WO2026030443A1 - Optimisation de décisions de gestion de ressources radio dans des réseaux o-ran à l'aide d'informations prédites - Google Patents

Optimisation de décisions de gestion de ressources radio dans des réseaux o-ran à l'aide d'informations prédites

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Publication number
WO2026030443A1
WO2026030443A1 PCT/US2025/039866 US2025039866W WO2026030443A1 WO 2026030443 A1 WO2026030443 A1 WO 2026030443A1 US 2025039866 W US2025039866 W US 2025039866W WO 2026030443 A1 WO2026030443 A1 WO 2026030443A1
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Prior art keywords
predicted
csi
user
metric
average
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Marri Vineeth REDDY
Mukesh Taneja
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Mavenir Systems Inc
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Mavenir Systems Inc
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G06N3/0455—Auto-encoder networks; Encoder-decoder networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/373—Predicting channel quality or other radio frequency [RF] parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/0626—Channel coefficients, e.g. channel state information [CSI]

Definitions

  • O-RAN Open Radio Access Network
  • RRM radio resource management
  • a method for a system executing AIML models comprising: computing scheduler metric of each logical channel PLC using, where for an User Equipment (UE) Channel State Indicator (CSI) at time instant is given by and predicted CSI for next slots is given by , , wherein CSI comprises a Channel Quality Indicator (CQI) and a Rank Indicator (RI).
  • the scheduler metric can further comprise: a Priority metric for each user (or UE) with respect to its own predicted CSI for a future time interval ( for user i); and a Priority metric of a user relative to predicted CSI of all other users in that cell; a Priority metric for each user with respect to its predicted CSI.
  • (t, t+ ) can take a higher value when CSI at time t, is greater than or equal to its average predicted CSI for time interval (t,t+m), , , so that the RRM attempts to serve packets for this LC i for the corresponding UE i at time t; or (t, t+ ) can take a lower value, when CSI at 64K3052 time t, is less than its average predicted CSI for time interval (t,t+m), , . so that the RRM attempts to delay serving packets for this LC i (for the corresponding UE i) beyond time t.
  • the method can further comprise: computing average predicted CSI values , for each user i among the users and computing a minimum value among the users be , corresponding to user and the maximum value be , corresponding to user; and calculating a range as , , .
  • BO Buffer Occupancy
  • the method can comprise: computing quantized values for , , and .
  • the method can further comprise: adjusting the values weights used to compute an overall priority metric P for LC I using predicted CSI and BO values, and computing g the metrics P , using P and P .
  • the method can further comprise: computing the AIML model is located at a Near-RT-RIC and , , and are computed at the Near-RT-RIC and communicated to a DU.
  • the method can further comprise computing , , and at the CU-CP and communicated to a DU via an F1AP protocol running over an F1-C interface between a CU- CP and a DU.
  • a system configured to execute AIML model, the system being configured to: compute a scheduler metric of each logical channel P LC , where for an User Equipment (UE) Channel State Indicator (CSI) at time instant is given by and predicted CSI for next slots is given by , , wherein CSI comprises a Channel Quality Indicator (CQI) and a Rank Indicator (RI).
  • the scheduler metric can further comprise: a Priority metric for each user (or UE) with respect to its own predicted CSI for a future time interval ( for user i); and a Priority metric of a user relative to predicted CSI of all other users in that cell; a Priority metric for each user with respect to its predicted CSI.
  • the system can be further configured to: predict, by the AIML model, a Buffer Occupancy (BO) for each LC in RLC queue as well.
  • BO Buffer Occupancy
  • FIG. 1a is a block diagram of a system architecture.
  • FIG. 1b shows an example of a User Plane Stack.
  • FIG. 2 shows an example of a Control Plane Stack.
  • FIG. 3 shows an example of high-level NG-RAN including a gNB CU and DU.
  • FIG.4 shows an example of a Separation of CU-CP (CU-Control Plane) and CU- UP (CU-User Plane) in a 5G gNB.
  • FIG. 5 shows a DL (Downlink) Layer 2 Structure.
  • FIG. 6 shows an exemplary logical flow for implementing an RB allocation policy.
  • FIG. 7 shows an L2 Data Flow example.
  • FIG. 8a shows an example of an O-RAN architecture.
  • FIG. 8b shows a logical flow for an O-RAN architecture.
