Protocol Data Unit (PDU) Error Probability Feedback

Abstract

Systems, methods, apparatuses, and computer program products for error probability feedback are provided. A method may include: sending a configuration for protocol data unit error probability calculation and reporting to at least one user equipment. The method may further include receiving feedback related to the protocol data unit error probability from at least one user equipment.

Description

Technical Field

[0001] Some example embodiments may generally relate to mobile or wireless communication systems, such as Long Term Evolution (LTE) or Fifth Generation (5G) radio access technology or New Radio (NR) access technology, or other communication systems. For example, a particular embodiment may relate to a system and / or method for error probability feedback. Background Art

[0002] Examples of mobile or wireless communication systems may include Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access Network (UTRAN), Long Term Evolution (LTE) Evolved UTRAN (E-UTRAN), Advanced LTE (LTE-A), Multefire, LTE-A Pro, and / or Fifth Generation (5G) radio access technology or New Radio (NR) access technology. The 5G wireless system refers to the next generation (NG) of radio systems and network architectures. The 5G system is mainly built on 5G New Radio (NR), but 5G (or NG) networks can also be built on E-UTRA radio. It is estimated that NR provides a bit rate of about 10 - 20 Gbit / s or higher and can support at least service categories such as enhanced mobile broadband (eMBB) and ultra-reliable low latency communication (URLLC) as well as massive machine type communication (mMTC). It is expected that NR delivers extreme broadband and super robust, low latency connections and a large number of networkings to support the Internet of Things (IOT). As IOT and machine-to-machine (M2M) communications become more and more popular, the demand for networks that meet lower power, low data rate, and long battery life will grow. The Next Generation Radio Access Network (NG-RAN) represents the RAN for 5G, which can provide NR and LTE (and Advanced LTE) radio access. Note that in 5G, a node that can provide radio access functions to a user equipment (i.e., similar to Node B, NB in UTRAN or evolved NB, eNB in LTE), when built on NR radio, can be called a Next Generation NB (gNB), and when built on E-UTRA radio, can be called a Next Generation eNB (NG-eNB). Summary of the Invention

[0003] One embodiment relates to an apparatus that may include at least one processor and at least one memory including computer program code. The at least one memory and the computer program code are configured to, together with the at least one processor, cause the apparatus to at least send, to at least one user equipment, a configuration for protocol data unit error probability calculation and reporting, and receive, from at least one user equipment, feedback related to the protocol data unit error probability.

[0004] Another embodiment relates to a method that may include sending a configuration for protocol data unit error probability calculation and reporting to at least one user equipment, and receiving feedback related to the protocol data unit error probability from at least one user equipment.

[0005] Another embodiment relates to a device that may include: means for sending a configuration for protocol data unit error probability calculation and reporting to at least one user equipment; and means for receiving feedback related to the protocol data unit error probability from at least one user equipment.

[0006] Another embodiment relates to a device that may include at least one processor and at least one memory including computer program code. The at least one memory and the computer program code are configured to, together with the at least one processor, cause the device to at least receive from a network node a configuration for protocol data unit error probability calculation and reporting for at least one network layer to enable recording and estimation of block error probability, calculate the protocol data unit error probability for at least one network layer based on the received configuration and the block error probability estimation, and send feedback related to the protocol data unit error probability to the network node.

[0007] Another embodiment relates to a method that may include receiving, at a user equipment, a configuration from a network node, where the configuration is for protocol data unit error probability calculation and reporting for at least one network layer. The method may further include enabling recording and estimation of block error probability, calculating the protocol data unit error probability for at least one network layer based on the received configuration and the block error probability estimation, and sending feedback related to the protocol data unit error probability to the network node.

[0008] Another embodiment relates to a device that may include: means for receiving from a network node a configuration for protocol data unit error probability calculation and reporting for at least one network layer; means for enabling recording and estimation of block error probability; means for calculating the protocol data unit error probability for at least one network layer based on the received configuration and the block error probability estimation; and means for sending feedback related to the protocol data unit error probability to the network node. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] For a proper understanding of the exemplary embodiments, reference should be made to the drawings, in which:

[0010] Figure 1A An exemplary signaling diagram according to an embodiment is illustrated;

[0011] Figure 1B An exemplary signaling diagram according to an embodiment is illustrated;

[0012] Figure 1CIllustrates an exemplary signaling diagram according to an embodiment;

[0013] Figure 1D Illustrates an exemplary signaling diagram according to an embodiment;

[0014] Figure 2 Illustrates an example of the mapping of PDU from MAC to SDAP according to an embodiment, and includes the PHY layer;

[0015] Figure 3 Illustrates an example of PDCP duplication according to an embodiment;

[0016] Figure 4A Illustrates an example table of action classification based on PDCP-PDU-EP feedback according to an embodiment;

[0017] Figure 4B Illustrates a table of examples describing classification by considering reliability and latency targets according to an embodiment;

[0018] Figure 5A Illustrates an exemplary flowchart of a method according to an embodiment;

[0019] Figure 5B Illustrates an exemplary flowchart of a method according to an embodiment;

[0020] Figure 6A Illustrates an exemplary block diagram of a device according to an embodiment; and

[0021] Figure 6B Illustrates an exemplary block diagram of a device according to an embodiment. Detailed Description of the Invention

[0022] It will be readily understood that the components of the specific example embodiments generally described and illustrated in the figures herein can be arranged and designed in a variety of different configurations. Accordingly, the following detailed description of some example embodiments of a system, method, device, and computer program product for error probability feedback is not intended to limit the scope of the specific embodiments, but rather represents selected exemplary embodiments.

[0023] The features, structures, or characteristics of the example embodiments described throughout this specification may be combined in any suitable manner in one or more example embodiments. For example, the use of phrases such as "specific embodiments", "some embodiments", or other similar language throughout this specification refers to the fact that the specific features, structures, or characteristics described in connection with the embodiments may be included in at least one embodiment. Thus, the appearances of phrases such as "in a specific embodiment", "in some embodiments", "in other embodiments", or other similar language throughout this specification do not necessarily all refer to the same set of embodiments, and the described features, structures, or characteristics may be combined in any suitable manner in one or more example embodiments.

[0024] Additionally, if desired, the different functions or processes discussed below may be performed in different orders and / or concurrently with each other. Further, if desired, one or more of the described functions or processes may be optional or may be combined. Accordingly, the following description should be regarded as illustrative of the principles and teachings of a particular example embodiment, and not in limitation thereof.

[0025] Certain embodiments may relate to optimizing and / or improving URLLC, for example, by introducing new radio feedback to measure the reliability of radio links. URLLC is a relatively new area of wireless communication and is considered an important pillar in fifth-generation (5G) cellular networks (other cellular networks include enhanced mobile broadband and massive machine-type communication). URLLC potentially opens up a variety of new use cases for wireless networks in various vertical domains (such as, but not limited to, industrial automation or e-health). Dedicated and virtual private networks supporting 5G vertical use cases are expected to be a promising growth area in the wireless industry.

[0026] One challenge associated with URLLC is that URLLC sets new, more stringent optimization criteria for network deployment. For example, the network should support ultra-high reliability (such as 99.999% or higher), and in some cases, may also support extremely low end-to-end latency and jitter (such as, a guaranteed deterministic latency of 1 ms and microsecond-level jitter). While the particular embodiments described herein are applicable to 5G URLLC use cases and may be described in conjunction with 5G URLLC use cases, it should be noted that some embodiments are also applicable to other 5G use cases (such as, eMBB and mMTC), as well as other wireless technologies (such as, industrial Wi-Fi).

