Information request method between network nodes, network nodes and storage medium
By adding time or accuracy indication information of prediction results to the inter-network information request method of wireless communication system, the problem of insufficient reference of prediction results in the existing system is solved, and more accurate and reliable network decisions are achieved.
Patent Information
- Application Number
- CN202311013319.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-08-11
- Publication Date
- 2025-02-18
AI Technical Summary
When existing wireless communication systems deal with complex heterogeneous networks and diverse communication scenarios, the time and accuracy indication information of the prediction results are insufficient, which affects the accuracy and reliability of network decisions.
In the inter-network node information request method, the time or accuracy indication information of the prediction result is added, and relevant information is carried through signaling to help the network node adjust the weight of the prediction result in network decision-making.
It improves the reference of the prediction results, assists network nodes to make more accurate and reliable network decisions, and enhances the adaptability and stability of the system in complex scenarios.
Smart Images
Figure CN119485422A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of wireless communications, and in particular to a method for requesting information between network nodes, a network node, and a storage medium. Background Art
[0002] New Radio (NR) communication systems will introduce artificial intelligence and big data technologies to cope with more complex heterogeneous networks and more diverse communication scenarios.
[0003] like Figure 1 The RAN (Radio Access Network) intelligent architecture shown in the figure, wherein the data collection (Data Collection) module is a module that provides input data for the model training (Model Training) module and the model inference (Model inference) module. The input data may include measurements, performance feedback, AI (Artificial Intelligence) / ML (Machine learning) model outputs and other data from user equipment (User Equipment, UE) or different network entities. The data type of the input data includes training data (i.e., the input information required for model training) and inference data (i.e., the input information required for model inference). Among them, the model training module can provide the model inference module with a deployed or updated model, and the model inference module feeds back feedback data such as model performance to the model training module. Network elements and other executors refer to the prediction information output by the model inference module to make network decisions. Summary of the invention
[0004] The disclosed embodiments add time or accuracy indication information of the prediction results to improve the reference value of the prediction results, assist network nodes in adjusting the weight of the prediction results in network decision-making, and help network nodes make more accurate and reliable network decisions.
[0005] Some embodiments of the present disclosure provide a method for requesting information between network nodes, including:
[0006] The second network node receives a first signaling sent by the first network node, which is used to request the second network node to provide prediction information, where the first signaling carries time indication information related to the prediction information;
[0007] In response to the first signaling, the second network node sends a second signaling to the first network node, indicating acceptance of the request for the prediction information and carrying the prediction parameters that can be provided;
[0008] The second network node sends a fourth signaling to the first network node, wherein the fourth signaling includes the prediction result and time or accuracy indication information of the prediction result.
[0009] In some embodiments, the method further includes: the second network node sending a third signaling to the first network node in response to the first signaling, indicating that the request for prediction information fails.
[0010] In some embodiments, the time indication information carried by the first signaling includes at least one of the following information: start time; duration; cycle; end time; and prediction result feedback time requirement.
[0011] In some embodiments, the time or accuracy indication information of the prediction result carried in the fourth signaling includes one or more of the following information: precision; confidence; validity time.
[0012] In some embodiments, the first signaling includes accuracy requirements required for prediction information requested by the first network node.
[0013] In some embodiments, the second signaling includes: indication information capable of providing all prediction information or part of the prediction information.
[0014] In some embodiments, the first signaling is one of the following signalings: data collection request signaling; information request signaling; artificial intelligence or machine learning information request signaling.
[0015] In some embodiments, the second signaling is one of the following signalings: data collection response signaling; data collection feedback signaling; information request response signaling; information request feedback signaling; artificial intelligence or machine learning information feedback signaling.
[0016] In some embodiments, the fourth signaling is one of the following signaling: data report; data collection report; data collection update; artificial intelligence or machine learning information update; artificial intelligence or machine learning information report.
[0017] In some embodiments, the third signaling is one of the following signalings: data collection failure signaling; information request failure signaling; artificial intelligence or machine learning information failure signaling.
[0018] In some embodiments, the accuracy is the degree of closeness between the predicted result and the actual result; or, the confidence is the probability that the predicted result is within a certain error range; or, the effective time is the time during which the predicted result is greater than a preset value; or, the effective time is the time during which the confidence is greater than a preset value.
[0019] In some embodiments, the predicted parameters include one or more of the following parameters: predicted network node traffic; predicted network node resource status; predicted number of users; predicted user device trajectory; predicted user device traffic; predicted user experience rate; predicted user device user experience QoE.