  • FIG. 8b shows a logical flow for an O-RAN architecture.
  • FIG. 9 illustrates a PDU Session architecture comprising of multiple DRBs and multiple QoS Flows.
  • FIG. 10 illustrates a PDU Session flow comprising multiple DRBs. 64K3052
  • FIG. 11 illustrates a CU and DU view on PDU session, DRBs and GTP-U tunnels for a 5G network architecture.
  • FIG. 12 illustrates a logical flow for an AIML supported scheduler metric flow.
  • FIG. 13 logical flow for quantized values.
  • FIG. 14a shows flow for a computation of a quantized value.
  • FIG. 14b shows a flow for a computation of a quantized value.
  • FIG. 14b shows a flow for a computation of a quantized value.
  • NG-RAN Next Generation Radio Access Network
  • NR 5G New Radio
  • PDU layer 9010 corresponds to the PDU carried between the UE 101 and the data network (DN) 9011 over the PDU session.
  • UE 101 is connected to the 5G access network (AN) 902, which AN 902 is in turn connected via the N3 interface to the Intermediate UPF (I- UPF) 903a portion of the UPF 903, which I-UPF 903a is in turn connected via the N9 interface to the PDU session anchor 903b portion of the UPF 903, and which PDU session anchor 903b is connected to the DN 9011.
  • the PDU session can correspond to IPv4, IPv6, or both types of IP packets, when the PDU session is of type IPv4, IPv6 or IPv4v6, respectively.
  • GTP-U shown in FIG.1b supports tunnelling user plane data over N3 and N9 interfaces and provides encapsulation of end user PDUs for N3 and N9 interfaces.
  • 64K3052 For the control plane, as shown in FIG. 2 in accordance with 3GPP TS 38.300, RRC (Radio Resource Control), PDCP, RLC, MAC and PHY sublayers originate in the UE 101 and are terminated in the gNB 102 on the network side, and NAS (Non-Access Stratum) originate in the UE 101 and is terminated in the AMF (Access Mobility Function) 103 on the network side.
  • RRC Radio Resource Control
  • PDCP Packet Control
  • RLC Radio Link Control
  • MAC Network Control Protocol
  • MAC Non-Access Stratum
  • NG-Radio Access Network (NG-RAN) architecture from 3GPP TS 38.401 is shown in FIGS.3-4.
  • the NG-RAN 301 comprises of a set of gNBs 302 connected to the 5GC 303 through the NG interface.
  • Each gNB comprises gNB-CU 304 and one or more gNB-DU 305 (see FIG.3).
  • 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 305
  • F1-U is the interface between gNB-CU-UP 304b and gNB-DU 305.
  • gNB 302 comprises a gNB-CU-CP 304a, multiple gNB-CU-UPs (or gNB-CU-UP instances) 304b and multiple gNB-DUs (or gNB-DU instances) 305.
  • a gNB-DU 305 is connected to gNB-CU-CP 304a
  • gNB-CU-UP 304b is connected to gNB-CU-CP 304a.
  • F1-AP F1-Application Protocol
  • NR-U NR User Plane
  • L2 of 5G NR is split into the following sublayers in accordance with 3GPP TS 38.30: [0039] 1) Medium Access Control (MAC) 501 in FIGS. 5-7: Logical Channels (LCs) are SAPs (Service Access Points) between the MAC and RLC layers. This layer runs a MAC scheduler to schedule radio resources across different LCs (and their associated radio bearers). For the downlink direction, the MAC layer processes and sends RLC PDUs received on LCs to the Physical layer as Transport Blocks (TBs).
  • MAC Medium Access Control
  • LCs Logical Channels
  • SAPs Service Access Points
  • Radio Link Control (RLC) 502 in FIGS. 5-7 The RLC sublayer presents RLC channels to the Packet Data Convergence Protocol (PDCP) sublayer.
  • the RLC sublayer supports three transmission modes: RLC-Transparent Mode (RLC-TM), RLC- Unacknowledged Mode (RLC-UM) and RLC-Acknowledgement Mode (RLC-AM).
  • RLC configuration is per logical channel. It hosts ARQ (Automatic Repeat Request) protocol for RLC-AM mode.
  • FIG. 5 is a block diagram illustrating DL L2 structure, in accordance with 3GPP TS 38.300.
  • FIG.6 is a block diagram illustrating UL L2 structure, in accordance with 3GPP TS 38.300.
