performed.

[0008] According to an embodiment of the disclosure, a Remote Unit (RU) in a wireless network is provided. The RU may include a processor, and a memory module. The processor is coupled with the memory module. The processor may be configured to receive a data corresponding to at least one UE in a UL transmission. The processor may be configured to estimate an ESINR value per UE for the received data. The processor may be configured to predict a decoding status for the received data based on the estimated ESINR value per UE. The processor may be configured to communicate the predicted decoding status to a DU in the wireless network.

[0009] These and other aspects of the example embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating example embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the example embodiments herein without departing from the spirit thereof, and the example embodiments herein include all such modifications.

[0010] Embodiments herein are illustrated in the accompanying drawings, throughout which like reference letters indicate corresponding parts in the various figures. The embodiments herein will be better understood from the following description with reference to the following drawings. Embodiments herein are illustrated by way of examples in the accompanying drawings, and in which:

[0011] FIG. 1 illustrates a Centralized-Radio Access Network (C-RAN) architecture, according to an embodiment of the disclosure;

[0012] FIG. 2 illustrates a 7.3 split architecture for uplink, according to an embodiment of the disclosure;

[0013] FIG. 3 illustrates a block diagram of a system for performing Hybrid　Automatic Repeat Request　(HARQ) decoding in a C-RAN system, according to an embodiment of the disclosure;

[0014] FIG. 4 illustrates a method for performing HARQ decoding in a C-RAN system by a Remote Unit (RU), according to an embodiment of the disclosure;

[0015] FIG. 5 illustrates a proposed split architecture for uplink, according to an embodiment of the disclosure; and

[0016] FIG. 6 illustrates a process of performing Effective Signal-to-Interference-plus-Noise Ratio (ESINR)-based Block Error Rate (BLER) prediction at RU, according to an embodiment of the disclosure.

[0017] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0018] For the purposes of interpreting this specification, the definitions (as defined herein) will apply and whenever appropriate the terms used in singular will also include the plural and vice versa. It is to be understood that the terminology used herein is for the purposes of describing particular embodiments only and is not intended to be limiting. The terms "comprising", "having" and "including" are to be construed as open-ended terms unless otherwise noted.

[0019] The words / phrases "exemplary", "example", "illustration", "in an instance", "and the like", "and so on", "etc.", "etcetera", "e.g.,", "i.e.," are merely used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein using the words / phrases "exemplary", "example", "illustration", "in an instance", "and the like", "and so on", "etc.", "etcetera", "e.g.,", "i.e.," is not necessarily to be construed as preferred or advantageous over other embodiments.

[0020] Embodiments herein may be described and illustrated in terms of blocks which carry out a described function or functions.　These blocks, which may be referred to herein as managers, units, modules, hardware components or the like, are physically implemented by analog and / or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits and the like, and may optionally be driven by a firmware. The circuits may, for example, be embodied in one or more semiconductor chips, or on substrate supports such as printed circuit boards and the like.　The circuits constituting a block may be implemented by dedicated hardware, or by a processor (e.g., one or more programmed microprocessors and associated circuitry), or by a combination of dedicated hardware to perform some functions of the block and a processor to perform other functions of the block.　Each block of the embodiments may be physically separated into two or more interacting and discrete blocks without departing from the scope of the disclosure.　Likewise, the blocks of the embodiments may be physically combined into more complex blocks without departing from the scope of the disclosure.

[0021] It should be noted that elements in the drawings are illustrated for the purposes of this description and ease of understanding and may not have necessarily been drawn to scale. For example, the flowcharts / sequence diagrams illustrate the method in terms of the steps required for understanding of aspects of the embodiments as disclosed herein. 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 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. Furthermore, in terms of the system, one or more components / modules which comprise the system 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 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.

[0022] The accompanying drawings are used to help easily understand various technical features and it should be understood that the embodiments presented herein are not limited by the accompanying drawings. As such, the disclosure should be construed to extend to any modifications, equivalents, and substitutes in addition to those which are particularly set out in the accompanying drawings and the corresponding description. Usage of words such as first, second, third etc., to describe components / elements / steps is for the purposes of this description and should not be construed as sequential ordering / placement / occurrence unless specified otherwise.

[0023] It should be appreciated that the blocks in each flowchart and combinations of the flowcharts may be performed by one or more computer programs which include computer-executable instructions. 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.

