Patent Description:
In the following sections, overview of Next Generation Radio Access Network (NG-RAN) architecture and <NUM> New Radio (NR) stacks will be discussed. <NUM> NR (New Radio) user and control plane functions with monolithic gNB (gNodeB) are shown in <FIG>. For the user plane (shown in <FIG>, which is in accordance with 3GPP TS <NUM>), PHY (physical), MAC (Medium Access Control), RLC (Radio Link Control), PDCP (Packet Data Convergence Protocol) and SDAP (Service Data Adaptation Protocol) sublayers originate in the UE <NUM> and are terminated in the gNB <NUM> on the network side. For the control plane (shown in <FIG>, which is in accordance with 3GPP TS <NUM>), RRC (Radio Resource Control), PDCP, RLC, MAC and PHY sublayers originate in the UE <NUM> and are terminated in the gNB <NUM> on the network side, and NAS (Non-Access Stratum) originate in the UE <NUM> and is terminated in the AMF (Access Mobility Function) <NUM> on the network side.

NG-Radio Access Network (NG-RAN) architecture from 3GPP TS <NUM> is shown in <FIG>. As shown in <FIG>, the NG-RAN <NUM> consists of a set of gNBs <NUM> connected to the 5GC <NUM> through the NG interface. Each gNB comprises gNB-CU <NUM> and one or more gNB-DU <NUM> (see <FIG>). As shown in <FIG> (which illustrates separation of CU-CP (CU-Control Plane) and CU-UP (CU-User Plane)), E1 is the interface between gNB-CU-CP (CU-Control Plane) 304a and gNB-CU-UP (CU-User Plane) 304b, F1-C is the interface between gNB-CU-CP 304a and gNB-DU <NUM>, and F1-U is the interface between gNB-CU-UP 304b and gNB-DU <NUM>. As shown in <FIG>, gNB <NUM> can consist of a gNB-CU-CP 304a, multiple gNB-CU-Ups 304b and multiple gNB-DUs <NUM>. One gNB-DU <NUM> is connected to only one gNB-CU-CP 304a, and one gNB-CU-UP 304b is connected to only one gNB-CU-CP 304a.

In this section, an overview Layer <NUM> (L2) of <NUM> NR will be provided in connection with <FIG>. L2 of <NUM> NR is split into the following sublayers (in accordance with 3GPP TS <NUM>):.

<FIG> is a block diagram illustrating DL L2 structure, in accordance with 3GPP TS <NUM>. <FIG> is a block diagram illustrating UL L2 structure, in accordance with 3GPP TS <NUM>. <FIG> is a block diagram illustrating L2 data flow example, in accordance with 3GPP TS <NUM> (in <FIG>, H denotes headers or sub-headers).

Open Radio Access Network (O-RAN) is based on disaggregated components which are connected through open and standardized interfaces based on 3GPP NG-RAN. An overview of O-RAN with disaggregated RAN CU (Centralized Unit), DU (Distributed Unit), and RU (Radio Unit), near-real-time Radio Intelligent Controller (RIC) and non-real-time RIC is illustrated in <FIG>.

As shown in <FIG>, the CU (shown split as O-CU-CP 801a and O-CU-UP 801b) and the DU (shown as O-DU <NUM>) are connected using the F1 interface (with F1-C for control plane and F1-U for user plane traffic) over a mid-haul (MH) path. One DU can host multiple cells (e.g., one DU could host <NUM> cells) and each cell may support many users. For example, one cell may support <NUM> Radio Resource Control (RRC)-connected users and out of these <NUM>, there may be <NUM> Active users (i.e., users that have data to send at a given point of time).

A cell site can comprise multiple sectors, and each sector can support multiple cells. For example, one site could comprise three sectors and each sector could support eight cells (with eight cells in each sector on different frequency bands). One CU-CP (CU-Control Plane) could support multiple DUs and thus multiple cells. For example, a CU-CP could support <NUM>,<NUM> cells and around <NUM>,<NUM> User Equipments (UEs). Each UE could support multiple Data Radio Bearers (DRB) and there could be multiple instances of CU-UP (CU-User Plane) to serve these DRBs. For example, each UE could support <NUM> DRBs, and <NUM>,<NUM> DRBs (corresponding to <NUM>,<NUM> UEs) may be served by five CU-UP instances (and one CU-CP instance).