  • FIG.7 is a block diagram illustrating L2 data flow example, in accordance with 3GPP TS 38.300 (in FIG.7, H denotes headers or sub-headers).
  • Open Radio Access Network 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.8.
  • RIC Radio Intelligent Controller
  • FIG.8 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 802) 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 can host 24 cells) and each cell can support many users. For 64K3052 example, one cell can support 800 Radio Resource Control (RRC)-connected users and out of these 800, there can be 250 Active users (i.e., users that have data to send at a given point of time).
  • RRC Radio Resource Control
  • a cell site can comprise multiple sectors, and each sector can support multiple cells.
  • one site can comprise three sectors and each sector can support eight cells (with each cell being on a different frequency band in a given sector).
  • One CU-CP (CU-Control Plane) can support multiple DUs and thus multiple cells.
  • a CU-CP can support 500 cells and around 100,000 User Equipment (UEs).
  • Each UE can support multiple Data Radio Bearers (DRBs) and there can be multiple instances of CU-UP (CU-User Plane) to serve these DRBs.
  • DRBs Data Radio Bearers
  • CU-UP CU-User Plane
  • each UE can support 4 DRBs, and 400,000 DRBs (corresponding to 100,000 UEs) can be served by five CU-UP instances (and one CU-CP instance).
  • the DU can be located in a private data center, or it can be located at a cell- site.
  • the CU can 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, can be tens of kilometers apart.
  • the CU communicates with a 5G core system, which can also be hosted in the same public cloud system (or can be hosted by a different cloud provider).
  • a RU (Radio Unit) (shown as O-RU 803 in FIG.8) is located at a cell-site and communicates with the DU via a front-haul (FH) interface.
  • FH front-haul
  • the E2 nodes (CU and DU) are connected to the near-real-time RIC 132 using the E2 interface.
  • the E2 interface is used to send data (e.g., user and/or cell KPMs) from the RAN, and deploy control actions and policies to the RAN at near-real-time RIC 132.
  • the applications or services at the near-real-time RIC 132 that deploys the control actions and policies to the RAN are called xApps.
  • the E2 node advertises the metrics it can expose, and an xApp in the near-RT RIC can send a subscription message specifying key performance metrics which are of interest.
  • the near- real-time RIC 132 is connected to the non-real-time RIC 133 (which is shown as part of Service Management and Orchestration (SMO) Framework 805 in FIG.8) using the A1 interface.
  • the applications that are hosted at non-RT-RIC are called rApps.
  • O-eNB 806 which is shown as being connected to the near-real-time RIC 132 and the SMO Framework 805) and O-Cloud 804 (which is shown as being connected to the SMO Framework 805).
  • E2 node which is DU or CU
  • Near-RT-RIC establish E2 session using E2 SETUP REQUEST and E2 SETUP RESPONSE.
  • Near-RT-RIC can subscribe to certain parameters from the E2 node (on behalf the xApp running at Near-RT-RIC) using the RIC SUBSCRIPTION REQUEST and E2 node acknowledges this message by sending RIC SUBSCRIPTION RESPONSE to the Near-RT-RIC.
  • xApp running at the Near- RT-RIC also provides the event triggers to E2 node, e.g. it can ask E2 node to REPORT subscribed parameters periodically to the xApp or to REPORT these subscribed parameters based on certain events to the xApp.
  • E2 node communicates subscribed parameters to Near-RT-RIC (and the xApp) using RIC INDICATION as shown in FIG.8b.
  • Near- RT-RIC can send RIC CONTROL REQUEST to take an action at the E2 node (e.g. influence mobility decision).
  • E2 node acknowledges this message by sending RIC CONTROL ACKNOWLEDGE to Near-RT-RIC while E2 node takes action as asked by the Near-RT-RIC.
  • PDU sessions, DRBs, and Quality of Service (QoS) flows EW are described.
  • PDU connectivity service is a service that provides exchange of PDUs between a UE and a Data Network (DN) identified by a Data Network Name (DNN).
  • DN Data Network
  • DNN Data Network Name
  • the PDU Connectivity 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 (5G QoS Identifier).
  • a PDU session includes the following: Data Radio Bearers which are 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.9 illustrates an example PDU session (in accordance with 3GPP TS 23.501) comprising multiple DRBs, where each DRB can include of multiple QoS flows.