[0024] 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 central processing unit (CPU)), a communication processor (CP, e.g., a modem), a graphical processing unit (GPU), a neural processing unit (NPU) (e.g., an artificial intelligence (AI) chip), a wireless-fidelity (Wi-Fi) chip, a BluetoothTM 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 drive integrated circuit (IC), an audio CODEC chip, a universal serial bus (USB) controller, a camera controller, an image processing IC, a microprocessor unit (MPU), a system on chip (SoC), an IC, or the like.

[0025] Centralized RAN (C-RAN) systems use a network-side split architecture design to improve the cost-performance trade-off during network deployment.

[0026] FIG. 1 illustrates a Centralized-Radio Access Network (C-RAN) architecture, according to an embodiment of the disclosure.

[0027] Referring to FIG. 1, the network-side split architecture design is divided into a Centralized Unit (CU), a Distributed Unit (DU) and a Remote Unit (RU). In the 7.x approach, the likely candidate for C-RAN, physical layer processing tasks are split between the DU and the RU. For uplink transmissions, the channel estimation is carried out at the RU, whereas the channel decoding operation is handled by the DU. The CU handles the core-network functionality. The DU executes higher physical layer functions. The RU handles the lower physical layer functions. Typically, the RU location is close to antennas and the DUs are expected to be located higher in the network. Further, the CUs are more centralized and are implemented in servers using network function virtualization (NFVs).

[0028] The precise functional requirements of the DU and RU are dependent on the split architecture. The two most common designs the markets are inclined to are:

[0029] - 7.2x: Chosen by Open Radio Access Network (O-RAN) as the base version for further development.

[0030] - 7.3: The version recently preferred by network vendors.

[0031] The primary advantage of 7.3 over 7.2x is the significant reduction in the fronthaul bandwidth requirement, for a given configuration, as summarized in Table 1.

[0032]

[0033] FIG. 2 illustrates a detailed split architecture for uplink, according to an embodiment of the disclosure.

[0034] Referring to FIG. 2, the architecture indicates a 7.3 split from uplink point of view. In the case of uplink, soft bits need to be transmitted from the RU to the DU to enable demodulation. The DU performs Low density parity codes (LDPCs) decoding in both the cases: Cyclic Redundancy Check (CRC) success and CRC failure. If CRC failure is encountered, a retransmission is requested to the UE, and the LDPC repeats the decoding procedure after combining the initial and retransmission LLRs. Consequently, in the case of failures, the LDPC needs multiple decoding attempts until data is received successfully (CRC pass) or until maximum number of retransmissions are attempted. It can be observed that 7.3 multiple channel decoder attempts are needed at the DU when retransmissions are encountered. This leads to additional overhead in terms of power consumed and computations, hence is sub-optimal.

[0035] Hence, there is a need for solutions which will overcome the above mentioned drawback(s), among others.

[0036] The embodiments herein disclose methods and systems for performing efficient Hybrid　Automatic Repeat Request　(HARQ) decoding in Centralized-Radio Access Networks (C-RAN) systems based on a decoding status predicted at a Remote Unit (RU) in a wireless network. Referring now to the drawings, and more particularly to FIGS. 3 through 6, where similar reference characters denote corresponding features consistently throughout the figures, there are shown embodiments.

[0037] Embodiments herein disclose methods and systems for performing efficient HARQ decoding in C-RAN systems, based on the decoding status predicted at the RU. The channel decoding operation can be performed at a Distributed Unit (DU), and the channel estimation and an Effective Signal-to-Interference-plus-Noise Ratio (ESINR) estimation can be performed at the RU. The RU can compute the ESINR, per User Equipment (UE), corresponding to the data received in the uplink (UL) in each slot, using corresponding Demodulation Reference Signals (DMRS) channel estimates. The RU can perform a decoding status prediction for the data based on an estimated Block Error Rate (BLER). The RU can predict the decoding status using at least one ESINR estimate by comparing the ESINR to at least one threshold. The threshold may be chosen corresponding to a predetermined BLER value. The RU can predict the decoding status using at least one ESINR estimate by employing a machine learning or deep learning based approach. The predicted status for a packet in the uplink may be communicated from the RU to the DU using a 1-bit feedback, along with the data from the UE. The DU can decide on performing the channel decoding operation on the received data from the UE based on the 1-bit feedback. The DU can skip the channel decoding operation based on the RU feedback, per UE, and trigger HARQ based packet recovery procedures corresponding to the skipped UEs. The decoding status prediction at the RU and selective channel decoding at the DU can be performed for an initial and / or retransmission from a UE in the uplink.