The DU could be located in a private data center, or it could be located at a cell-site. The CU could also be in a private data center or even hosted on a public cloud system. The DU and CU, which are typically located at different physical locations, could be tens of kilometers apart. The CU communicates with a <NUM> core system, which could also be hosted in the same public cloud system (or could be hosted by a different cloud provider). A RU (Radio Unit) (shown as O-RU <NUM> in <FIG>) is located at a cell-site and communicates with the DU via a front-haul (FH) interface.

The E2 nodes (CU and DU) are connected to the near-real-time RIC <NUM> using the E2 interface. The E2 interface is used to send data (e.g., user, cell, slice KPMs) from the RAN, and deploy control actions and policies to the RAN at near-real-time RIC <NUM>. The application or service at the near-real-time RIC <NUM> that deploys the control actions and policies to the RAN are called xApps. The near-real-time RIC <NUM> is connected to the non-real-time RIC <NUM> (which is shown as part of Service Management and Orchestration (SMO) Framework <NUM> in <FIG>) using the A1 interface. Also shown in <FIG> are O-eNB <NUM> (which is shown as being connected to the near-real-time RIC <NUM> and the SMO Framework <NUM>) and O-Cloud <NUM> (which is shown as being connected to the SMO Framework <NUM>).

In this section, PDU sessions, DRBs, and quality of service (QoS) flows will be discussed. In <NUM> networks, PDU connectivity service is a service that provides exchange of PDUs between a UE and a data network identified by a Data Network Name (DNN). The PDU Connecitivity service is supported via PDU sessions that are established upon request from the UE. The DNN defines the interface to a specific external data network. One or more QoS flows can be supported in a PDU session. All the packets belonging to a specific QoS flow have the same 5QI (<NUM> QoS Identifier). A PDU session consists of the following: Data Radio Bearer which is between UE and CU in RAN; and an NG-U GTP tunnel which is between CU and UPF (User Plane Function) in the core network. <FIG> illustrates an example PDU session (in accordance with 3GPP TS <NUM>) consisting of multiple DRBs, where each DRB can consist of multiple QoS flows. In <FIG>, three components are shown for the PDU session <NUM>: UE <NUM>; access network (AN) <NUM>; and UPF <NUM>, which includes Packet Detection Rules (PDRs) <NUM>.

The following should be noted for 3GPP <NUM> network architecture, which is illustrated in <FIG> in the context of Radio Resource Management (RRM) (for connecting UE <NUM> to the network via RU <NUM>) with a MAC Scheduler <NUM>:.

In this section, standardized 5QI to QoS characteristics mapping will be discussed. As per 3GPP TS <NUM>, the one-to-one mapping of standardized 5QI values to <NUM> QoS characteristics is specified in Table <NUM> shown below. The first column represents the 5QI value. The second column lists the different resource types, i.e., as one of Non-GBR, GBR, Delay-critical GBR. The third column ("Default Priority Level") represents the priority level Priority5QI, for which lower the value the higher the priority of the corresponding QoS flow. The fourth column represents the Packet Delay Budget (PDB), which defines an upper bound for the time that a packet may be delayed between the UE and the N6 termination point at the UPF. The fifth column represents the Packet Error Rate (PER). The sixth column represents the maximimum data burst volume for delay-critical GBR types. The seventh column represents averaging window for GBR, delay critical GBR types.

For example, as shown in Table <NUM>, 5QI value <NUM> is of resource type GBR with the default priority value of <NUM>, PDB of <NUM>, PER of <NUM>, and averaging window of <NUM>. Conversational voice falls under this catogery. Similarly, as shown in Table <NUM>, 5QI value <NUM> is of resource type Non-GBR with the default priority value of <NUM>, PDB of <NUM> and PER of <NUM>. Voice, video (live streaming), and interactive gaming fall under this catogery.