  • FIG.9 three components are shown for the PDU session 901: UE 101; access network (AN) 902; and UPF 903, which includes Packet Detection Rules (PDRs) 9031.
  • PDRs Packet Detection Rules
  • 64K3052 A 3GPP 5G network architecture is illustrated in FIG. 10.
  • the transport connection between the DU 305 and the CU-UP 304b of FIG. 11 uses a single GTP-U tunnel per DRB (see also FIG.10 and FIG.11).
  • the DU is provided with an UL GTP-U TEID and the CU is provided with the corresponding DL GTP-U TEID to allow for data communication for that DRB between DU and CU-UP.
  • SDAP [0055] a) The SDAP (Service Adaptation Protocol) 504 Layer receives downlink data from the UPF 903 across the NG-U interface (see FIG.11). [0056] b) The SDAP 504 maps one or more QoS Flow(s) onto a specific DRB.
  • the SDAP header is present between the UE 101 and the CU (when reflective QoS is enabled) and includes a field to identify the QoS flow within a specific PDU session.
  • GTP-U protocol includes a field to identify the QoS flow and is present between CU and UPF 903 (in the core network).
  • One (logical) DU (or RLC) queue exists per DRB (or per logical channel) for RLC PDUs that are to be transmitted for the first time, as shown in FIG.11. Separate logical queues can exist in DU for packets that are to be retransmitted to UE.
  • 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 can be delayed between the UE and the N6 termination point at the UPF.
  • PDB Packet Delay Budget
  • the fifth column represents the Packet Error Rate (PER).
  • the sixth column represents the maximum data burst volume for delay-critical GBR types.
  • the seventh column represents averaging window for GBR, delay critical GBR types. Note that only a subset of 5QI values defined in 3GPP TS 23.501 are shown in Table 1 below. [0061] For example, as shown in Table 1, 5QI value 1 is of resource type GBR with the default priority value of 20, PDB of 100ms, PER of 0.01, and averaging window of 2000 ms. Conversational voice falls under this category. Similarly, as shown in Table 1, 5QI value 7 is of resource type Non-GBR with the default priority value of 70, PDB of 100ms and PER of 0.001. Voice, video (live streaming), and interactive gaming fall under this category.
  • PER Packet Error Rate
  • RRM Radio Resource Management
  • P5QI is the priority metric corresponding to the QoS class (5QI) of the logical channel. Incoming traffic from a DRB is mapped to Logical Channel (LC) at RLC level.
  • P5QI is a function of the default 5QI priority value, Priority 5QI , of a QoS flow that is mapped to the current LC. The lower the value of Priority 5QI the higher the priority of the corresponding QoS flow. For example, Voice over New Radio (VoNR) (with 5QI of 1) will have a higher P 5QI compared to web browsing (with 5QI of 9).
  • VoIP Voice over New Radio
  • PGBR is the priority metric corresponding to the target bit rate of the corresponding logical channel.
  • the GBR metric PGBR represents the fraction of data that must be delivered to the UE within the time left in the current averaging window Tavg_win (as per 5QI table, default is 2000 msec.) to meet the UE’s GBR requirement.
  • PGBR remData/targetData 64K3052
  • targetData is the total data bits to be served in each averaging window Tavg_win in order to meet the GFBR (Guaranteed Flow Bit Rate) of the given QoS flow
  • remData is the amount of data bits remaining to be served within the time left in the current averaging window
  • P GBR is reset to 1 (or some other suitable value) at the start of each averaging window T avg_win , and should go down to 0 towards the end of this window if the GBR criterion is met
  • P GBR 0 for non-GBR flows.
  • each (time) slot can be equal to 1 ms or 0.5 ms.
  • ‘Slot’ and ‘time slot’ are used interchangeably in this document.
  • Packet delay budget at DU is denoted as PDBDU. Waiting time for HoL RLC packet for DRB m corresponding to UE h at time t (i.e.
  • PPF is the priority metric corresponding to proportional fair metric of the UE.
  • 1_ is upper bounded by 1_ , and lower bounded by zero.
  • achievable data rate in slot t is denoted as r(h; t) and this is influcned by CSI reported by UE h for slot t which is denoted as CSI(h; t).
  • UE’s weighted average throughput for UE h at the beginning of slot t or at the end of the slot (t- 1) is denoted as Ravg(h; t-1).