[0020] In some embodiments, it also includes: the first network node adjusts the weight of the prediction result in the network decision according to the received prediction result and the time or accuracy indication information of the prediction result, and determines the network decision according to the prediction result and its weight.
[0021] Some embodiments of the present disclosure provide a method for requesting information between network nodes, including: a third network node sends a fifth signaling to a fourth network node, the fifth signaling including a prediction result and time or accuracy indication information of the prediction result.
[0022] In some embodiments, the fifth signaling is at least one of the following signalings: switching request signaling; secondary node adding request signaling; secondary node changing requirement signaling.
[0023] In some embodiments, the time or accuracy indication information of the prediction result carried in the fifth signaling includes one or more of the following information: precision; confidence; validity time.
[0024] In some embodiments, the accuracy is the degree of closeness between the predicted result and the actual result; or, the confidence is the probability that the predicted result is within a certain error range; or, the effective time is the time during which the predicted result is greater than a preset value; or, the effective time is the time during which the predicted confidence is greater than a preset value.
[0025] In some embodiments, the third network node sends the fifth signaling to the fourth network node, including: when the third network node intends to switch the user equipment to the fourth network node, sending a switching request signaling to the fourth network node; or,
[0026] The third network node intends to configure dual connectivity for the user equipment, and sends a secondary node adding request signaling to a fourth network node that is a candidate target secondary node; or,
[0027] When the third network node, as the primary node, triggers a secondary node change, sends a secondary node adding request signaling to a fourth network node, as a candidate target secondary node; or,
[0028] When the third network node, as a secondary node, triggers a secondary node change, it sends a secondary node change requirement signaling to the fourth network node, which is the primary node, and the fourth network node sends a secondary node add request signaling to the candidate target secondary node.
[0029] In some embodiments, it also includes: the fourth network node adjusts the weight of the prediction result in the network decision according to the received prediction result and the time or accuracy indication information of the prediction result, and determines the network decision according to the prediction result and its weight.
[0030] Some embodiments of the present disclosure provide a network node, including: a memory; and a processor coupled to the memory, wherein the processor is configured to execute a method for requesting information between network nodes based on instructions stored in the memory.
[0031] Some embodiments of the present disclosure provide a network node, comprising: a module for executing a method for requesting information between network nodes.
[0032] Some embodiments of the present disclosure provide a non-transitory computer-readable storage medium having a computer program stored thereon, which implements the steps of a method for requesting information between network nodes when the program is executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] The following is a brief introduction to the drawings required for use in the embodiments or related technical descriptions. The present disclosure can be more clearly understood according to the following detailed description with reference to the drawings.
[0034] Obviously, the drawings described below are only some embodiments of the present disclosure, and a person skilled in the art can obtain other drawings based on these drawings without creative work.
[0035] Figure 1 The RAN intelligent architecture is shown.
[0036] Figure 2 A schematic diagram showing a flow chart of a method for requesting information between network nodes according to some embodiments of the present disclosure.
[0037] Figure 3 A schematic diagram showing a flow chart of a method for requesting information between network nodes according to some embodiments of the present disclosure.
[0038] Figure 4 A schematic diagram showing a flow chart of a method for requesting information between network nodes according to some embodiments of the present disclosure.
[0039] Figure 5 A schematic diagram showing a flow chart of a method for requesting information between network nodes according to some embodiments of the present disclosure.
[0040] Figure 6 A schematic diagram showing a flow chart of a method for requesting information between network nodes according to some embodiments of the present disclosure.
[0041] Figure 7 A schematic diagram showing a flow chart of a method for requesting information between network nodes according to some embodiments of the present disclosure.
[0042] Figure 8 A schematic diagram showing the structure of a network node according to some embodiments of the present disclosure. DETAILED DESCRIPTION
[0043] The technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure.
[0044] Unless otherwise specified, descriptions such as “first” and “second” in the present disclosure are used to distinguish different objects and are not used to indicate meanings such as size or time sequence.
[0045] The disclosed embodiments add time or accuracy indication information of the prediction results to improve the reference value of the prediction results, assist network nodes in adjusting the weight of the prediction results in network decision-making, and help network nodes make more accurate and reliable network decisions.
[0046] The timing or accuracy indication of the prediction result can be obtained by testing the network node providing the prediction result on a recent test set. The recent test set is the most recently collected network data, including measurements from user devices or different network entities, performance feedback, AI / ML model output, etc.
[0047] The time or accuracy indication information of the prediction result includes one or more of the following information: accuracy; confidence; validity time.