[0038] FIG. 3 illustrates a block diagram of a system 300 for performing HARQ decoding in a C-RAN system, according to an embodiment of the disclosure.

[0039] Referring to FIG. 3, the system 300 may include a Remote Unit (RU) 302, and a base station 304 in a wireless network. The RU 302 may include a processor 306, a communication module 308, a memory module 310, an Effective Signal-to-Interference-plus-Noise Ratio (ESINR) module 312, and a decoding module 314.

[0040] In an embodiment herein, the processor 306 can perform the ESINR computation and / or prediction after each Physical Uplink Shared Channel (PUSCH) reception. The processor 306 can predict a Cyclic Redundancy Check (CRC) outcome from the ESINR calculation. After PUSCH processing, the processor 306 of the RU 302 may share soft Log-Likelihood Ratio (LLR) bit-widths, and an additional CRC success indication bit to a Distributed Unit (DU) of the wireless network. After LLR bit-width reception from the processor 306 of the RU 302, the DU can perform channel decoding and CRC check, only if 1-bit indication from the RU 302 is set to proceed. For LLR transfer from the RU 302 to the DU, the LLR bit-width may be chosen as a function of a Signal-to-Interference-plus-Noise Ratio (SINR) and / or ESINR of the received Uplink (UL) packet.

[0041] In an embodiment herein, the ESINR module 312 can receive a data corresponding to at least one UE in a UL transmission from at least one base station 304. The ESINR module 312 can estimate an ESINR value per UE for the received data. The ESINR module 312 can estimate the ESINR value per UE by combining one or more SINR values using at least one computation technique. The computation technique can be selected from any known techniques such as, but not limited to, an Exponential Effective SINR Mapping (EESM), a Received Bit Information Rate (RBIR), and Mean Mutual Information per Bit (MMIB).

[0042] In an embodiment herein, the decoding module 314 can predict a decoding status for the received data, based on the estimated ESINR value per UE. The decoding module 314 can communicate the predicted decoding status to the DU in the wireless network. The predicted decoding status may indicate whether the channel decoding operations have to be performed or not. The predicted decoding status may indicate one of a CRC-success, and a CRC-failure for indicating if the data is decodable by the DU.

[0043] In an embodiment herein, the decoding module 314 can predict the decoding status using one or more training predictors from at least one of at least one SINR value, and the ESINR value per UE. The training predictors can include one or more deep learning based models.

[0044] In an embodiment herein, the decoding module 314 can compare the ESINR value per UE to a threshold value. The decoding module 314 can predict the decoding status for the received data, based on the compared ESINR value.

[0045] In an embodiment herein, the decoding module 314 can compute an expected Block Error Rate (BLER) value from the estimated ESINR value, through mapping using a lookup table. The decoding module 314 can compare the expected BLER value to a threshold value. The decoding module 314 can predict the decoding status for the received data, based on the compared expected BLER value.

[0046] In an embodiment herein, the decoding module 314 can communicate the predicted decoding status to the DU through a 1-bit information flag. The decoding module 314 can set the 1-bit information flag as proceed for indicating the DU to execute the channel decoding operation. The decoding module 314 can set the 1-bit information flag as hold for indicating the DU to skip the channel decoding operation and store the LLR for future decoding.

[0047] In an embodiment herein, the decoding module 314 can communicate to the DU one or more Log-Likelihood Ratios (LLR) with different bit-widths according to mutual information requirements for bandwidth optimization. The decoding module 314 can select a lower LLR bit-width for performing an LLR transmission from the RU 302 to the DU, if the ESINR value depicts good signal conditions. The decoding module 314 can select a higher LLR bit-width for performing the LLR transmission from the RU 302 to the DU, if the ESINR value depicts poor signal conditions.