  • BO is the (normalized) buffer occupancy in the RLC queue (e.g. in the RLC queue at DU for traffic in downlink direction for a DRB).
  • PBO is the normalized value of buffer occupancy across all DRBs which can be proportional to the value of BO.
  • RLC queue (normalized) BO for DRB m corresponding to UE h at time t is denoted as RLCBO(h, m; t).
  • W5QI is the weight of P5QI
  • WGBR is the weight of PGBR
  • WPDB is the weight of PPDB
  • WBO is the weight of PBO
  • WPF is the weight of PPF.
  • each of the above weights can be set to a value between 0 and 1 though other suitable set of values can be chosen too.
  • UE reports BSR (Buffer Status Report) to DU based on the data which is waiting in the UL queues at the UE and this BSR can be used to estimate the (UL) BO.
  • BSR Buffer Status Report
  • LCGs Logical Channel Groups
  • one LCG can be used for GBR traffic and another GBR can be used for non-GBR traffic.
  • a separate LCG can be used for signalling traffic.
  • remData(h,m;t) and targetData(h,m;t) at time t are computed by monitoring data received in the UL direction from the UE at the DU.
  • the PF metric for a UE can be computed using the MCS and the corresponding TB (Transport Block) size for the UL direction for that UE.
  • the weighted average throughput (Ravg) for the UL traffic from each UE can be computed at the DU by monitoring UL traffic from that UE.
  • RNNs Recurrent neural networks
  • RNNs are a family of neural networks that are 64K3052 suited for handling sequential data.
  • RNNs use a hidden state associated with each time-step and the output at each time step is computed using the input and the previous hidden state.
  • RNN architectures suffer from some limitations. First, RNNs fail to store information for a long period of time and thus do not handle the situations well in which a reference to certain information stored quite a long time ago is required to predict the current output.
  • LSTM Long Short-Term Memory
  • LSTM The basic difference between the architectures of RNNs and LSTMs is that the hidden layer of LSTM is a gated unit or a gated cell, which has four layers that interact with one another in a way to produce the output of that cell along with the cell state. The output and the cell state are then passed onto the next hidden layer.
  • LSTMs comprise three logistic sigmoid gates and one tanh layer. It should be noted that output of sigmoid activation function is in the range (0 to 1) for any real value as input, while the output of tanh is in the range (-1 to 1).
  • Gates are provided in LSTM in order to limit the information that is passed through the cell, i.e., the gates determine which part of the information will be needed by the next cell and which part is to be discarded.
  • the output is usually in the range of 0 to 1, where “0” means “reject all”, and “1” means “include all”.
  • Information is retained by the cells, and the memory manipulations are done by the gates.
  • Three gates are provided in LSTM: 1) forget gate; 2) input gate; and 3) output gate.
  • DNNs Deep Neural Networks
  • MLP Multi-Layer Perceptron
  • DNNs 64K3052 train the model to learn the dependencies between the target and the independent variables from a dataset.
  • These architectures have three types of layers: input layer, hidden layers and output layer. DNNs can map nonlinear input to output by extracting subtle patterns and multiple features from a dataset through each layer. [0085]
  • the input layer processes the input data and passes it to the hidden layer.
  • Each hidden layer processes the output from the previous layer and passes it to the next layer. Activation functions such as ReLU, sigmoid and tanh are used.
  • the output layer produces the DNN result.
  • Weights used in the DNN architecture are usually updated using gradient descent process where the gradient indicates how the weights should change in order to reduce the error or loss (i.e.
  • the training process is repeated iteratively to continuously reduce the overall error (or loss) until the error (or loss) is below a predefined threshold.
  • a DNN Once a DNN is trained, it can compute the output of the network using the weights derived during the training process.
  • channel conditions can vary depending on many factors such as atmospheric conditions, multi-path propagation, and mobility. So, mere interpolation of channel estimates will not promise optimal performance.
  • traffic conditions vary for services like video streaming, live streaming and the like.
  • AIML techniques such as LSTM or Transformer based DNN architectures
  • One such method is presented in Indian Patent application number 202321076223 (having a filing date November 8, 2023), incorporated in its entirety by reference hereby.
  • AIML techniques such as deep neural networks
  • CSI channel state information
  • 64K3052 [0090] CSI and BO play a key role in RRM related decisions for UEs in a cell.