[0048] The accuracy refers to the closeness between the predicted result and the actual result, and the value range is {0,100}.
[0049] The confidence is the probability that the prediction result is within a certain error range, and the value range is {0,100}. The error range m is determined by the node requesting the prediction information based on demand, or by the node providing the prediction information based on experience.
[0050] The effective time is the time during which the prediction result is greater than a preset value, or the time during which the confidence is greater than a preset value. The duration is, for example, {T0, T0+T}.
[0051] Figure 2 A schematic diagram showing a flow chart of a method for requesting information between network nodes according to some embodiments of the present disclosure.
[0052] like Figure 2 As shown, the method of this embodiment includes one or more of the following steps.
[0053] In step 210, the user equipment is connected to the first network node, the first network node sends a first signaling to the second network node, and the second network node receives the first signaling sent by the first network node to request the second network node to provide prediction information.
[0054] The first network node and the second network node are, for example, RAN nodes, and the RAN nodes include, but are not limited to, NG (Next generation)-RAN nodes. The RAN nodes are, for example, base stations.
[0055] In some embodiments, the first signaling carries time indication information related to the prediction information. The time indication information carried by the first signaling includes at least one of the following information:
[0056] Start time, indicating the prediction start time;
[0057] Duration, which indicates the predicted duration;
[0058] Cycle, which indicates the predicted cycle;
[0059] End time, indicating the prediction end time;
[0060] The prediction result feedback time requirement indicates the desired time for feedback of the prediction result.
[0061] In some embodiments, the first signaling includes the accuracy requirement or error requirement required by the prediction information requested by the first network node. Some examples of prediction parameters and their accuracy / error requirements are listed below.
[0062] 1) Prediction parameter 1, predicted network node (such as base station) traffic / load / resource status;
[0063] Accuracy / error requirement 1;
[0064] Time indication information 1 (such as start time, end time).
[0065] 2) Prediction parameter 2, predicted UE trajectory information;
[0066] Accuracy / error requirement 2;
[0067] Time indication information 2 (such as start time, duration).
[0068] 3) Prediction parameter 3, predicted UE traffic;
[0069] Accuracy / error requirement 3;
[0070] Time indication information 3 (such as start time, period).
[0071] 4) Prediction parameter 4, other prediction parameters, such as predicted UE QoE (Quality of Experience, service experience), etc.
[0072] Accuracy / error requirement 4;
[0073] Time indication information 4 (such as start time, prediction result feedback time requirement).
[0074] In some embodiments, the first signaling, for example, includes but is not limited to one of the following signaling: data collection request signaling; information request signaling; artificial intelligence or machine learning information request (AI / ML Information Request) signaling.
[0075] The second network node executes step 220a or 220b in response to the first signaling. If the second network node can provide all or part of the prediction information indicated in the first signaling according to the time and accuracy requirements, step 220a is executed; if the second network node cannot provide all the prediction information indicated in the first signaling according to the time and accuracy requirements, step 220b is executed.
[0076] In step 220a, the second network node sends a second signaling to the first network node in response to the first signaling, indicating acceptance of the request for the prediction information and carrying the prediction parameters that can be provided.
[0077] In some embodiments, the second signaling includes: indication information capable of providing all prediction information or part of the prediction information.
[0078] In some embodiments, the second signaling, for example, includes but is not limited to one of the following signaling: data collection response signaling; data collection feedback signaling; information request response signaling; information request feedback signaling; artificial intelligence or machine learning information feedback (AI / ML Information Confirm) signaling.
[0079] In some embodiments, the predicted parameters include, but are not limited to, one or more of the following parameters: predicted network node (such as base station) traffic; predicted network node (such as base station) resource status; predicted network node (such as base station) load (such as the number of users); predicted user device trajectory; predicted user device traffic; predicted user experience rate; predicted user device user experience QoE.
[0080] In step 220b, the second network node sends a third signaling to the first network node in response to the first signaling, indicating that the request for prediction information has failed.
[0081] In some embodiments, the third signaling includes, for example, but is not limited to, one of the following signalings: data collection failure signaling; information request failure signaling; artificial intelligence or machine learning information failure (AI / ML Information Failure) signaling.
[0082] In step 230, the second network node performs prediction and sends a fourth signaling to the first network node, wherein the fourth signaling includes the prediction result and time or accuracy indication information of the prediction result.
[0083] The second network node inputs the collected prediction-related input data into the model reasoning module. The input data may include measurements from user devices or different network entities, performance feedback, AI / ML model outputs, etc. The model reasoning module inputs the input data into the trained AI / ML model and outputs various prediction results. The model reasoning module tests the time or accuracy indication information of the prediction results on the most recent test set.