[0048] In an embodiment herein, the processor 306 can process and execute data of a plurality of modules of the RU 302. The processor 306 can be configured to execute instructions stored in the memory module 310. The processor 306 may comprise one or more of microprocessors, circuits, and other hardware configured for processing. The processor 306 can be at least one of a single processer, a plurality of processors, multiple homogeneous or heterogeneous cores, multiple Central Processing Units (CPUs) of different kinds, microcontrollers, special media, and other accelerators. The processor 306 may be an application processor (AP), a graphics-only processing unit (such as a graphics processing unit (GPU), a visual processing unit (VPU)), and / or an Artificial Intelligence (AI)-dedicated processor (such as a neural processing unit (NPU)).

[0049] In an embodiment herein, the plurality of modules of the processor 306 of the RU 302 can communicate via the communication module 308. The communication module 308 may be in the form of either a wired network or a wireless communication network module. The wireless communication network may comprise, but not limited to, Global Positioning System (GPS), Global System for Mobile Communications (GSM), Wi-Fi, Bluetooth low energy, Near-field communication (NFC), and so on. The wireless communication may further comprise one or more of Bluetooth, ZigBee, a short-range wireless communication (such as Ultra-Wideband (UWB)), and a medium-range wireless communication (such as Wi-Fi) or a long-range wireless communication (such as 3G / 4G / 5G / 6G and non-3GPP technologies or WiMAX), according to the usage environment.

[0050] In an embodiment herein, the memory module 310 may comprise one or more volatile and non-volatile memory components which are capable of storing data and instructions of the modules of the RU 302 to be executed. Examples of the memory module 310 can be, but not limited to, NAND, embedded Multi Media Card (eMMC), Secure Digital (SD) cards, Universal Serial Bus (USB), Serial Advanced Technology Attachment (SATA), solid-state drive (SSD), and so on. The memory module 310 may also include one or more computer-readable storage media. Examples of non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory module 310 may, in some examples, be considered a non-transitory storage medium. The term "non-transitory" may indicate that the storage medium is not embodied in a carrier wave or a propagated signal. However, the term "non-transitory" should not be interpreted to mean that the memory module 310 is non-movable. In certain examples, a non-transitory storage medium may store data that can, over time, change (for example, in Random Access Memory (RAM) or cache).

[0051] FIG. 3 shows example modules of the RU 302, but it is to be understood that other embodiments are not limited thereon. In an embodiment, the RU 302 may include less or more number of modules. Further, the labels or names of the modules are used only for illustrative purpose and does not limit the scope of the disclosure. One or more modules can be combined together to perform same or substantially similar function in the RU 302.

[0052] FIG. 4 illustrates a method 400 for performing HARQ decoding in a C-RAN system by the RU 302, according to an embodiment of the disclosure.

[0053] Referring to FIG. 4, the method 400 may include receiving a data corresponding to at least one UE in a UL transmission, as illustrated in operation 402. The method 400 may include estimating an ESINR value per UE for the received data, as illustrated in operation 404. The method 400 may include predicting a decoding status for the received data, based on the estimated ESINR value per UE, as illustrated in operation 406. The method 400 may include communicating the predicted decoding status to the DU in the wireless network, as illustrated in operation 408. The predicted decoding status may indicate whether the channel decoding operations have to be performed or not.

[0054] The various operations in method 400 may be performed in the order presented, in a different order or simultaneously. Further, in an embodiment, some operations illustrated in FIG. 4 may be omitted.

[0055] FIG. 5 illustrates a proposed split architecture for uplink, according to an embodiment of the disclosure.

[0056] ESINR based transmission detection:

[0057] Referring to FIG. 5, an additional ESINR computation operation can be performed at the RU 302 to compute the ESINR to predict the CRC success for the data. Effective SINR (ESINR) computation may be useful in estimating the BLER of the received symbols. The process may involve estimating the ESINR of the equalized symbols, channel and noise estimates to determine the probability of failure. In this case, the expected BLER on the received symbols could be estimated as follows:

[0058] Operation 1: Perform channel estimation using a received PUSCH DMRS

[0059] Operation 2: Estimate the noise

[0060] Operation 3: Compute the post-equalization SINR, per tone, depending on the receiver used

[0061] Operation 4: Combine the per-tone SINR to compute the effective SINR .

[0062] The ESINR operation mentioned in Operation 4 may be performed using one of the standard ESINR computation techniques such as Mutual Information Effective SINR Mapping (MIESM), EESM, RBIR, and so on. Among the operations mentioned above, operations 1-3 may be performed by default, and only operation 4 should be executed for ESNR computation. Further, this may incur minimal complexity.