[0027] In a radio network, the measurement and evaluation of the service level requirements of URLLC applications are sometimes difficult tasks. As an example, for an application that sends packets every 100 ms and has a packet error probability target of 10^-7, an error may occur on average once every 28 hours. To verify this, measurements from a longer period are needed to have a statistically meaningful sample set. In addition, network optimization usually involves iterations, resulting in a longer optimization time for URLLC. For real-time optimization (e.g., for radio resource management), the measurement interval should be significantly shorter (on the order of dozens or hundreds of milliseconds). For non-real-time optimization (e.g., self-organizing networks), longer measurement cycles can be tolerated, but an optimization cycle of several days may be problematic because it may take an unreasonably long time before the network stabilizes to an optimal state (or if the radio environment changes, the algorithm may not find any stable optimal value). In addition, during optimization, normal operation may not be possible because it may degrade the performance of critical URLLC applications. Therefore, a problem may involve how to shorten the very long time required to collect application failure or packet error probability statistics in the URLLC usage scenario.

[0028] Another problem involves the feasibility of using existing radio feedback for radio link performance optimization. URLLC applications typically have QoS requirements related to the packet error probability; for example, each packet arriving at the gNB should be transmitted with a certain probability within a certain delay, or only a certain amount of consecutive packet errors are allowed within a certain time window. Alternatively, the requirement may be related to an error probability target on some radio layer such as the Packet Data Convergence Protocol (PDCP).

[0029] When a packet arrives at the gNB, it is passed to the lower layer; at each layer, the data from it can be divided into one or more segments, which are in turn passed to the lower layer. When the packet arrives at the Physical Layer (PHY), the bits of the packet may already have been divided into multiple code blocks, which are sent on different physical radio resource blocks, and each physical radio resource block may potentially have decorrelated radio channel conditions. Existing methods are to adjust the Block Error Probability (BLEP) to a specific percentage with the help of Channel Quality Indicator (CQI) reports, which have been proven to be a reasonable method for optimizing MBB-type services, for example. However, since BLEP or CQI is not fully related to the above URLLC requirements, another problem involves how to provide a better appropriate feedback mechanism to optimize the performance requirements on the physical layer, such as the Quality of Service (QoS) of URLLC. Therefore, the most important problem involves how to estimate the performance (near real-time) on the layers above the physical layer without collecting a large amount of measurements.

[0030] Certain embodiments provide methods capable of pointing out and solving at least the above problems.

[0031] For example, certain embodiments provide a new feedback mechanism for a transmitter to obtain an error probability estimate of a protocol data unit (PDU) at any radio layer. This new key performance indicator (KPI) may be referred to as PDU error probability (PDU-EP) or Lx-pdu-EP, where Lx refers to the layer at which the estimate is calculated (e.g., between the PHY and the service data adaptation protocol (SDAP)). In one embodiment, the receiver may track which code blocks (CBs) contribute to the transmission of the Lx-PDU and may calculate the PDU-EP based on the BLEP estimate of the decoded user plane CBs.

[0032] According to some embodiments, the BLEP estimate may be calculated at the receiver (e.g., UE) based on a mutual information (MI)-based link-to-system mapping (e.g., mutual information effective signal-to-noise ratio (SNR) mapping (MIESM)). For example, considering the actual modulation and coding scheme (MCS) and code block size, as well as the effective signal-to-interference-and-noise ratio (SINR) after decoding, the BLEP may be calculated from the successfully received user plane data (e.g., CBs contributing to the transmission of the Lx-pdu). In some embodiments, the exact method for the BLEP estimate may be left to the receiver implementation or may be defined by a standard.

[0033] Figure 1A , Figure 1B , Figure 1C and Figure 1D illustrates an exemplary signaling diagram according to various example embodiments. It should be noted that Figures 1A to 1D these are only some examples, and according to specific embodiments, other examples are possible.

[0034] Figure 1A illustrates an example signaling diagram according to an example embodiment. Note that Figure 1A the example of Figure 1B illustrates a signaling between a first network element (network element 1) and a second network element (network element 2). According to some embodiments, network element 1 may represent a base station, such as a gNB or an eNB, or may represent a UE. Additionally, in a specific embodiment, network element 2 may represent a UE. Figure 1C illustrates another example signaling diagram depicting a downlink scenario, where network element 1 is represented by gNB1 and network element 2 is represented by a UE. Figure 1D illustrates yet another example signaling diagram depicting an uplink scenario, where network element 1 is represented by UE1 and network element 2 is represented by UE2.

[0035] AsFigure 1A , Figure 1B and Figure 1C As shown in the example of Figure 1C , at 101, network element 1 (or gNB1 or UE1) may send a configuration to network element 2 (or UE or UE2). In one embodiment, the configuration may include information on PDU-EP calculation and reporting for one or more specific network layers (Lx). For example, in some embodiments, the configuration provided by network element 1 (or gNB1 or UE1) may be a measurement configuration, which may include information on one or more layers for PDU-EP calculation, one or more specific channels for calculating PDU-EP, such as QoS flows, radio bearers, and / or branches (e.g., radio link control (RLC) entities). According to an embodiment, the configuration may be provided by network element 1 (or gNB1 or UE1) to network element 2 (or UE or UE2) via radio resource control (RRC).

[0036] As Figures 1A to 1D further shown in the example of Figures 1A to 1D , at 102, recording and estimation of BLEP may be enabled at network element 2. For example, in response to receiving the configuration, network element 2 (or UE or UE2) may enable BLEP estimation for the user plane CB and record the BLEP value. In addition, in one embodiment, network element 2 (or UE or UE2) may record how the received CB is mapped to the upper layer PDU at (multiple) network layers Lx. In some embodiments, as Figures 1A to 1D shown, at 103, network element 2 (or UE or UE2) may receive one or more PDU transmissions.

[0037] According to a specific embodiment, at 104, network element 2 (or UE or UE2) may calculate the PDU-EP for (multiple) network layers Lx. For example, the calculation of PDU-EP may include: when network element 2 (or UE or UE2) decodes a user plane PDU that matches the received configuration, calculating the PDU-EP for (multiple) network layers Lx based on the CBs that contribute to the transmission of the PDU, using the recorded BLEP values and the mapping of CBs to PDUs. In an embodiment, at 105, network element 2 (or UE or UE2) may report feedback related to the PDU-EP to network element 1 (or gNB1 or UE1).

[0038] According to some embodiments, the network element 2 (or UE or UE2) may report feedback periodically, for example, based on a fixed time interval or the number of PDUs of a specific layer Lx received. In another embodiment, the network element 2 (or UE or UE2) may report feedback aperiodically, for example, based on a trigger such as a media access control (MAC)-control element (CE) (e.g., based on the occurrence or detection of such a trigger). In yet another embodiment, the network element 2 (or UE or UE2) may utilize event-based reporting, such as triggering a feedback report based on a specific threshold. Additionally, in some embodiments, the reporting of feedback may be dynamically turned on or off.

[0039] In a particular embodiment, the feedback report may include one or more of the following: PDU-EP statistics and / or additional information associated with the PDU-EP statistics. For example, the PDU-EP statistics may include a single PDU-EP value, statistical values of several PDU-EPs (e.g., average, percentile, and / or standard), and / or an event indicating that the PDU-EP is below or above a threshold. According to some examples, the additional information associated with the PDU-EP statistics may include the identifier (ID) of the PDU, such as the PDCP sequence number (SN), the channel (e.g., RLC branch, QoS flow, radio bearer), the number of retransmissions, and / or the time difference between the received copy PDCP-PDU and the received primary PDCP-PDU.

[0040] In some embodiments, the PDU-EP value may be compressed. For example, in one embodiment, the PDU-EP closest to x-9 reliability: P = 10^−x, which is closest to the PDU-EP, may be reported, e.g., with 4 bits x = 0, 1, 2, ……, 15. According to a particular embodiment, the PDU-EP may also be defined using a threshold y, P 10^-(x + y), and x may be defined with a step size k, e.g., k = 2, then x = 0, 2, 4, 8, etc. In one embodiment, a single bit may be used to indicate whether the PDU-EP is above or below the threshold.