In this section, Radio Resource Management (RRM) will be discussed (a block diagram for an example RRM with a MAC Scheduler is shown in <FIG>). L2 methods (such as MAC scheduler) play a critical role in allocating radio resources to different UEs in a cellular network. The scheduling priority of a UE, i.e., PUE, is determined as part of MAC scheduler as follows:<MAT>.

In the above expression, the parameters are defined as follows:.

In another example variant, the scheduling priority of a UE is determined as follows:<MAT>or as<MAT>.

In yet another example variant, the scheduling priority of a UE is determined as follows:<MAT>or as<MAT>.

The scheduling priority of a UE is based on the maximum logical channel priority value across the logical channels (LCs) of the UE, and the resources allocated to a UE are based on this maximum logical channel priority.

The above-described weights (e.g., W5QI, WGBR, WPDB, WPF) determine the importance of the priority values (P5QI, PGBR, PPDB, PPF). Determining the optimal weights (or substantially optimal weights) that balance the different target parameters (e.g., 5QI priority, target bit rate, packet delay budget, proportional fairness) is difficult, especially in the presence of different traffic types, varying channel conditions, high cell load, etc. The traffic could be different at different times in the day and night, and the traffic density varies with the region and/or location (rural, urban), e.g., there will be heavy traffic in crowded stadiums or malls. Static weights may not consider these variations for UE priority calculation in an optimal manner.

There is also a trade-off between maximizing cell throughput and providing performance guarantees for applications with diverse QoS requirements. An operator with a greater focus on increasing cell throughput may want to choose higher weights for the proportional fairness (i.e., PPF) part in the scheduler metric described above. On the other end, an operator with main focus on providing performance guarantees may want to choose weights that help optimize application performance. Still yet, other operators may want to choose weights to support a good balance between cell throughput and meeting QoS requirements for good number of applications with diverse QoS requirements. These competing considerations make it challenging to select a suitable set of values for these weights.

Patent application <CIT> pertain to next generation (NG) wireless communications. In particular, some embodiments relate to beam compression in NG networks. Patent application <CIT> relates to the technical field of wireless communication, and in particular, to a model training method, a model training device, and a storage medium. Patent application <CIT> relates to artificial intelligence (AI)/machine learning (ML) microservices synchronization, sharing, deployment, and automation, for example, in radio access networks (RANs), such as virtualized RANs (vRANs). Document by <NPL>, discusses an accelerated gradient descent method to expedite the FL convergence, and a compression operator to reduce the communication cost. Document by <NPL>, discusses disaggregating the traditional monolithic control plane (CP) RAN architecture and introduce a RAN Intelligent Controller (RIC) platform decoupling the control and data planes of the RAN driving an intelligent and continuously evolving radio network by fostering network openness and empowering network intelligence with AI-enabled applications. Document titled "<NPL>, provides details on AI/ML lifecycle management including model design, composition, and model runtime access to data and the model deployment solutions. However, the above-mentioned issues are not solved.

Accordingly, there is a need for an enhanced system and method to determine suitable values of the weights used in determining scheduling priority of UEs for policy-based performance management in cellular networks.

The present invention relates to a system and method to achieve enhanced radio resource management (RRM) by utilizing machine-learning-based methods, as defined in the annexed claims.

According to an example embodiment, enhanced RRM utilizing machine-learning-based method is implemented, e.g., to determine suitable values of the weights for determining scheduling priority of UEs for policy-based performance management in cellular networks.

According to an example embodiment, enhanced RRM utilizing machine-learning-based method is implemented at distributed unit (DU) / Centralized unit (CU) or at a RAN Intelligent Controller (RIC) to determine suitable values of the weights for determining scheduling priority of UEs.

According to an example embodiment, enhanced RRM utilizing machine-learning-based method is implemented to determine, e.g., dynamically or in a semi-static manner, suitable values of the weights for determining scheduling priority of UEs.

According to an example embodiment, selection of suitable values of the weights for determining scheduling priority of UEs is linked with the flow-control procedure between Control Unit User Plane (CU-UP) and DU to help improve performance, e.g., for high-load networks).