[0063] Predict data reception decision:

[0064] Decision logic for CRC success prediction:

[0065] The RU 302 can generate an estimate on whether the data is decodable or not by the DU, using the computed ESINR. Once the ESINR is computed, the corresponding BLER for the received transmission may be estimated using a Look Up Table (LUT). The estimated BLER may be as given below:

[0066]

[0067] The method chosen for ESINR and BLER computation may vary depending on whether the received data belongs to the initial transmission or a re-transmission.

[0068] Decision logic:

[0069] Using the BLER estimate , and comparing the same to a predetermined threshold, the RU 302 may generate an estimate of CRC-success or CRC-failure for the received data. Alternatively, the RU 302 may perform the same comparison in the ESNR domain, by comparing the ESINR value per UE against an ESNR threshold. Further, an additional predictor such as a deep-learning based network that takes SINR and ESINR as inputs, can be used for effective and accurate prediction.

[0070] 1-bit indication from RU to DU:

[0071] 1-bit indication on decodability:

[0072] A 1-bit indication can be provided from the RU 302 to the DU. In an embodiment herein, the RU 302 can share an additional 1-bit indication to the DU to indicate if the DU should proceed or hold on the channel decoding operation. In an embodiment herein, if the indication is set to proceed, then the DU can execute channel decoding. Else, if the indication is set to hold, then the DU can skip the channel decoding, and save the LLRs for future decoding.

[0073] After generating the LLRs, the RU 302 can share them with the DU for channel decoding operation. In addition, the CRC success prediction decision may be shared by the RU 302 to the DU to indicate whether to hold or to proceed with the channel decoding operation. During the reception of LLRs at the DU, if the 1-bit indication is set to proceed, then the DU can execute the decoding operation. If the data is decoded successfully, then the LLRs may be discarded, or else the LLRs may be stored for further usage during the data retransmission. During the LLRs are received, if the 1-bit is set to hold, then the DU can only store the LLRs to use them during the retransmission and can skip the channel decoding to save power.

[0074] FIG. 6 illustrates a process of performing ESINR-based BLER prediction at the RU 302, according to an embodiment of the disclosure.

[0075] Referring to FIG. 6, ESINR can be calculated based on the channel estimate values. The channel estimates may be available at the RU 302.

[0076] The channel estimation is as follows:

[0077] : Channel estimate on RE of size

[0078] = Number of Rx Digital ports

[0079] = Number of Tx layers

[0080] There are several methods to calculate ESINR from channel estimates. Few of them are:

[0081] - EESM (Exponential effective SINR Mapping)

[0082] - RBIR (Received Bit Information Rate)

[0083] - MMIB (Mean Mutual Information per bit)

[0084] For example, in case of EESM, the ESINR may be computed using the below relation:

[0085]

[0086] where may denote the effective SINR, may denote the coefficient of scaling, may denote the number of individual SINR values for combining and may denote the SINR of the SINR component

[0087] Once, the ESINR is calculated (using one of the above 3 methods), the ESINR can be mapped to appropriate BLER value.

[0088] The RU 302 may perform ESINR computation after each PUSCH reception. The CRC outcome prediction may be generated at the RU 302. After PUSCH processing, the RU 302 may share the soft LLR bits and an additional CRC success indication bit to the DU. After LLR reception from the RU 302, the DU may perform channel decoding and CRC check, only if 1-bit indication from the RU 302 is set to proceed. Typically, 10% of the received packets fail at the DU after reception. Using the proposed approach, in such instances, the failure may be detected in advance by the RU 302, hence the channel decoding and CRC check operations may be reduced at the DU. Thus, the proposed approach may improve power saving and complexity at the DU.

[0089] ESINR based LLR bit-width selection:

[0090] The LLR bit-width may be selected for transmitting the information from the RU 302 to the DU as a function of the mutual information requirement for successful decoding. When the ESINR is good, for example have better signal conditions, then a lower LLR bit-width may be selected for RU 302 to DU transmission, and vice-versa.

[0091] For the given received payload in the UL, RU may compute the ideal mutual information (MI), depending on the BLER target requirement. Similarly, using the ESINR, the RU 302 may compute the MI, , of the received payload. RU 302 computes the difference as,

[0092]

[0093] Let be the difference thresholds, and let denote the LLR bit-width supported for RU 302 to DU transmission. The RU 302 selects the bit-width as follows:

[0094]

[0095]

[0096]

[0097] Therefore, the RU 302 may select the bit-width for LLR transmission from RU 302 to DU as a function of ESINR and SINR of the received payload in the uplink. In good signal conditions characterized by smaller higher bit-widths may be chosen, and vice-versa.