[0041] Figure 1D Another example signaling diagram for a multi-connectivity scenario according to an example embodiment is illustrated. In Figure 1D the example, network element 1 may represent the primary gNB, network element 2 may represent the UE, and network element 3 may represent the secondary gNB. Figure 1D The signaling in the example of Figure 1A may be similar to Figure 1D the signaling in

[0042] Figure 2 An example of the mapping of PDUs from MAC to SDAP and including the PHY layer is illustrated. According to a specific example, the PHY-PDU-EP can be equal to the CB BLEP after Hybrid Automatic Repeat reQuest (HARQ), the MAC-PDU-EP can be the error probability of the Transport Block (TB) calculated from the CB_BLEP, and the RLC-PDU-EP can be the error probability of the MAC-PDU-EP contributing to the RLC-PDU transmission.

[0043] In some embodiments, the PDU-EP on layer n (denoted by P n ) can be calculated by computing the joint error probability of the lower layer (N-1)-PDU-EP (denoted by P n-1 ). The joint error probability can be the same as the inverse probability of all (N-1) PDUs received successfully, because if one or more of the (N-1) PDUs fail, the N-PDU fails. According to one example, the PDU-EP on layer n can be determined according to the following formula: where M is the number of (N-1)-PDUs contributing to the N-PDU, and i is an index on the subset M. It should be noted that when n is PHY, then is the CB BLEP value after HARQ.

[0044] For example, considering the example of Figure 2 , the PDCP-PDU-EP of the first two IP packets will be equal to the first MAC-PDU-EP or the TB error probability. For the third packet, the PDCP-PDU will be the joint error probability of the two MAC-PDUs contributing to the transmission.

[0045] An example usage of some embodiments can include, but is not limited to, reward calculation for PDCP duplication based on Machine Learning (ML). Figure 3 An example of PDCP duplication is illustrated, where the PDCP-PDU can be duplicated via a second branch (Branch 2), and the second branch can be another Component Carrier (CC) and / or Dual Connectivity (DC). Currently, the standard supports up to 4 configurable branches. Duplication increases spatial and temporal diversity and generally increases the probability of successfully receiving the PDU. However, duplication may also increase the load and interference in the network. Therefore, overly frequent duplication may have a negative impact on the overall network performance. It is best to avoid unnecessary duplication, and necessary duplication is preferred. However, in URLLC, when the network has been performing with high reliability but below the reliability target, it is difficult to distinguish good and bad actions because the vast majority of PDCP-PDUs are received successfully.

[0046] Thus, a particular embodiment can provide a method that collects labeled training data for ML-based PDCP duplication. Given the current state of the system, the ML model can predict whether a PDCP-PDU is duplicated and on which branch it is duplicated; this prediction can be referred to as an action. The ML model can be constructed in various ways, and example embodiments can be applicable to various implementations. Independent of the model, the action and the state can be associated with the reward / label measurement results of the action. When training the ML model, states and actions with positive rewards can be used to reinforce certain more likely actions, and correspondingly, bad actions can be discouraged. In some cases, such as for multi-arm bands, the ML model does not necessarily use the state and can only associate actions and rewards.

[0047] In the following, examples with two-branch duplication as in Figure 3 are considered; however, these examples can be extended to any number of branches. In one example, the UE can be configured to feedback PDCP-PDU-EP for both branches. This feedback can be associated with the corresponding SN, which allows the network to calculate arbitrary labels for each duplication decision. In this way, the ML algorithm can classify and label the actions, even if all PDUs have been successfully received, which helps to guide the algorithm to meet the URLLC reliability target. Additionally, the feedback can be optimized by providing a PDU-EP target to the receiver and reporting a single bit indicating whether the PDU-EP is below or above the target.

[0048] According to a particular embodiment, a network node such as a gNB can configure one or more UEs for PDCP-PDU-EP measurement and reporting. In one example, PDCP-PDU measurements can be performed for each branch, which can be represented by P 主 and P 辅 . The definition of the PDCP-PDU-EP threshold represented by P 目标 can be provided by the network node to the UE(s). In some embodiments, the network node can also provide instructions to the UE(s) for calculating the joint error probability, such as P 总 =P 辅 *P 复制 and / or instructions for calculating the threshold comparison for each PDCP-PDU (true & false), such as: (i) P 主 <P 目标 , (ii) P 辅 <P 目标 , (iii) P 总 <P 目标 . In one embodiment, the measurement can be associated with the corresponding PDCP SN. In one example, optionally, the time difference T diff= P 主 -P 辅 。

[0049] In one embodiment, a network node (e.g., gNB) may predict the duplication of each PDCP-PDU and may temporarily store one or more of the following: status (optional), action / duplication decision, and / or the sequence number of the PDCP-PDU.

[0050] In one embodiment, the (multiple) UEs may feedback (multiple) PDCP-PDU-EP reports to the network node. According to some embodiments, the feedback may include a bitmap of conditions and is associated with the PDCP SN. In one example embodiment, the feedback report may not have a PDCP SN, and the (multiple) reports may be sorted by sequence number. Thus, the transmitter may map the reports to the corresponding SNs. In some instances, the reports may include SNs to assist in the synchronization mapping. Optionally, in one embodiment, T diff , in combination with T of DC diff is useful. The delay of the secondary branch can also be obtained through the Xn interface, but it may be more efficient to measure it in the UE. In one embodiment, n bits may be utilized to perform the reporting. Table 1 below illustrates an example of PDCP-PDU-EP reporting for ML-based PDCP duplication. For example, in Table 1, T diff = [-3, -2, -1, 0, 1, 2, 3, 4] may be reported using 3 bits representing the Transmission Time Interval (TTI) time difference or milliseconds.

[0051] Table 1

[0052]

[0053] In some embodiments, the reported PDCP-PDU-EP may be used to classify actions. Figure 4A Illustrates an example table of action classification based on PDCP-PDU-EP feedback. For example, as shown in the 6th column of Figure 4A , even if the PDCP-PDU is correctly received, if the PDCP-PDU-EP meets the target, it may be classified as a failure. Separately, in the 1st and 2nd columns of Figure 4A , if the primary PDCP-PDU is received with sufficient PDCP-PDU-EP, the duplicated PDCP-PDU is discarded, and thus, the action may be marked as an unnecessary duplication. When Figure 4AWhen the main PDCP-PDU-EP in the 3rd column and the 4a column is higher than the threshold, but the combined error probability is lower than the target, replication is required, so this behavior can be encouraged. In this way, after each PDCP-PDU-EP feedback, the ML policy can be guided in the desired direction without collecting a large number of samples to determine the goodness of the action. According to a particular embodiment, if the transmitter can measure the latency of the PDCP-PDU, for example, by enabling optional time difference reporting, classification can be achieved to take into account probability and latency requirements. According to an exemplary embodiment, Figure 4B A table illustrating an example of classification by considering reliability and latency targets.

[0054] According to an embodiment, the label of the action can be determined based on the classification. Although the ML model training can be carried out in various ways, in reference Figure 4A In one example, a reward of +1 can be assigned to categories 3, 4a, and 5, and a -1 can be assigned to other categories. Since less interference is generated, this will encourage the ML model to perform replication only when needed, thereby improving spectral efficiency and reducing the need for replication.

[0055] According to an example embodiment, Figure 5A An example flowchart of a method for error probability feedback is illustrated. In a particular example embodiment, Figure 5A The flowchart can be executed by a network entity or network node associated with a communication system such as LTE or 5G NR. For example, in some example embodiments, the network node that executes Figure 5A The method may include a base station, eNB, gNB, and / or NG-RAN node. Additionally or alternatively, in a particular embodiment, the network node that executes Figure 5A The method may include a UE, mobile device, mobile station, IoT device, etc. For example, in some embodiments, as described above, Figure 5A The method can be performed by Figure 1A Or Figure 1D The network element 1 of Figure 1B The gNB1 of Figure 1C And / or the UE1 of

[0056] As Figure 5AAs shown in the example of, the method may include, at 500, sending a configuration for PDU-EP calculation and reporting to at least one UE or network element. In some embodiments, the configuration may include an indication of the network layer for PDU-EP calculation, and / or an indication of the following items for PDU-EP calculation: a specific channel, service flow quality, radio bearer, and / or branch. In one embodiment, the configuration may be sent to at least one UE via RRC. According to one embodiment, the configuration may include a target threshold for PDU-EP. In some embodiments, the method may include sending one or more PDUs to at least one UE.