  • AIML module may not be able to predict values of some of these parameters very accurately. For example, it can happen when the AIML model is not trained with sufficient data (e.g. during the initial deployments). Also, values of the predicted parameters can become stale, and it is important to take right near-optimal RRM decisions for such scenarios also. Implementations as described herein advantageously address these issues.
  • FIG.12 shows an implementation of an AIML supported scheduler metric flow configured take into account predicted BO and predicted CSI to optimize RRM in O-RAN systems.
  • RRM is enhanced to take into account predicted BO and predicted CSI to optimize radio resource management in O-RAN systems.
  • CSI comprises CQI (Channel Quality Indicator), RI (Rank Indicator) and other parameters.
  • CQI Channel Quality Indicator
  • RI Rank Indicator
  • CSI as used herein denotes CQI part of CSI, but can also denote another number derived from CQI, RI and optionally other parameters.
  • This method considers the following when computing scheduler metric related to predicted CSI: - (a) Priority metric for each user (or UE) with respect to its own predicted CSI for a future time interval - (b) Priority metric of a user relative to predicted CSI of all other users in that cell [0099] (a) Priority metric for each user with respect to its predicted CSI (denoted as for user i) is computed as follows: - Calculate average of predicted CSI over slots for each user, denoted as , .
  • (t, + ) is the priority metric for LC i (corresponding to UE i) with respect to predicted CSI where predicted CSI is available for the time interval (t, t+ ).
  • This metric, (t, t+ ) is also denoted as in this description here.
  • (t, t+ ) takes higher value (i.e., greater than or equal to one), when CSI at time t, is than (or equal to) its average predicted CSI for time interval (t,t+m), , .
  • the optimized RRM method given here attempts to serve packets for this LC i (for the corresponding UE i) at time t if possible, as average CSI for user i is expected to become worse beyond time t.
  • (t, t+ ) takes a lower value (i.e. less than one), when CSI at time t, is less than its average predicted CSI for time interval (t,t+m), , .
  • this optimized RRM method given here attempts to 64K3052 opportunistically delay serving packets for this LC i (for the corresponding UE i) beyond time t if possible, as average CSI is expected to improve beyond time t.
  • Priority metric with respect to predicted CSI relative to all the other users is computed as follows: - Sort out average predicted CSI values (i.e. , considering each user i) among the users. - Let the minimum value among the users be , corresponding to user and the maximum value be , corresponding to user.
  • each LC in the UE has its own (logical) RLC queue and thus its own BO.
  • the optimized RRM method is described here assuming that there is one LC per-UE.
  • LC refers to UE in the description here. Note that this method is equally applicable for the case when there are multiple LCs communicating data in that UE. For example, there can be three LCs, denoted as i(1), i(2),i(3), for UE (or user) i and this method works for such cases also.
  • AIML models are used to predict Buffer Occupancy (BO) for each LC in RLC queue as well.
  • BO buffer occupancy
  • (t, t+ ) is the priority metric for LC i with respect to predicted BO where predicted BO is available for the time interval (t, t+ ).
  • This metric, (t, t+ ) is also denoted as in this description here. 64K3052 [00112]
  • (t, t+ ) takes higher value (i.e., greater than or equal to one), when BO at time t, , is greater than its average predicted BO for time interval (t, t+ ), , .
  • the optimized RRM method given here attempts to serve packets for this LC i (for the corresponding UE i) at time t if possible or it can opportunistically delay serving packets for this LC beyond t as somewhat lower number of new RLC packets expected during the time interval (t, t+ ) for this LC in the DU.
  • (t, t+ ) takes a lower value (i.e. less than one), when BO at time t, is less than its average predicted BO for time interval (t, t+ ), , .
  • this optimized RRM method given here attempts to serve packets for this LC i (for the corresponding UE i) at time t if possible as BO expected to further increase during the time interval (t, t+ ) for this LC in the DU.
  • (b) Priority metric with respect to predicted BO relative to all the other LCs is computed as follows: [00115] Sort out average predicted BO values among the LCs. Let the minimum value among the LCs be , corresponding to LC and the maximum value be , corresponding to LC. [00116] Calculate range as ( , , ).
  • this method used predicted BO for applications such as video streaming (e.g., for 5QI 9, 8 or 6 bearers in 5G network), video conferencing and AR / VR (Augmented Reality / Virtual Reality). It does not need to use predicted BO for low data rate applications such as voice over 5G NR.