[0098] The methods may provide estimation of the ESINR at the RU 302 for prediction of the packet decoding status, transfer of the prediction status to the DU through a 1-bit information, execution of channel decoding operation at the DU as a function of the received 1-bit prediction status, and selection of LLR transfer bit-width as a function of the packet SINR or ESINR.

[0099] Thus, the proposed methods may provide HARQ status decoding and retransmissions as part of uplink data transmissions in an Open Radio Access Network (ORAN) setup. The methods 400 may provide a low complexity extension to compute the ESINR for predicting the packet decoding status and to decide the bit-width of LLR for transfer from the RU 302 to the DU. This, may reduce the computational complexity and the operating bandwidth, therefore, improving the operating efficiency.

[0100] The embodiments disclosed herein can be implemented through at least one software program running on at least one hardware device and performing network management functions to control the network elements. The network elements shown in FIG. 3 include blocks which can be at least one of a hardware device, or a combination of hardware device and software module.

[0101] The embodiment disclosed herein describes methods 400 and systems 300 for performing efficient HARQ decoding in C-RAN systems at a Centralized Unit (CU) based on the decoding status predicted at the RU 302. Therefore, it is understood that the scope of the protection is extended to such a program and in addition to a computer readable means having a message therein, such computer readable storage means contain program code means for implementation of one or more steps of the method, when the program runs on a server or mobile deviceor any suitable programmable device. The method is implemented in at least one embodiment through or together with a software program written in e.g., Very high speed integrated circuit Hardware Description Language (VHDL) another programming language, or implemented by one or more VHDL or several software modules being executed on at least one hardware device. The hardware device can be any kind of portable device that can be programmed. The device may also include means which could be e.g., hardware means like e.g., an ASIC, or a combination of hardware and software means, e.g., an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. The method embodiments described herein could be implemented partly in hardware and partly in software. Alternatively, the embodiment of the disclosure may be implemented on different hardware devices, e.g., using a plurality of CPUs.

[0102] According to an embodiment of the disclosure, methods and systems for performing an efficient Hybrid　Automatic Repeat Request　(HARQ) decoding in Centralized-Radio Access Networks (C-RAN) systems are provided.

[0103] According to an embodiment of the disclosure, methods and systems for predicting a decoding status for a data at a Remote Unit (RU) in wireless network are provided.

[0104] According to an embodiment of the disclosure, a 1-bit feedback mechanism from the RU to a Distributed Unit (DU) to improve the power efficiency of operations at the DU is provided, wherein the RU predicts if a data packet received in the uplink from a UE is decodable or not using an Effective Signal-to-Interference-plus-Noise Ratio (ESINR) approach and intimates the same to the DU using 1-bit feedback.

[0105] According to an embodiment of the disclosure, methods and systems for enabling the DU to proceed with a channel decoding operation or to skip the channel decoding operation and / or trigger HARQ retransmission to the UE, based on the indication received from the RU are provided.

[0106] In an embodiment, a method performed by a Remote Unit (RU) in a wireless communication system is provided. The method may include receiving a data from at least one User Equipment (UE) in an Uplink (UL) transmission. The method may include estimating an Effective Signal-to-Interference-plus-Noise Ratio (ESINR) value per UE for the received data. The method may include predicting a decoding status for the received data, based on the estimated ESINR value per UE. The method may include transmitting the predicted decoding status to a Distributed Unit (DU) in the wireless communication system. The method, wherein the predicted decoding status may indicate whether channel decoding operations have to be performed.

[0107] In an embodiment, the method, wherein the RU may estimate the ESINR value per UE by combining one or more SINR values using at least one computation technique. The method, wherein the at least one computation technique may include at least one of an Exponential Effective SINR Mapping (EESM), a Received Bit Information Rate (RBIR), Mean Mutual Information per Bit (MMIB), or Mutual Information Effective SINR Mapping (MIESM).

[0108] In an embodiment, the method, wherein the predicted decoding status indicates one of a Cyclic Redundancy Check (CRC)-success or a CRC-failure.