[0057] According to an embodiment, Figure 5A the method may further include: at 510, receiving feedback related to PDU-EP from at least one UE or network element. In some embodiments, receiving 510 may include, for example, periodically receiving feedback based on a fixed time interval or the number of PDUs received. In another embodiment, receiving 510 may include receiving feedback aperiodically based on, for example, a trigger. In yet another embodiment, receiving 510 may include receiving feedback based on meeting a specific threshold. According to one embodiment, the feedback may be dynamically turned on or off.

[0058] In a particular embodiment, the feedback may include PDU-EP statistics, such as a single PDU-EP value, statistical values of multiple PDU-EPs, and / or an event indicating that the PDU-EP is below or above a specific threshold. According to some embodiments, the feedback may include additional information associated with the PDU-EP statistics, such as an identifier of the PDU, an indication of the channel where the PDU error probability is calculated, an indication of the number of retransmissions of the PDU, and / or an indication of the time difference between the reception of the duplicate PDCP PDU and the primary PDCP PDU. In one embodiment, the feedback may include a single bit indicating whether the protocol data unit error probability is above or below the target threshold.

[0059] According to one embodiment, Figure 5AExamples can be applied to the PDCP duplication scenario. In such an embodiment, the transmitter 500 may include configuring at least one UE for PDCP PDU-EP measurement and reporting. In an embodiment, configuring at least one UE for PDCP PDU-EP measurement and reporting may include one or more of the following: providing instructions for performing PDCP PDU-EP measurement branch by branch, providing a definition of a threshold for PDCP PDU-EP measurement, providing instructions for calculating the combined error probability for each branch, providing instructions for calculating the threshold comparison for each PDCP PDU, and / or providing instructions to associate the PDCP PDU-EP measurement with the corresponding PDCP sequence number. Then, in some embodiments, the method may include predicting the duplication of each PDCP PDU and temporarily storing the prediction of whether the PDCP PDU should be duplicated, the decision on whether to duplicate the PDCP PDU, and / or the sequence number of the PDCP PDU. According to a particular embodiment, the method may further include: receiving a PDCP PDU-EP report; using the PDCP PDU-EP report to classify the prediction of whether the PDCP PDU should be duplicated; and determining a label for the prediction based on the classification. In one embodiment, the determined label can be used to train an ML model to perform PDCP duplication when duplication is needed and avoid unnecessary duplication.

[0060] According to an example embodiment, Figure 5B illustrates an example flowchart of a method for error probability feedback. In a particular example embodiment, Figure 5B the flowchart can be executed by a network entity or network node associated with a communication system such as LTE or 5G NR. For example, in some example embodiments, the network entity that executes Figure 5B the method may include a UE, a mobile device, a mobile station, an Internet of Things device, etc. For example, in some embodiments, as described above, Figure 5B the method can be performed by Figure 1A or Figure 1D network element 2 of Figure 1B the UE and / or Figure 1C UE2 of

[0061] In one embodiment, Figure 5B the method may include: at 550, receiving from a network node a configuration for PDU-EP calculation and a report for at least one network layer. In some embodiments, the configuration may include an indication of the network layer for PDU-EP calculation and / or an indication of the following items for PDU-EP calculation: a specific channel, service flow quality, radio bearer, and / or branch. In one embodiment, the configuration may be received from the network node via RRC. According to one embodiment, the configuration may include a target threshold for PDU-EP.

[0062] According to one embodiment, the method may further include, at 560, enabling recording and estimating BLEP. For example, in one embodiment, enabling 560 may include enabling BLEP estimation for one or more user plane CBs and recording the BLEP values. In one embodiment, enabling 560 may further include recording the mapping of user plane CBs to upper layer PDUs at at least one network layer.

[0063] In some embodiments, Figure 5B the method may include receiving, at 570, one or more PDUs from a network node or another network node. According to a particular embodiment, Figure 5B the method may include: at 580, calculating a PDU-EP for at least one network layer based on the received configuration and BLEP estimation. According to a particular embodiment, calculating 580 may include, when the decoded PDU matches the received configuration, using the recorded BLEP values and the mapping of user plane CBs to upper layer PDUs to calculate a PDU-EP for at least one network layer based on the CBs contributing to the PDU transmission.

[0064] In one embodiment, calculating 580 may include calculating the PDU-EP by calculating the joint error probability of the layer directly below at least one network layer. For example, if the PDU-EP on at least one layer is represented as P n , then the PDU-EP may be calculated by calculating the joint error probability of the lower layer (N-1)-PDU-EP represented by P n-1 . Then, according to an example, the PDU-EP on at least one layer may be determined according to the following formula: where M is the number of (N-1)-PDUs contributing to the N-PDU, and i is an index on the subset M.

[0065] According to a particular embodiment, Figure 5B the method may further include: at 590, sending feedback related to the PDU-EP to a network node. For example, in a particular embodiment, the feedback may be sent periodically based on a fixed time interval or the number of received PDUs, may be sent aperiodically based on a trigger, and / or may be sent based on meeting a specific threshold.

[0066] In some embodiments, the feedback may include one or more of the following: a single PDU-EP value, statistical values of multiple PDU-EPs, an event indicating that the PDU-EP is below or above a specific threshold, an identifier of the PDU, an indication of the channel for calculating the PDU-EP, an indication of the number of retransmissions of the PDU, and / or an indication of the time difference between the reception of a duplicate PDCP PDU and the reception of the primary PDCP PDU. In one embodiment, when a target threshold for the PDU-EP is configured, the feedback may include a single bit that indicates whether the PDU-EP is above or below the target threshold. Note that in a particular embodiment, the transmission of the feedback may be dynamically turned on or off.

[0067] As described above, some embodiments may be applied to the scenario of PDCP duplication. In such an example embodiment, the receiver 550 may include receiving a configuration for PDCP PDU-EP measurement and reporting. According to one embodiment, the configuration for PDCP PDU-EP measurement and reporting may include one or more of the following: instructions for performing PDCP PDU-EP measurement per branch, definition of the threshold for PDCP PDU-EP measurement, instructions for calculating the joint error probability for each branch, instructions for calculating the threshold comparison for each PDCP PDU, and / or instructions for associating the PDCP PDU-EP measurement with the corresponding PDCP sequence number. In some embodiments, the transmitter 290 may include transmitting a PDCP PDU-EP report to a network node.

[0068] According to an embodiment, Figure 6A An example of the apparatus 10 is illustrated. In one embodiment, the apparatus 10 may be a node, host, or server in a communication network or serving such a network. For example, the apparatus 10 may be a satellite, a base station, a Node B, an evolved Node B (eNB), a 5G Node B or access point, a next-generation Node B (NG-NB or gNB), and / or a WLAN access point, which is associated with a radio access network such as an LTE network, 5G, or NR. In an example embodiment, the apparatus 10 may be an NG-RAN node, an eNB in LTE, or a gNB in 5G. In other example embodiments, the apparatus 10 may be a UE, a mobile device, a mobile station, an Internet of Things device, etc.