According to an example method, machine learning techniques are incorporated to improve the determination of the priority of a UE at the Level <NUM> (L2) scheduler of the base station, e.g., by training, deploying, and updating the relevant parameters to provide policy-based determination of the priority of a UE.

According to an example method, machine-learning-assisted technique for determining suitable values of the weights for determining scheduling priority of UEs is implemented in conjunction with a near-real-time RIC (near-RT RIC).

According to an example method, machine-learning-assisted technique for determining suitable values of the weights for determining scheduling priority of UEs is implemented using DU and CU.

According to an example method, the least squares method is utilized to update the weights. The method of least squares is used in regression analysis, which minimizes the sum of the squared errors. As the objective function is a sum of squared errors, which is a convex function, so there exists a minimum, and the weight updating method moves in a direction to get to the error minimum. The weights are updated based on the difference in error between two consecutive samples, which is a tweaked version (or variant) of the gradient descent. The step size utilized in the weight updating determines how fast or slow the method converges to the minimum.

For this application the following terms and definitions shall apply:.

The term "network" as used herein includes both networks and internetworks of all kinds, including the Internet, and is not limited to any particular type of network or inter-network.

The terms "first" and "second" are used to distinguish one element, set, data, object or thing from another, and are not used to designate relative position or arrangement in time.

The terms "coupled", "coupled to", "coupled with", "connected", "connected to", and "connected with" as used herein each mean a relationship between or among two or more devices, apparatus, files, programs, applications, media, components, networks, systems, subsystems, and/or means, constituting any one or more of (a) a connection, whether direct or through one or more other devices, apparatus, files, programs, applications, media, components, networks, systems, subsystems, or means, (b) a communications relationship, whether direct or through one or more other devices, apparatus, files, programs, applications, media, components, networks, systems, subsystems, or means, and/or (c) a functional relationship in which the operation of any one or more devices, apparatus, files, programs, applications, media, components, networks, systems, subsystems, or means depends, in whole or in part, on the operation of any one or more others thereof.

The above-described and other features and advantages of the present disclosure will be appreciated and understood by those skilled in the art from the following detailed description, drawings, and appended claims. In the following, the invention is best understood in view of <FIG> (in particular, steps (<NUM>) to (<NUM>)). The remaining embodiments, aspects and examples disclosed below are included for illustrative purposes and for facilitating the understanding of the invention.

According to an example embodiment of the method according to the present disclosure, multiple parameters are utilized to train the machine-learning (ML) model which can be deployed i) at the DU and/or CU, or ii) at a near-RT RIC. The multiple parameters are computed (and/or acquired) every Tfdbk interval, and the value of each parameter of a single UE calculated and/or acquired in a Tfdbk interval define an "instance". Tfdbk interval is measured in number of time slots. Every Tfdbk, multiple instances (of parameters) are used by the ML model. Although a static Tfdbk is assumed in the present example embodiment, the example method is equally applicable in the case of dynamic intervals.

In the example embodiment, the following RAN-related parameters can be utilized to train the ML model:.

In this section, example embodiment of weight computation method is described in conjunction with <FIG>, which summarizes the overall method. As shown in block <NUM>, the relevant weights (e.g., WGBR, WPDB and WPF) are initialized, i.e., the weights are configured based on some selected guidelines. For example, one can consider the case for which these weights can be common across all UEs or common across UEs within the same QoS class (i.e., 5QI for <NUM> systems), but different for various QoS classes. As shown in box <NUM>, the relevant weights will be used for an interval of Tfdbk at the scheduler, e.g., MAC scheduler, as previously described above. As shown in box <NUM>, the parameters related to the instances in the Tfdbk interval are used to compute the objective function, which is a least square error function. The main objective is to minimize this least square error function, which is sum of the error functions corresponding to PDB, GBR and PF. We compute the overall error function over two consecutive samples, calculate the difference, and update the relevant weights according to a weight updating method based on the difference (as shown in box <NUM>), which updating of the weights is explained in greater detail below.