[0109] In an embodiment, the method, wherein the RU may predict the decoding status using one or more training predictors from at least one of at least one SINR value, and the ESINR value per UE. The method, wherein the one or more training predictors may include one or more deep learning based models.

[0110] In an embodiment, the method, wherein predicting the decoding status may include comparing the ESINR value per UE to a threshold value. The method, wherein predicting the decoding status may include predicting the decoding status for the received data, based on the compared ESINR value.

[0111] In an embodiment, the method, wherein predicting the decoding status may include computing an expected Block Error Rate (BLER) value from the estimated ESINR value, through mapping using a lookup table. The method, wherein predicting the decoding status may include comparing the expected BLER value to a threshold value. The method, wherein predicting the decoding status may include predicting the decoding status for the received data, based on the compared expected BLER value.

[0112] In an embodiment, the method, wherein the RU may transmit the predicted decoding status to the DU through a 1-bit information.

[0113] In an embodiment, the method may include at least one of: setting the 1-bit information as proceed for indicating the DU to execute the channel decoding operation, and setting the 1-bit information as hold for indicating the DU to skip the channel decoding operation and store a Log-Likelihood Ratio (LLR).

[0114] In an embodiment, the method, wherein the RU may transmit to the DU one or more Log-Likelihood Ratios (LLR) with different bit-widths according to mutual information requirements.

[0115] In an embodiment, the method may include at least one of: selecting a lower LLR bit-width for performing an LLR transmission from the RU to the DU, if the ESINR value depicts good signal conditions; and selecting a higher LLR bit-width for performing the LLR transmission from the RU to the DU, if the ESINR value depicts poor signal conditions.

[0116] In an embodiment, a Remote Unit (RU) in a wireless communication system is provided. The RU may include at least one processor. The at least one processor may be configured to receive a data from at least one User Equipment (UE) in an Uplink (UL) transmission. The at least one processor may be configured to estimate an Effective Signal-to-Interference-plus-Noise Ratio (ESINR) value per UE for the received data. The at least one processor may be configured to predict a decoding status for the received data, based on the estimated ESINR value per UE. The at least one processor may be configured to transmit the predicted decoding status to a Distributed Unit (DU) in the wireless communication system, wherein the predicted decoding status may indicate whether channel decoding operations have to be performed.

[0117] In an embodiment, the RU, wherein the RU may estimate the ESINR value per UE by combining one or more SINR values using at least one computation technique. The RU, wherein the at least one computation technique may include at least one of an Exponential Effective SINR Mapping (EESM), a Received Bit Information Rate (RBIR), Mean Mutual Information per Bit (MMIB), or Mutual Information Effective SINR Mapping (MIESM).

[0118] In an embodiment, the RU, wherein the predicted decoding status may indicate one of a Cyclic Redundancy Check (CRC)-success or a CRC-failure.

[0119] In an embodiment, the RU, wherein the at least one processor may be configured to predict the decoding status using one or more training predictors from at least one of at least one SINR value, and the ESINR value per UE, wherein the one or more training predictors may include one or more deep learning based models.

[0120] In an embodiment, the RU, wherein the at least one processor may be configured to compare the ESINR value per UE to a threshold value. The at least one processor may be configured to predict the decoding status for the received data, based on the compared ESINR value.

[0121] In an embodiment, the RU, wherein the at least one processor may be configured to compute an expected Block Error Rate (BLER) value from the estimated ESINR value, through mapping using a lookup table. The at least one processor may be configured to compare the expected BLER value to a threshold value. The at least one processor may be configured to predict the decoding status for the received data, based on the compared expected BLER value.

[0122] In an embodiment, the RU, wherein the RU may transmit the predicted decoding status to the DU through a 1-bit information.

[0123] In an embodiment, the RU, wherein the at least one processor may be configured to perform at least one of: setting the 1-bit information as proceed for indicating the DU to execute the channel decoding operation; and setting the 1-bit information as hold for indicating the DU to skip the channel decoding operation and store a Log-Likelihood Ratio (LLR).

[0124] In an embodiment, the RU, wherein the RU may transmit to the DU one or more Log-Likelihood Ratios (LLR) with different bit-widths according to mutual information requirements.

[0125] In an embodiment, the RU, wherein the at least one processor may be configured to perform at least one of: selecting a lower LLR bit-width for performing an LLR transmission from the RU to the DU, if the ESINR value depicts good signal conditions; and selecting a higher LLR bit-width for performing the LLR transmission from the RU to the DU, if the ESINR value depicts poor signal conditions.