[0069] It should be understood that in some example embodiments, the apparatus 10 may include an edge cloud server as a distributed computing system, where the server and the radio nodes may be independent devices communicating with each other via a wireless circuit path or via a wired connection, or they may be in the same entity communicating via a wired connection. For example, in a particular example embodiment, the apparatus 10 represents a gNB, which may be configured in a central unit (CU) and distributed unit (DU) architecture that divides the gNB functions. In such an architecture, the CU may be a logical node including gNB functions such as the transmission of user data, mobility control, radio access network sharing, positioning, and / or session management. The CU may control the operation of the (one or more) DUs through a front-haul interface. Depending on the function split option, the DU may be a logical node including a subset of the gNB functions. It should be noted that those of ordinary skill in the art should understand that the apparatus 10 may include Figure 6A components or features not shown in

[0070] As Figure 6A shown in the example of, the apparatus 10 may include a processor 12 for processing information and executing instructions or operations. The processor 12 may be any type of general-purpose or special-purpose processor. In fact, by way of example, the processor 12 may include one or more of a general-purpose computer, a special-purpose computer, a microprocessor, a digital signal processor (DSP), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), and a processor based on a multi-core processor architecture. Although a single processor 12 is shown in Figure 6A , multiple processors may be used according to other embodiments. For example, it should be understood that in a particular embodiment, the apparatus 10 may include two or more processors (e.g., in this case, the processor 12 may represent a multi-processor) that may form a multi-processor system, which may support multi-processing. In a particular embodiment, the multi-processor system may be tightly coupled or loosely coupled (e.g., to form a computer cluster).

[0071] The processor 12 may execute functions associated with the operation of the apparatus 10, which may include, for example, precoding of antenna gain / phase parameters, encoding and decoding of individual bits forming communication messages, formatting of information, and overall control of the apparatus 10, including processes related to communication resource management.

[0072] Device 10 may also include or be coupled to a memory 14 (internal or external), which may be coupled to the processor 12 for storing information and instructions that can be executed by the processor 12. The memory 14 may be one or more memories and may be any type of memory suitable for a local application environment and may be implemented using any suitable volatile or non-volatile data storage technology, such as semiconductor-based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory and / or removable memory. For example, the memory 14 may include random access memory (RAM) 54, read-only memory (ROM) 44, static memory such as a magnetic disk or optical disk, a hard disk drive (HDD) or any other combination of non-transitory machine or computer-readable media. For example, in one embodiment, the device 10 may include a non-volatile medium 64. In one embodiment, the non-volatile medium 64 may be a removable medium. The memory 14 and / or the medium 64 may store software, computer program code or instructions. The instructions stored in the memory 14 or the medium 64 may include program instructions or computer program code that, when executed by the processor 12, cause the device 10 to perform the described tasks.

[0073] In one embodiment, the device 10 may also include or be coupled to (internal or external) a drive or port configured to accept and read an external computer-readable storage medium, such as an optical disk, a USB drive, a flash drive or any other storage medium. For example, the external computer-readable storage medium may store computer programs or software to be executed by the processor 12 and / or the device 10.

[0074] In some embodiments, the device 10 may also include or be coupled to one or more antennas 15 for transmitting signals and / or data to and receiving signals and / or data from the device 10. The device 10 may also include or be coupled to a transceiver 18 configured to transmit and receive information. The transceiver 18 may include, for example, a plurality of radio interfaces that may be coupled to the (multiple) antennas 15. The radio interfaces may correspond to a variety of radio access technologies, including one or more of the following: GSM, NB-IoT, LTE, 5G, WLAN, Bluetooth, BT-LE, NFC, radio frequency identifier (RFID), ultra-wideband (UWB), Multefire, etc. The radio interfaces may include components such as filters, converters (e.g., digital-to-analog converters, etc.), mappers, fast Fourier transform (FFT) modules, etc. to generate symbols for transmission via one or more downlinks and receive symbols (e.g., via an uplink).

[0075] Thus, the transceiver 18 can be configured to modulate information onto a carrier waveform for transmission by the antenna(s) 15 and demodulate information received via the antenna(s) 15 for further processing by other elements of the device 10. In other embodiments, the transceiver 18 is capable of directly transmitting and receiving signals or data. Additionally or alternatively, in some embodiments, the device 10 may include input and / or output devices (I / O devices).

[0076] In one embodiment, the memory 14 may store software modules that provide functionality when executed by the processor 12. These modules may include, for example, an operating system that provides operating system functionality for the device 10. The memory may also store one or more functional modules, such as applications or programs, to provide additional functionality for the device 10. The components of the device 10 may be implemented in hardware or any suitable combination of hardware and software.

[0077] According to some embodiments, the processor 12 and the memory 14 may be included in a processing circuitry or a control circuitry, or may form part of a processing circuitry or a control circuitry. Additionally, in some embodiments, the transceiver 18 may be included in a transceiver circuitry or may form part of a transceiver circuitry.

[0078] As used herein, the term "circuitry" may refer to only hardware circuit implementations (e.g., analog and / or digital circuitry), combinations of hardware circuits and software, combinations of analog and / or digital hardware circuits with software / firmware, any portion of a hardware processor (including a digital signal processor) with software that work together to cause a device (e.g., the device 10) to perform various functions, and / or hardware circuits and / or processors, or portions thereof, that use software for operation but for which the software may not be present when it is not needed for operation. As a further example, as used herein, the term "circuitry" may also cover an implementation that includes only a hardware circuit or a processor (or processors), or a portion of a hardware circuit or a processor, and its accompanying software and / or firmware. The term "circuitry" may also cover, for example, a baseband integrated circuit in a server, a cellular network node or device, or other computing or network device.

[0079] As described above, in certain embodiments, the device 10 may be a network node or a RAN node, such as a base station, an access point, a Node B, an eNB, a gNB, a WLAN access point, etc. In other exemplary embodiments, the device 10 may be a UE, a mobile device, a mobile station, an IoT device, etc. For example, in some embodiments, the device 10 may be configured to perform one or more of the processes depicted in any of the flowcharts or signaling diagrams described herein, such as in Figure 1A , Figure 1B , Figure 1C ,Figure 1D , Figure 3 , Figure 5A or Figure 5B those shown in Figure 5B . In some embodiments, as discussed herein, apparatus 10 may be configured to perform processes related to error probability feedback.

[0080] According to a particular embodiment, apparatus 10 may be controlled by memory 14 and processor 12 to send a configuration for PDU-EP calculation and reporting to at least one UE. In some embodiments, the configuration may include an indication of the network layer for PDU-EP calculation, and / or an indication of the following items for PDU-EP calculation: a specific channel, a quality of service flow, a radio bearer, and / or a branch. In one embodiment, the configuration may be sent to at least one UE via RRC. According to one embodiment, the configuration may include a target threshold for PDU-EP. In some embodiments, the method may include sending one or more PDUs to at least one UE.

[0081] According to an embodiment, apparatus 10 may be controlled by memory 14 and processor 12 to receive feedback related to PDU-EP from at least one UE. In some embodiments, for example, the feedback may be received periodically based on a fixed time interval or the number of PDUs received. In another embodiment, the feedback may be received aperiodically based on, for example, a trigger. In yet another embodiment, the feedback may be received based on meeting a specific threshold. According to one embodiment, the feedback may be dynamically turned on or off.

[0082] In a particular embodiment, the feedback may include PDU-EP statistics, such as a single PDU-EP value, statistical values of multiple PDU-EPs, and / or an event indicating that the PDU-EP is below or above a specific threshold. According to some embodiments, the feedback may include additional information associated with the PDU-EP statistics, such as an identifier of the PDU, an indication of the channel on which the PDU error probability is calculated, an indication of the number of retransmissions of the PDU, and / or an indication of the time difference between the reception of the duplicate PDCP PDU and the primary PDCP PDU. In one embodiment, the feedback may include a single bit that indicates whether the protocol data unit error probability is above or below the target threshold.