  • This method computes an enhanced scheduling metric, , , using PLC,i and other factors computed using predicted CSI and predicted BO as described above. As before certain number of LCs with the maximum value of , are selected in each time slot and resources are distributed for these LCs.
  • This RRM method opportunistically tries to delay serving RLC packets for a logical channel based on certain conditions, such as CSI of this UE expected to improve in next few time slots or less number of new RLC packets expected in next few time slots (and thus BO of this LC not expected increase significantly) or this UE expected to get better CSI in next few slots compared to other UEs in the cell or the BO in next the few slots expected to be less compared to other LCs (belonging to different UEs) in the cell.
  • certain conditions such as CSI of this UE expected to improve in next few time slots or less number of new RLC packets expected in next few time slots (and thus BO of this LC not expected increase significantly) or this UE expected to get better CSI in next few slots compared to other UEs in the cell or the BO in next the few slots expected to be less compared to other LCs (belonging to different UEs) in the cell.
  • this RRM method opportunistically tries to serve RLC packets for a logical channel at current slot based on certain conditions, such as CSI of this UE expected to degrade in the next few slots or higher number of new RLC packets expected in next few time slots (and thus BO of this LC expected to increase significantly) or this UE expected to get worse CSI in next few slots compared to other UEs in the cell or the BO in next the few slots expected to be more compared to other LCs (belonging to different UEs) in the cell.
  • certain conditions such as CSI of this UE expected to degrade in the next few slots or higher number of new RLC packets expected in next few time slots (and thus BO of this LC expected to increase significantly) or this UE expected to get worse CSI in next few slots compared to other UEs in the cell or the BO in next the few slots expected to be more compared to other LCs (belonging to different UEs) in the cell.
  • CSI is predicted over the time interval (t, t+m) for UE i and BO is predicted for LC i (corresponding to UE i) over the time interval (t, t+ ).
  • ‘m’ and ‘ can be different.
  • average CSI for UE i is computed using predicted CSI values for the time interval (t, t+m), and average BO for LC i (corresponding to UE i) is computed using the time interval (t, t+ ) even if ‘m’ and ‘ ’ are different. In this case, it is also ensured that each of ‘m’ and ‘ ’ are less than a pre-specified threshold.
  • METHOD IB [00130] This method uses quantized values for , , and .
  • AIML models to predict CSI and BO can be deployed at other nodes (such as as CU-CP or at Near-RT-RIC or another aalytics server).
  • M and M results in this method
  • FIG. 13 shows a logical flow for quantized values for , , and .
  • Step (1) in FIG. 13 shows computation of quantized value of and Step (2) shows computation of .
  • Step (3) of FIG.13 shows computation of quantized value of and FIGS.14a-14b show computation of quantized value of .
  • Step (5) in FIG.13 shows computation of quantized value of and Step (6) shows computation of .
  • Overall scheduling metric, , is computed as part of Step (7).
  • PLC,i is the scheduling metric for LC i as defined earlier. As described earlier, this depends on current and past values of some parameters.
  • New scheduling metric for LC i, , depends on past, current and predicted values of certain parameters.
  • M ETHOD IC This method further reduces the communication overhead of previous methods between the node where AIML models are hosted and the DU. It is also useful for scenarios where required hardware and software resources to run previous methods are not available at the DU. This method also considers the errors in prediction and takes corrective action while computing overall scheduling metric for each LC. [00163] In some scenarios, some of the above predicted values can go stale or the prediction accuracy may not be very good for certain time intervals. Base station and other modes provide feedback to the AI-ML models to improve the accuracy of predicted CSI and predicted BO but it can take some time to update the models to improve the accuracy. The optimized RRM method described here adjusts the values of certain weights which are used to compute an overall priority metric using predicted CSI and BO values.
  • This overall priority metric is denoted as P for LC i. 64K3052
  • This method computes the metric, P , using P and P as shown in Table 3.
  • P can be either positive or negative depending on the prediction accuracy of BO and is a step value to influence the value of P .
  • predicted BO is not very accurate for a time interval (e.g. when AIML model is not trained with sufficient data or when a new type of application starts communicating data)
  • a negative value of is used to reduce the impact of the value of predicted BO on the metric, P .
  • value of can be varied to further increase or decrease impact of on the metric, P .