[0126] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and / or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of embodiments and examples, those skilled in the art will recognize that the embodiments and examples disclosed herein can be practiced with modification within the scope of the embodiments as described herein.

Claims

1.A method performed by a Remote Unit (RU) in a wireless communication system, the method comprising:receiving a data from at least one User Equipment (UE) in an Uplink (UL) transmission;estimating an Effective Signal-to-Interference-plus-Noise Ratio (ESINR) value per UE for the received data;predicting a decoding status for the received data, based on the estimated ESINR value per UE; andtransmitting the predicted decoding status to a Distributed Unit (DU) in the wireless communication system, wherein the predicted decoding status indicates whether channel decoding operations have to be performed.2.The method of claim 1, wherein the RU estimates the ESINR value per UE by combining one or more SINR values using at least one computation technique, andwherein the at least one computation technique comprises at least one of an Exponential Effective SINR Mapping (EESM), a Received Bit Information Rate (RBIR), Mean Mutual Information per Bit (MMIB), or Mutual Information Effective SINR Mapping (MIESM).3.The method of claim 1, wherein the predicted decoding status indicates one of a Cyclic Redundancy Check (CRC)-success or a CRC-failure.4.The method of claim 1, wherein predicting the decoding status comprises:comparing the ESINR value per UE to a threshold value; andpredicting the decoding status for the received data, based on the compared ESINR value.5.The method of claim 1, wherein predicting the decoding status comprises:computing an expected Block Error Rate (BLER) value from the estimated ESINR value, through mapping using a lookup table;comparing the expected BLER value to a threshold value; andpredicting the decoding status for the received data, based on the compared expected BLER value.6.The method of claim 1, wherein the RU transmits the predicted decoding status to the DU through a 1-bit information, andwherein the method comprises at least one of:setting the 1-bit information as proceed for indicating the DU to execute the channel decoding operation; andsetting the 1-bit information as hold for indicating the DU to skip the channel decoding operation and store a Log-Likelihood Ratio (LLR).7.The method of claim 1, wherein the RU transmits to the DU one or more Log-Likelihood Ratios (LLR) with different bit-widths according to mutual information requirements.8.A Remote Unit (RU) in a wireless communication system, the RU comprising:at least one processor, wherein the at least one processor is configured to:receive a data from at least one User Equipment (UE) in an Uplink (UL) transmission;estimate an Effective Signal-to-Interference-plus-Noise Ratio (ESINR) value per UE for the received data;predict a decoding status for the received data, based on the estimated ESINR value per UE; andtransmit the predicted decoding status to a Distributed Unit (DU) in the wireless communication system, wherein the predicted decoding status indicates whether channel decoding operations have to be performed.9.The RU of claim 8, wherein the RU estimates the ESINR value per UE by combining one or more SINR values using at least one computation technique, andwherein the at least one computation technique comprises at least one of an Exponential Effective SINR Mapping (EESM), a Received Bit Information Rate (RBIR), Mean Mutual Information per Bit (MMIB), or Mutual Information Effective SINR Mapping (MIESM).10.The RU of claim 8, wherein the predicted decoding status indicates one of a Cyclic Redundancy Check (CRC)-success or a CRC-failure.11.The RU of claim 8, wherein the at least one processor is configured to:compare the ESINR value per UE to a threshold value; andpredict the decoding status for the received data, based on the compared ESINR value.12.The RU of claim 8, wherein the at least one processor is configured to:compute an expected Block Error Rate (BLER) value from the estimated ESINR value, through mapping using a lookup table;compare the expected BLER value to a threshold value; andpredict the decoding status for the received data, based on the compared expected BLER value.13.The RU of claim 8, wherein the RU transmits the predicted decoding status to the DU through a 1-bit information.14.The RU of claim 13, wherein the at least one processor is configured to perform at least one of:setting the 1-bit information as proceed for indicating the DU to execute the channel decoding operation; andsetting the 1-bit information as hold for indicating the DU to skip the channel decoding operation and store a Log-Likelihood Ratio (LLR).15.The RU of claim 8, wherein the RU transmits to the DU one or more Log-Likelihood Ratios (LLR) with different bit-widths according to mutual information requirements.

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