[0083] As described above, some embodiments can be applied to PDCP duplication scenarios. In such an exemplary embodiment, the apparatus 10 can be controlled by the memory 14 and the processor 12 to configure at least one UE for PDCP PDU-EP measurement and reporting. In one embodiment, the apparatus 10 can be controlled by the memory 14 and the processor 12 to configure at least one UE for PDCP PDU-EP measurement and reporting by one or more of the following: providing instructions for per-branch execution of PDCP PDU-EP measurement, providing a definition of a threshold for PDCP PDU-EP measurement, providing instructions for calculating the combined error probability for each branch, providing instructions for calculating threshold comparison for each PDCP PDU, and / or providing instructions for associating PDCP PDU-EP measurement with the corresponding PDCP sequence number. Then, in some embodiments, the apparatus 10 can be controlled by the memory 14 and the processor 12 to predict the duplication of each PDCP PDU, and temporarily store the prediction of whether a PDCP PDU should be duplicated, the decision on whether to duplicate a PDCP PDU, and / or the sequence number of the PDCP PDU. According to a particular embodiment, the apparatus 10 can be controlled by the memory 14 and the processor 12 to receive a PDCP PDU-EP report, use the PDCP PDU-EP report to classify the prediction of whether a PDCP PDU should be duplicated, and / or determine a label for the prediction based on the classification. In one embodiment, the apparatus 10 can be controlled by the memory 14 and the processor 12 to train an ML model with the determined label when duplication is needed to perform PDCP duplication, thus avoiding unnecessary duplication.

[0084] Figure 6B An example of an apparatus 20 according to another embodiment is illustrated. In one embodiment, the apparatus 20 can be a node or element in or associated with a communication network, such as a UE, a mobile device (ME), a mobile station, a mobile device, a fixed device, an IoT device, or other device. As described herein, a UE can alternatively be referred to as, for example, a mobile station, a mobile device, a mobile unit, a mobile device, a user equipment, a user station, a wireless terminal, a tablet, a smart phone, an Internet of Things device, a sensor, or an NB-IoT device, etc. As an example, the apparatus 20 can be implemented in, for example, a wireless handheld device, a wireless plug-in accessory, etc.

[0085] In some example embodiments, device 20 may include one or more processors, one or more computer-readable storage media (e.g., memory, storage devices, etc.), one or more radio access components (e.g., modems, transceivers, etc.) and / or a user interface. In some embodiments, device 20 may be configured to operate using one or more radio access technologies, such as GSM, LTE, LTE-A, NR, 5G, WLAN, WiFi, NB-IoT, Bluetooth, NFC, MulteFire and / or any other radio access technology. It should be noted that those of ordinary skill in the art should understand that device 20 may include Figure 6B modules or features not shown in

[0086] As Figure 6B shown in the example of Figure 6B , device 20 may include a processor 22 or be coupled to a processor 22 for processing information and executing instructions or operations. Processor 22 may be any type of general-purpose or special-purpose processor. In fact, by way of example, processor 22 may include one or more of the following: general-purpose computers, special-purpose computers, microprocessors, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs) and processors based on multi-core processor architectures. Although a single processor 22 is shown in

[0087] , multiple processors may be used according to other embodiments. For example, it should be understood that in a particular embodiment, device 20 may include two or more processors that may form a multi-processor system (e.g., in this case, processor 22 may represent a multi-processor), and the multi-processor system may support multi-processing. In a particular embodiment, the multi-processor system may be tightly coupled or loosely coupled (e.g., to form a computer cluster).

[0087] Processor 22 may perform functions associated with the operation of device 20, and as some examples, include precoding of antenna gain / phase parameters, encoding and decoding of the respective bits forming a communication message, formatting of information, and overall control of device 20, including processes related to communication resource management.

[0088] The apparatus 20 may also include or be coupled to a memory 24 (internal or external), which may be coupled to the processor 22 and is used to store information and instructions executable by the processor 22. The memory 24 may be one or more memories and may be any type of memory suitable for the local application environment, and may be implemented using any suitable volatile or non-volatile data storage technology, such as semiconductor memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory and / or removable memory. For example, the memory 24 may include random access memory (RAM) 84, read-only memory (ROM) 74, static memory such as a magnetic disk or optical disk, a hard disk drive (HDD), or any other combination of non-transitory machine or computer-readable media. For example, in one embodiment, the apparatus 20 may include a non-volatile medium 94. In one embodiment, the non-volatile medium 94 may be a removable medium. The memory 24 and / or the medium 94 may store software, computer program code or instructions. The instructions stored in the memory 24 or the medium 94 may include program instructions or computer program code, which, when executed by the processor 22, enable the apparatus 20 to perform the tasks described herein.

[0089] In one embodiment, the apparatus 20 may also include or be coupled to (internal or external) a drive or port configured to accept and read an external computer-readable storage medium, such as an optical disk, a USB drive, a flash drive, or any other storage medium. For example, the external computer-readable storage medium may store computer programs or software executable by the processor 22 and / or the apparatus 20.

[0090] In some embodiments, the apparatus 20 may also include or be coupled to one or more antennas 25 for receiving downlink signals from the apparatus 20 and transmitting via the uplink. The apparatus 20 may also include a transceiver 28 configured to transmit and receive information. The transceiver 28 may also include a radio interface (e.g., a modem) coupled to the antenna 25. The radio interface may correspond to multiple radio access technologies, including one or more of the following: GSM, LTE, LTE-A, 5G, NR, WLAN, NB-IoT, Bluetooth, BT-LE, NFC, RFID, UWB, etc. The radio interface may include other components, such as filters, converters (e.g., digital-to-analog converters, etc.), symbol demappers, signal shaping components, inverse fast Fourier transform (IFFT) modules, etc., to process symbols carried by the downlink or uplink, such as OFDMA symbols.

[0091] For example, the transceiver 28 can be configured to modulate information onto a carrier waveform for transmission by the antenna(s) 25 and demodulate the information received via the antenna(s) 25 for further processing by other elements of the device 20. In other embodiments, the transceiver 28 is capable of directly transmitting and receiving signals or data. Additionally or alternatively, in some embodiments, the device 20 may include input and / or output devices (I / O devices). In a particular embodiment, the device 20 may further include a user interface, such as a graphical user interface or a touch screen.

[0092] In one embodiment, the memory 24 stores software modules that provide functionality when executed by the processor 22. These modules may include, for example, an operating system that provides operating system functionality for the device 20. The memory may also store one or more functional modules, such as applications or programs, to provide additional functionality for the device 20. The components of the device 20 may be implemented in hardware or as any suitable combination of hardware and software. According to an example embodiment, the device 20 may optionally be configured to communicate with the device 10 via a radio or wired communication link 70 according to any radio access technology (e.g., NR).

[0093] According to some embodiments, the processor 22 and the memory 24 may be included in a processing circuitry or a control circuitry, or may form part of a processing circuitry or a control circuitry. Additionally, in some embodiments, the transceiver 28 may be included in a transceiver circuitry or may form part of a transceiver circuitry.

[0094] As described above, according to some embodiments, the device 20 may be, for example, a UE, a mobile device, a mobile station, a ME, an IoT device, and / or an NB-IoT device. According to a particular embodiment, the device 20 may be controlled by the memory 24 and the processor 22 to perform functions associated with the example embodiments described herein. For example, in some embodiments, the device 20 may be configured to perform one or more processes depicted in any of the flowcharts or signaling diagrams described herein, such as those shown in Figure 1A , Figure 1B , Figure 1C , Figure 1D , Figure 3 , Figure 5A or Figure 5B . In a particular embodiment, the device 20 may include or represent a UE and may be configured to perform, for example, a process related to error probability feedback.

[0095] In a particular embodiment, the apparatus 20 may be controlled by the memory 24 and the processor 22 to receive, from a network node, a configuration for PDU-EP calculation and reporting for at least one network layer. In some embodiments, the configuration may include an indication of the network layer for PDU-EP calculation, and / or an indication of the following items for PDU-EP calculation: a particular channel, a quality of service flow, a radio bearer, and / or a branch. In one embodiment, the configuration may be received from the network node via RRC. According to one embodiment, the configuration may include a target threshold for the PDU-EP.