  • P can be either positive or negative depending on the prediction accuracy of CSI and is a step value to influence the value of P .
  • a negative value of is used to reduce the impact of the value of predicted CSI on the metric, P .
  • value of can be varied to further increase or decrease impact of on the metric, P .
  • ( , , ) are used to reduce or increase the impact of the corresponding predicted parameters while computing value of P and eventually the overall scheduling metric for each LC.
  • value of weight, , associated with can also be changed dynamically depending on the observed error in predicting CSI for user i. For example, value of weight can be reduced (or even made zero) if this error in predicting CSI for user i is above a pre-defined threshold for d1 time slots out of d2 time slots.
  • d1 and d2 are cofigurable parameters. 64K3052
  • value of weight, , associated with can also be changed dynamically depending on the observed error in predicting BO for LC i (for UE i).
  • value of weight can be reduced (or even made zero) if this error in predicting BO for LC i (for UE i) is above a pre-defined threshold for d3 time slots out of d4 time slots.
  • d3 and d4 are cofigurable parameters.
  • New scheduling metric for LC i, , depends on past, current and predicted values of certain parameters.
  • number of quantization levels that are used in a system depends on its hardware and software capabilities (e.g. compute resources available at the DU, communication overhead which can be supported between the node where the AIML models are deployed and the DU for the case where it is not possible to directly deploy AIML models at the DU). In addition, this can also depend on the accuracy of the AIML models used. Also, number of quantized levels for can be different than the number of quantized levels used for P and P . [00187] Values of ( , , ) can be configured or derived using some policies at the DU.
  • AIML models to predict BO and CSI can be located at the DU or CU-CP or Near-RT-RIC or another entity.
  • predicted BO, predicted CSI and other parameters to compute overall scheduling metric are available at the DU itself.
  • these AIML models are located at the Near-RT-RIC (for example as xApps): , , and are computed at the Near-RT- RIC and communicated to DU by enhancing the E2 interface. This is shown in FIG.14a.
  • Values of ( , , ) can be derived using operator-defined policies at the Near-RT-RIC and communicated from the Near-RT-RIC to the DU.
  • Quantization can also be done at the Near-RT-RIC for the case where quantized values are used to compute the overall scheduling metric of a LC. In this case, and are computed at the Near-RT-RIC and communicated to DU by enhancing the E2 interface. This is shown in FIG.14b.
  • FIG.14b illustrates the case, where these AIML models are located at the CU-CP: , , and are computed at the CU-CP and communicated to DU by enhancing the F1AP protocol running over the F1-C interface between CU-CP and DU.
  • Quantization can also be done at the CU-CP for the case where quantized values are used to compute the overall scheduling metric of a LC. In this case, and are computed at the CU-CP and communicated to DU by enhancing the F1AP running over the F1-C interface between CU-CP and DU. This is also shown in FIG.15. [00196] With this deployment model, values of ( , , ) can also be communicated from the CU-UP to the DU. [00197] As shown in FIG. 15, AIML training can be done at SMO.
  • program instructions can be provided to a processor to produce a machine, so that the instructions, which execute on the processor, create means for implementing the actions specified herein.
  • the computer 64K3052 program instructions can be executed by a processor to cause a series of operational steps to be performed by the processor to produce a computer-implemented process so that the instructions, which execute on the processor to provide steps for implementing the actions specified.
  • some of the steps can also be performed across more than one processor, such as might arise in a multi-processor computer system or even a group of multiple computer systems.
  • one or more blocks or combinations of blocks in the flowchart illustration can also be performed concurrently with other blocks or combinations of blocks, or even in a different sequence than illustrated without departing from the scope or spirit of the disclosure.

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Abstract

L'invention concerne des systèmes, des procédés et des produits programmes d'ordinateur pour un système configuré pour exécuter des modèles d'intelligence artificielle et d'apprentissage automatique. Une métrique de planificateur de chaque canal logique, où pour un Ième équipement utilisateur, des informations d'état de canal (CSI) à un instant t sont données par CSli t et des CSI prédites pour m créneaux suivants sont données par PredCSIi <t,t+m>,, les CSI comprenant un indicateur de qualité de canal et un indicateur de rang.
PCT/US2025/039866 2024-07-31 2025-07-30 Optimisation de décisions de gestion de ressources radio dans des réseaux o-ran à l'aide d'informations prédites Pending WO2026030443A1 (fr)

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