The updating of the weights can vary on a case-by-case basis. For example, the weights can be changed (increased or decreased) gradually, aggressively or a mix of both based on the application. The updated weights will be used for an interval of Tfdbk and the process repeats. As noted above, initialization of the weights involves configuring the weights based on some selected guidelines, e.g., weight for delay sensitive applications or GBR applications can be configured to be higher than other weights. The weights (WGBR, WPDB, WPF) are to be determined to implement policy-based performance management (e.g., optimize the determination of the priority of UEs (PUE) for a specific scenario). As part of this method, using the parameters that are received from the DU (as previously described above), the relevant weights are optimized by minimizing the proposed error function over K<NUM> training data instances (also referred to as sampling points), K<NUM> ≥ <NUM>, at Tfdbk intervals (which can be static or dynamic), as explained below. We use least square error to compute the errors corresponding to Packet Delay Budget (PDB), Guaranteed Bit Rate (GBR), and PF, as explained below.

For the UE with delay more than it's assigned PDB, we define the PDB error (EPDB (t)) to be the sum of squared difference between the PDB to the delay at the UE over the K<NUM> training data instances. With increase in delay beyond the PDB, PDB error increases to reflect the lag the UE experiences for delay-sensitive applications: <MAT> where <MAT>. For the instances, where the UE-experienced delay ( <MAT>) is within its PDB budget, we consider the corresponding error term as zero. Otherwise, the error is given as square of the deviation from the PDB budget, as represented above. For the above error function, which is used for delay-sensitive applications, we consider a sample set of delays that each packet experiences in a given time interval, and the worst-case delay in the sample set is used for error computation.

If for a UE (with GBR Data Radio Bearer), the achieved bit rate is less than the target guaranteed bit rate (GBR), it means that there is a deficit in the achieved rate, and the DU needs to send data at a higher rate for this UE to compensate for this deficit, which deficit is represented by the corresponding GBR error function (EGBR(t)) given below. The GBR error for the UE with an achieved rate less than the target GBR is the sum of squared difference between the target GBR and the achieved GBR at the UE over the K<NUM> training data instances, as represented below: <MAT> where <MAT> if <MAT>. For the instances in which the UE overachieves (i.e., the achieved GBR rate is more than the target GBR rate), we consider the corresponding error term as zero. Otherwise, the error is taken as the square of deviation from the target GBR rate.

For UEs with different 5QI flows, to reduce the PDB error function, the Physical Resource Blocks (PRBs) allocation should be based on their packet delay budgets (PDBs). For UEs with different kinds of traffic, to reduce the GBR error function, the PRB allocation for the GBR UEs should be according to their guaranteed bit rates (GBRs). Either of these cases may lead to large deviation in the PRBs allocation across UEs. To mitigate this deviation, and to improve fairness of the system, we consider the PF error function (EPF (t)) defined below. The PF error is the sum of squared difference between the average PRBs per UE (e.g., in the network) and the number of allocated PRBs to the UE over the K<NUM> training data instances. <MAT> where <MAT> if <MAT>. For the instances in which the allocated PRBs to the UE are more than the average PRBs per UE across active UEs (e.g., in the network), we consider the corresponding error as zero.

In the above-described error functions for PDB, GBR and PF, t = i * Tfdbk, where i is a positive integer. For example, t=<NUM> gives first Tfdbk interval [<NUM>, Tfdbk], t=<NUM> gives interval from [Tfdbk + <NUM>, <NUM> * Tfdbk], and so on. In accordance with the present disclosure, we optimize the weights to minimize the error function over K<NUM> training data instances at Tfdbk interval (with training data received during [Tfdbk + <NUM>, <NUM> * Tfdbk]). The corresponding error function values EPDB (t + Tfdbk), EGBR(t + Tfdbk), and EPF (t + Tfdbk) are computed in a similar manner. In addition, as mentioned previously, the relevant weights are updated after every Tfdbk interval, and t = i * Tfdbk, where i is a positive integer.

In another example embodiment, a variant of the above-described PF error function can be utilized. In this example embodiment, the total number of PRBs is utilized, instead of average number of PRBs that should be given to each UE in a proportional fair (PF) resource allocation method.