[0096] According to one embodiment, the apparatus 20 may be controlled by the memory 24 and the processor 22 to enable recording and estimating the BLEP. For example, in one embodiment, the apparatus 20 may be controlled by the memory 24 and the processor 22 to enable BLEP estimation for one or more user plane CBs and record the BLEP values. In one embodiment, the apparatus 20 may be controlled by the memory 24 and the processor 22 to record the mapping of the user plane CB to the upper layer PDU at at least one network layer.

[0097] In some embodiments, the apparatus 20 may be controlled by the memory 24 and the processor 22 to receive one or more PDUs from a network node or another network node. According to a particular embodiment, the apparatus 20 may be controlled by the memory 24 and the processor 22 to calculate the PDU-EP for at least one network layer based on the received configuration and the BLEP estimation. According to a particular embodiment, when a PDU matching the received configuration is decoded, the apparatus 20 may be controlled by the memory 24 and the processor 22 to calculate the PDU-EP for at least one network layer based on the recorded BLEP values and the mapping of the user plane CB to the upper layer PDU, based on the CBs contributing to the PDU transmission.

[0098] In one embodiment, the apparatus 20 may be controlled by the memory 24 and the processor 22 to calculate the PDU-EP by calculating the combined error probability of the layer directly below at least one network layer. For example, if the PDU-EP on at least one layer is represented as P n , then the apparatus 20 may be controlled by the memory 24 and the processor 22 to calculate the PDU-EP by calculating the combined error probability of the lower layer (N-1)-PDU-EP represented by P n-1 . According to an example, the apparatus 20 may be controlled by the memory 24 and the processor 22 to determine the PDU-EP on at least one layer according to the following formula : where M is the number of (N-1)-PDUs contributing to the N-PDU, and i is an index over the subset M.

[0099] According to a particular embodiment, the apparatus 20 may be controlled by the memory 24 and the processor 22 to send feedback related to the PDU-EP to a network node. For example, in a particular embodiment, the apparatus 20 may be controlled by the memory 24 and the processor 22 to send feedback periodically based on a fixed time interval or the number of received PDUs, aperiodically based on a trigger and / or based on meeting a particular threshold.

[0100] In some embodiments, the feedback may include one or more of the following: a single PDU-EP value, statistical values of multiple PDU-EPs, an event indicating that the PDU-EP is below or above a particular threshold, an identifier of the PDU, an indication of the channel for calculating the PDU-EP, an indication of the number of retransmissions of the PDU, and / or an indication of the time difference between the reception of the duplicate PDCP PDU and the primary PDCP PDU. In one embodiment, when a target threshold for the PDU-EP is configured, the feedback may include a single bit that indicates whether the PDU-EP is above or below the target threshold. Note that in a particular embodiment, the transmission of the feedback may be dynamically turned on or off.

[0101] As described above, some embodiments may be applied to a PDCP duplication scenario. In such an exemplary embodiment, the apparatus 20 may be controlled by the memory 24 and the processor 22 to receive a configuration for PDCP PDU-EP measurement and reporting. According to one embodiment, the configuration for PDCP PDU-EP measurement and reporting may include one or more of the following: instructions for performing PDCP PDU-EP measurement per branch, definition of a threshold for PDCP PDU-EP measurement, instructions for calculating the joint error probability for each branch, instructions for calculating the threshold comparison for each PDCP PDU, and / or instructions for associating the PDCP PDU-EP measurement with the corresponding PDCP sequence number. In some embodiments, the apparatus 20 may be controlled by the memory 24 and the processor 22 to send a PDCP PDU-EP report to a network node.

[0102] Accordingly, certain example embodiments provide several technical improvements, enhancements, and / or advantages over existing technology processes and constitute at least an improvement in the technical field of wireless network control and management. As an example, a particular embodiment may improve URLLC, for example, by introducing new radio feedback to measure the reliability of radio links. Similarly, some embodiments may provide improvements in other applications such as, but not limited to, eMBB and mMTC, as well as other wireless technologies (e.g., industrial Wi-Fi). It should be noted that some example embodiments may provide improvements in spectral efficiency and may reduce the need for duplication, for example, due to generating less interference. Thus, the use of certain example embodiments results in improved functionality of communication networks and their nodes, such as base stations, eNBs, gNBs, and / or UEs or mobile stations.

[0103] In some example embodiments, the functionality of any method, process, signaling diagram, algorithm, or flowchart described herein may be implemented by software and / or computer program code or portions of code stored in a memory or other computer-readable or tangible medium and executed by a processor.

[0104] In some example embodiments, an apparatus may include or be associated with at least one software application, module, unit, or entity configured to perform (a plurality of) arithmetic operations, programs, or portions of a program (including software routines that are added or updated) by at least one operating processor. A program, also referred to as a program product or a computer program, includes software routines, applets, and macros, and the program may be stored in any device-readable data storage medium and may include program instructions for performing a particular task.

[0105] A computer program product may include one or more computer-executable components that, when the program is run, are configured to perform some exemplary embodiments. The one or more computer-executable components may be at least one software code or portion of code. Modifications and configurations for implementing the functionality of the example embodiments may be performed as (a plurality of) routines that may be implemented as (a plurality of) software routines that are added or updated. In one example, the (a plurality of) software routines may be downloaded to a device.

[0106] By way of example, software or computer program code or code portions may be in source code form, object code form or some intermediate form, and it may be stored in some type of carrier, distribution medium or computer-readable medium, which may be any entity or device capable of carrying the program. Such a carrier may include, for example, a recording medium, computer memory, read-only memory, electro-optical and / or electrical carrier signals, telecommunication signals and / or software distribution packages. Depending on the required processing capabilities, the computer program may be executed in a single electronic digital computer or may be distributed among multiple computers. The computer-readable medium or computer-readable storage medium may be a non-transitory medium.

[0107] In other example embodiments, the functionality may be performed by hardware or circuitry included in the device, such as by using an application specific integrated circuit (ASIC), programmable gate array (PGA), field programmable gate array (FPGA) or any other combination of hardware and software. In yet another example embodiment, the functionality may be implemented as a signal, such as a non-tangible device, which may be carried by an electromagnetic signal downloaded from the Internet or other network.

[0108] According to an exemplary embodiment, a device such as a node, device or corresponding component may be configured as a circuit system, computer or microprocessor such as a single-chip computer element, or a chipset, which may at least include a memory for providing storage capacity for performing (multiple) arithmetic operations, and / or an arithmetic processor for performing (multiple) arithmetic operations.

[0109] Those of ordinary skill in the art will readily understand that the example embodiments described above may be practiced with a process in a different order, and / or with hardware elements in a configuration different from the disclosed configuration. Thus, while some embodiments have been described based on these exemplary embodiments, it will be apparent to those skilled in the art that certain modifications, variations and alternative constructions will be apparent while remaining within the spirit and scope of the exemplary embodiments.

Claims

1. A device for communication, comprising: at least one processor; and at least one memory, including computer program code, wherein the at least one memory and the computer program code are configured to, together with the at least one processor, cause the device to at least: send a configuration for protocol data unit error probability calculation and reporting to at least one user equipment; and receive feedback related to the protocol data unit error probability from the at least one user equipment; wherein the configuration includes a target threshold for the protocol data unit error probability, and wherein the feedback includes a single bit indicating whether the protocol data unit error probability is higher or lower than the target threshold.

2. The device according to claim 1, wherein the at least one memory and the computer program code are configured to, together with the at least one processor, cause the device to at least: send at least one protocol data unit to the at least one user equipment.

3. The device according to claim 1, wherein the configuration includes at least one of the following: an indication of the network layer for the protocol data unit error probability calculation; and an indication of the following items for the protocol data unit error probability calculation: a specific channel, service flow quality, radio bearer, and / or branch.

4. The device according to claim 1, wherein the feedback is received periodically based on a fixed time interval or the number of received protocol data units; or wherein the feedback is received aperiodically based on a trigger; or wherein the feedback is received based on meeting a specific threshold.