In another example embodiment, another error function (EPF,5QI,j(t)) can be optionally further considered, which additional error function considers deviation in proportional fair (PF) behavior for UEs with DRBs of the same 5QI: <MAT> where <MAT><MAT>if <MAT>, for UEs supporting only DRBs of type 5QI j.

The weights (WGBR, WPDB, WPF) are updated, e.g., as shown in <FIG> (which will be explained in a subsequent section), based on the difference between the current error function and the previous error function, and the UE priorities are determined using the updated weights. The step size Delta is a value between (y1, y2), which is a configurable parameter (with y2 > y1). For example, Delta in (<NUM>,<NUM>) implies a smaller step size, so the weights increase/decrease gradually.

According to an example embodiment of the method, the relevant weights can be common for all UEs in a cell, or the relevant weights can be common for UEs with traffic from the same QoS class (e.g., same 5QI for <NUM> systems).

For the case in which the relevant weights are common across all UEs, if the difference in the error function (for the delay part) increases over consecutive samples, we increase the weights associated with PDB, i.e., WPDB. Increase of difference in error implies the overall delay across UEs is increasing (compared to their PDBs). To mitigate this increase in delay, or to make the UEs to meet their PDBs, WPDB is increased as given below:
<IMG>
In the above expressions, <NUM> < DeltaPDB_stepup < ThreshPDB, i.e., DeltaPDB_stepup is the PDB step size, which is upper-bounded by ThreshPDB.

In another example embodiment, for the case in which the UEs are classified based on the QoS class (i.e., 5QI for <NUM> systems) of the critical DRB, the error function can be redefined as multiple sums over these QoS classes to calculate the error across each class. In this case, the weight updating method is redefined by considering same weights for the UEs having similar 5QI.

For the case in which the weights are common across all UEs, if the difference in the error function for the throughput part of the GBR DRBs increases in consecutive samples, we increase the weights associated with GBR throughput, i.e., WGBR. Increase of difference in error implies the UEs' current sample achieved rate is less than the previous sample achieved rate. To reduce the variance between the achieved bit rate and the target bit rates across UEs, or to make UEs to meet their target GBRs, WGBR is increased as given below:
<IMG>
In the above expressions, <NUM> < DeltaGBR_stepup < ThreshGBR, i.e., DeltaGBR_stepup is the GBR step size, which is upper-bounded by ThreshGBR.

For the case in which the weights are common across all UEs, if difference in the error function (for the PF part) increases over consecutive samples, we increase the weights associated with the PF factor, i.e., WPF. Increase of difference in error implies, across UEs, the variance between average PRBs and allocated PRBs of the current sample is more than that of the previous sample. To reduce the variance between average PRBs and allocated PRBs across UEs, the WPF is increased as given below:
<IMG>
In the above expressions, <NUM> < DeltaPF_stepup < ThreshPF, i.e., DeltaPF_stepup is the step size, which is upper-bounded by ThreshPF.

According to an example embodiment, a weight threshold, WTh, is specified, which is a large value. If any one of the weights (i.e., W5QI, WGBR, WPDB, and WPF) reaches WTh, we adjust the threshold-exceeding weight by subtracting a value corresponding to the minimum value among the weights, and proceed with these new weights. For the 5QI priority, no explicit weight updating is applied. If the 5QI priority is not in the range of other weights, the 5QI priority can be periodically (e.g., function of Tfdbk) reset to a value corresponding to the minimum value among the other three weights.

According to an example embodiment, if one priority relative error (between consecutive samples) is very large compared to other relative errors over consecutive intervals, then the relevant weight can be increased in an aggressive manner till they are in a comparable range. If the relative error is bare minimum over consecutive intervals, then the corresponding weight can be decreased gradually.

Although the above example method has been described as using a constant step size for step-up, an alternative example embodiment can use a dynamically varying step size. In addition, although the above example method has been described in the context of using one logical channel (or DRB) per-UE, the above example method is equally applicable for the case in which a UE supports multiple DRBs. In the case of more than one logical channel (LC) per UE, an example method can consider the LC with the highest priority.

<FIG> is a flowchart summarizing the overall weight updating method discussed above. As shown in box <NUM>, the error functions EPDB, EGBR, and EPF are computed for two consecutive sampling instants. As shown in box <NUM>, for each error function, the difference in error (Delta, or "D") between the two sampling instants is determined. If D is greater than <NUM> (as shown in the decision box <NUM>) for a particular error function, then the corresponding weight is adjusted (e.g., increased) and updated (as shown in box <NUM>). If D is not greater than <NUM>, then no change in weight is implemented (as shown in box <NUM>).

In this section, an example embodiment of a method for policy-based performance management in an O-RAN system using an RIC will be discussed, in conjunction with <FIG> and <FIG>. As part of this method, the following parameters are sent to the near-real-time RAN Intelligent Controller (near-RT RIC) from the RAN over the E2 interface to train the machine learning (ML) model that is deployed at the near-real-time RIC. These parameters are forwarded from the RAN to the near-RT RIC at Tfdbk interval, and the value of each parameter of a single UE acquired in a Tfdbk interval define an "instance". Tfdbk interval is measured in number of time slots. Every Tfdbk, multiple instances (of parameters) are sent to the near-RT RIC. Although a static Tfdbk is assumed in the present example embodiment, the example method is equally applicable in the case of dynamic intervals.

In the example embodiment, the following RAN-related parameters can be communicated to the near-RT RIC via E2 interface (by adding suitable objects in the E2 protocol):.

As shown in <FIG>, which is a signal flow diagram of E2AP SUBSCRIPTION procedure utilized for policy-based performance management in an O-RAN system using a RIC module, the example method starts with an E2 setup procedure <NUM> between the E2 Node <NUM> (which encompasses DU and CU) and the near-RT RIC <NUM>, which E2 setup procedure is as specified in the O-RAN and 3GPP specifications. The next step <NUM> involves the RIC subscription procedure including a sequence of actions: i) Near-RT RIC requests a SUBSCRIPTION from the E2 Node <NUM> for REPORT, with the corresponding Event Trigger (e.g., periodic trigger), and the E2 Node <NUM> acknowledges the SUBSCRIPTION (generally referenced by the process arrow <NUM>); and ii) Near-RT RIC requests a SUBSCRIPTION from the E2 Node <NUM> for POLICY, with the corresponding Event Trigger (e.g., periodic trigger), and the E2 Node <NUM> acknowledges the SUBSCRIPTION (generally referenced by the process arrow <NUM>). In this manner, the Near-RT RIC subscribes to the above-listed parameters from the E2 Node (DU and/or CU) and provides the triggers for the E2 Node to communicate the subscribed information. Subsequently, the E2 Node (DU and/or CU) detects the RIC event trigger (as shown in box <NUM>). In the case of the REPORT trigger event occurring, the REPORT service sends the above-listed RAN-related DU and/or CU parameters from the E2 Node <NUM> (DU and/or CU) to the Near-RT RIC <NUM> in an RIC INDICATION report (as shown by the process arrow <NUM>). The Near-RT RIC uses the received information to implement weight updating to obtain (compute) new weights as discussed above. In parallel, DU continues to run radio resource management methods (including scheduling methods) with the latest set of weights it had received before the current time, as shown in box <NUM>. Once the event corresponding to the POLICY action is triggered at the Near-RT RIC <NUM> (which occurs when the scheduler weights are updated at the near-RT RIC <NUM>), the near-RT RIC <NUM> communicates the updated weights to the E2 Node (i.e., DU in this case), and the E2 Node (i.e., DU) modifies the scheduler process according to information contained in the POLICY description (i.e., new weights are provided to the scheduler), as summarized in box <NUM>.