5. The device according to claim 1, wherein the feedback is dynamically turned on or off.

6. The device according to claim 1, wherein the feedback includes at least one of the following: a single protocol data unit error probability value; a statistical value of multiple protocol data unit error probabilities; an event indicating that the protocol data unit error probability is lower or higher than a specific threshold; an identifier of the protocol data unit; an indication of the channel for calculating the protocol data unit error probability; an indication of the number of retransmissions of the protocol data unit; or an indication of the time difference between the received copy packet data convergence protocol (PDCP) protocol data unit and the received primary PDCP protocol data unit.

7. The device according to claim 1, wherein the at least one memory and the computer program code are configured to, together with the at least one processor, cause the device to at least: configure the at least one user equipment for packet data convergence protocol (PDCP) protocol data unit error probability measurement and reporting, wherein configuring the at least one user equipment includes at least one of the following: Provide instructions for performing the packet data convergence protocol (PDCP) protocol data unit error probability measurement branch by branch, provide a definition of a threshold for the PDCP protocol data unit error probability measurement, provide instructions for calculating a combined error probability for each branch, provide instructions for calculating the threshold comparison for each PDCP protocol data unit, and provide the following instructions: associate the PDCP protocol data unit error probability measurement with the corresponding PDCP sequence number.

8. The apparatus according to claim 1, wherein the at least one memory and the computer program code are configured to, together with the at least one processor, cause the apparatus to at least: Predict the duplication of each PDCP protocol data unit; and Temporarily store at least one of the following: a prediction of whether a PDCP protocol data unit should be duplicated, a determination of whether to duplicate the PDCP protocol data unit, or the sequence number of the PDCP protocol data unit.

9. The apparatus according to claim 8, wherein the at least one memory and the computer program code are configured to, together with the at least one processor, cause the apparatus to at least: Receive a PDCP protocol data unit error probability report; Use the PDCP protocol data unit error probability report to classify the prediction of whether a PDCP protocol data unit should be duplicated; And Determine a label for the prediction based on the classification.

10. The apparatus according to claim 9, wherein the at least one memory and the computer program code are configured to, together with the at least one processor, cause the apparatus to at least: When the duplication is needed, use the determined label to train a machine learning model to perform PDCP duplication.

11. The apparatus according to any one of claims 1 to 10, wherein the apparatus comprises at least one of the following: a network node or a user equipment.

12. A communication method, comprising: Send a configuration for protocol data unit error probability calculation and reporting to at least one user equipment; And Receive feedback related to the protocol data unit error probability from the at least one user equipment; Wherein the configuration includes a target threshold for the protocol data unit error probability, and wherein the feedback includes a single bit indicating whether the protocol data unit error probability is higher or lower than the target threshold.

13. A communication apparatus, comprising: Means for sending a configuration for protocol data unit error probability calculation and reporting to at least one user equipment; And Means for receiving feedback related to the protocol data unit error probability from the at least one user equipment; Wherein the configuration includes a target threshold for the protocol data unit error probability, and wherein the feedback includes a single bit indicating whether the protocol data unit error probability is above or below the target threshold.

14. A device for communication, comprising: at least one processor; and at least one memory, including computer program code, the at least one memory and the computer program code are configured to, together with the at least one processor, cause the device to at least: receive, from a network node, a configuration for protocol data unit error probability calculation and reporting for at least one network layer; enable recording and estimation of block error probability; calculate, based on the received configuration and the block error probability estimation, the protocol data unit error probability for the at least one network layer; and send feedback related to the protocol data unit error probability to the network node; wherein the configuration includes at least one of the following: an indication of the at least one network layer for the protocol data unit error probability calculation; and an indication of the following items for the protocol data unit error probability calculation: a specific channel, service flow quality, radio bearer, and / or branch.

15. The device according to claim 14, wherein the at least one memory and the computer program code are configured to, together with the at least one processor, cause the device to at least: receive at least one protocol data unit from the network node or another network node.

16. The device according to claim 14, wherein the at least one memory and the computer program code are configured to, together with the at least one processor, cause the device to at least enable: estimation of the block error probability of one or more user plane code blocks, recording of the block error probability value, and recording of the mapping of the user plane code blocks to the upper layer protocol data units at the at least one network layer.

17. The device according to claim 16, wherein the at least one memory and the computer program code are configured to, together with the at least one processor, cause the device to at least: when decoding a protocol data unit that matches the received configuration, use the recorded block error probability value and the mapping of the user plane code blocks to the upper layer protocol data units, and calculate the protocol data unit error probability for the at least one network layer based on the code blocks contributing to the transmission of the protocol data unit.

18. The device according to claim 14, wherein the at least one memory and the computer program code are configured to, together with the at least one processor, cause the device to at least: calculate the protocol data unit error probability by calculating the combined error probability of the layer directly below the at least one network layer.

19. The device according to claim 14, wherein the feedback is sent periodically based on a fixed time interval or the number of received protocol data units; or wherein the feedback is sent aperiodically based on a trigger; or wherein the feedback is sent based on meeting a specific threshold.

20. The apparatus according to claim 14, wherein the feedback is dynamically turned on or off.

21. The apparatus according to claim 14, wherein the feedback includes at least one of the following: A single protocol data unit error probability value; A statistical value of multiple protocol data unit error probabilities; An event indicating that the protocol data unit error probability is lower or higher than a specific threshold; An identifier of the protocol data unit; An indication of the channel for calculating the protocol data unit error probability; An indication of the number of retransmissions of the protocol data unit; or An indication of the time difference between a received copy packet data convergence protocol (PDCP) protocol data unit and a received primary PDCP protocol data unit.

22. The apparatus according to claim 14, wherein the configuration includes a target threshold for the protocol data unit error probability, and wherein the feedback includes a single bit indicating whether the protocol data unit error probability is higher or lower than the target threshold.

23. The apparatus according to claim 14, wherein the at least one memory and the computer program code are configured to, together with the at least one processor, cause the apparatus to at least: Receive a configuration for packet data convergence protocol (PDCP) protocol data unit error probability measurement and reporting, the configuration including at least one of the following: Instructions to perform the PDCP protocol data unit error probability measurement branch by branch, a definition of a threshold for the PDCP protocol data unit error probability measurement, instructions to calculate a joint error probability for each branch, instructions to calculate the threshold comparison for each PDCP protocol data unit, and the following instructions: associate the PDCP protocol data unit error probability measurement with a corresponding PDCP sequence number; and Send a PDCP protocol data unit error probability report to the network node.

24. The apparatus according to any one of claims 14 to 23, wherein the apparatus includes a user equipment, and wherein the network node includes at least one of the following: gNB or user equipment.

25. A method of communication, comprising: Receiving, at a user equipment, a configuration from a network node, wherein the configuration is for protocol data unit error probability calculation and reporting for at least one network layer; Enabling recording and estimation of block error probability; Calculating, based on the received configuration and the block error probability estimation, the protocol data unit error probability for the at least one network layer; And Sending feedback related to the protocol data unit error probability to the network node; Wherein the configuration includes at least one of the following: An indication of the at least one network layer for the protocol data unit error probability calculation; and An indication of the following items for the protocol data unit error probability calculation: a specific channel, service flow quality, radio bearer, and / or branch.

26. An apparatus for communication, comprising: A component for receiving, from a network node, a configuration for protocol data unit error probability calculation and reporting for at least one network layer; A component for enabling recording and estimation of block error probability; A component for calculating, based on the received configuration and the block error probability estimation, the protocol data unit error probability for the at least one network layer; And A component for sending feedback related to the protocol data unit error probability to the network node; Wherein the configuration includes at least one of the following: An indication of the at least one network layer for the protocol data unit error probability calculation; and An indication of the following items for the protocol data unit error probability calculation: a specific channel, service flow quality, radio bearer, and / or branch.

27. A computer-readable medium comprising program instructions stored thereon for performing the method according to any one of claims 12 or 25.

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