We now turn to <FIG>, which illustrates a signal flow diagram of an example embodiment of a method for enhanced RRM utilizing machine-learning-based technique at a RAN Intelligent Controller (RIC) to determine suitable values of the weights for determining scheduling priority of UEs. In the initial training phase (referenced by <NUM>), various RAN-related parameters previously listed above and have been collected (e.g., weights of various priority values for GBR, PDB and PF, etc.) are used to train a machine learning (ML) model, which can be run at non-RT RIC <NUM>. As referenced by <NUM>, the trained ML model is deployed at near-RT RIC <NUM>, where the training of the ML model continues. The trained ML model has trained weights for the various scheduling parameters, e.g., the priority weights WGBR, WPDB, and WPF. Alternatively, equal weights for the priority weights can be used initially. As referenced by <NUM>, the trained weights (or equal weights, if so chosen) from the near-RT RIC <NUM> are deployed at CU 131a and/or DU 131b. As referenced by <NUM>, at Tfdbk interval, the above-listed RAN-related parameters are reported from DU 131b and/or CU 131a to the near-RT RIC <NUM>.

Continuing with <FIG>, as referenced by <NUM>, the near-RT RIC implements the weight updating method discussed above, i.e., the relevant weights are optimized by minimizing the proposed error function over K<NUM> training data instances at Tfdbk intervals. The parameters related to the instances in this Tfdbk interval are used to compute the objective function, which is a least squared error function. The main objective is to minimize this least square error function which is sum of the error functions corresponding to Packet Delay Budget (PDB), Guaranteed Bit Rate (GBR), and PF factors. Each error function defined corresponding to the PDB, GBR and PF factor is a positive term which is squared errors summed over K<NUM> training instances. We compute the overall error function over two consecutive samples, calculate the difference and update weights based on this difference. The objective function can be expressed as follows: <MAT> As referenced by <NUM>, the new, updated weights computed at the near-RT RIC <NUM> are deployed at the DU 131b. Process steps referenced by <NUM>, <NUM> and <NUM> are repeated.

In another example embodiment, a network operator can specify at least one desired policy, which is used along with the previous example method to optimize the values for the scheduler weights. In one example policy, the operator can specify a higher preference for increased cell throughput and accept performance degradation for some UEs and their associated applications. In this case, DeltaPF_stepup described above (in connection with weight increase) can be chosen to be higher than the usual DeltaPF_stepup, or WPF can be increased in a more aggressive way. In another example policy, the operator can give a higher preference to delay sensitive applications. In this case, DeltaPDB_stepup can be chosen to be higher than the usual value, or WPDB can be increased more aggressively.

While the present disclosure has been described with reference to one or more exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the present disclosure. For example, although the example methods have been described in the context of <NUM> cellular networks, the example methods are equally applicable for <NUM> and other similar wireless networks. Furthermore, example methods described herein can be implemented i) at an RIC in conjunction with DU and/or CU, or ii) at DU and/or CU only (if enough processing resources available at DU/CU). In addition, many modifications may be made to adapt a particular situation or material to the teachings of the disclosure without departing from the scope thereof. Therefore, it is intended that the present disclosure not be limited to the particular embodiment(s) disclosed as the best mode contemplated, but that the disclosure will include all embodiments falling within the scope of the appended claims.

Claim 1:
A method for implementing enhanced radio resource management of radio access network, RAN, based on machine-learning-based technique, characterized by comprising the steps of:
deploying, from a RAN Intelligent Controller, RIC, to a distributed unit, DU, of the RAN, a trained machine-learning-based model trained on a plurality of RAN-related parameters for a selected user equipment, UE, on the RAN, wherein the trained machine-learning-based model comprises the plurality of RAN-related parameters including i) plurality of trained weights for determining scheduling priority of the selected UE, ii) a network operator policy influencing scheduling priority of the selected UE, and iii) at least one of Packet Delay Budget, PDB, a target guaranteed bit rate, GBR, and proportional fair, PF, metric of the selected UE;
sending, from the DU to the RIC, values of the plurality of RAN-related parameters for the selected UE observed at least at a first sampling time point and a sequentially following second sampling time point;
computing (<NUM>; <NUM>), at the RIC, a difference between <NUM>) a first value of overall error function for PDB, GBR, and PF calculated based on corresponding observed values at the first sampling time point, and <NUM>) a second value of overall error function for PDB, GBR, and PF calculated based on corresponding observed values at the second sampling time point; and
updating (<NUM>; <NUM>), at the RIC, the plurality of weights based on the difference between the first and second values of the overall error function.