[0084] In some embodiments, the fourth signaling is one of the following signaling: data report; data collection report; data collection update; artificial intelligence or machine learning information update (AI / ML Information Update); artificial intelligence or machine learning information report.
[0085] In some embodiments, the time or accuracy indication information of the prediction result carried in the fourth signaling includes one or more of the following information: accuracy; confidence; effective time. For relevant explanations, please refer to the above and will not be repeated here. Some examples of prediction results and their time or accuracy indication information are listed below.
[0086] 1) Prediction result 1, predicted network node (such as base station) traffic / load / resource status;
[0087] Precision, value range {0,100};
[0088] Confidence, value range {0,100};
[0089] Valid time {T0, T0+T}.
[0090] 2) Prediction result 2, predicted UE trajectory information;
[0091] Precision, value range {0,100};
[0092] Confidence, value range {0,100};
[0093] Valid time {T0, T0+T}.
[0094] 3) Prediction result 3, predicted UE traffic;
[0095] Precision, value range {0,100};
[0096] Confidence, value range {0,100};
[0097] Valid time {T0, T0+T}.
[0098] 4) Prediction result 4, other prediction results, such as predicted UE QoE (Quality of Experience, service experience), etc.
[0099] Precision, value range {0,100};
[0100] Confidence, value range {0,100};
[0101] Valid time {T0, T0+T}.
[0102] In step 240, the first network node adjusts the weight of the prediction result in the network decision according to the received prediction result and the time or accuracy indication information of the prediction result, and determines the network decision according to the prediction result and its weight.
[0103] After receiving each prediction result, the first network node makes a comprehensive reference based on the accuracy / confidence and effective time of the prediction result. When the prediction confidence provided by the second network node is not ideal, the first network node can rely more on actual measurements to make decisions, such as increasing the weight of actual measurements in the final decision, or terminating the current process of requesting prediction information from the second network node. When the prediction result confidence provided by the second network node is high, the first network node can increase the weight of the prediction parameter in the final decision.
[0104] Based on the request of the first network node, the second network node improves the reference of the prediction result by adding time or accuracy indication information of the prediction result, assists the first network node in adjusting the weight of the prediction result in network decision-making, and helps the first network node make more accurate and reliable network decisions.
[0105] Figure 3 A schematic diagram showing a flow chart of a method for requesting information between network nodes according to some embodiments of the present disclosure.
[0106] like Figure 3 As shown, the method of this embodiment includes one or more of the following steps.
[0107] In step 310, the third network node sends a fifth signaling to the fourth network node, wherein the fifth signaling includes the prediction result and time or accuracy indication information of the prediction result.
[0108] In some embodiments, the fifth signaling is at least one of the following signalings: switching request signaling; secondary node adding request signaling; secondary node changing requirement signaling.
[0109] The time or accuracy indication information of the prediction result carried in the fifth signaling includes one or more of the following information: accuracy; confidence; effective time. For relevant explanations, please refer to the above, which will not be repeated here.
[0110] In step 320, the fourth network node adjusts the weight of the prediction result in the network decision according to the received prediction result and the time or accuracy indication information of the prediction result, and determines the network decision according to the prediction result and its weight.
[0111] In business scenarios such as user equipment switching network nodes or dual connections, the third network node improves the reference of the prediction results by adding time or accuracy indication information of the prediction results, assists the fourth network node in adjusting the weight of the prediction results in network decisions, and helps the fourth network node to make more accurate and reliable network decisions.
[0112] Below Figure 3 The solutions of the embodiments are described in different application scenarios. The third network node and the fourth network node described below are, for example, RAN nodes, and the RAN nodes include, but are not limited to, NG-RAN nodes. The RAN nodes are, for example, base stations.
[0113] Figure 4 A schematic diagram showing a flow chart of a method for requesting information between network nodes according to some embodiments of the present disclosure.
[0114] like Figure 4 As shown, the method of this embodiment includes one or more of the following steps.
[0115] In step 410, the user equipment is connected to the third network node. When the third network node intends to hand over the user equipment to the fourth network node, it performs prediction and sends a handover request signaling to the fourth network node, including the prediction result and the time or accuracy indication information of the prediction result.
[0116] The third network node inputs the collected prediction-related input data into the model reasoning module. The input data may include measurements from user devices or different network entities, performance feedback, AI / ML model outputs, etc. The model reasoning module inputs the input data into the trained AI / ML model and outputs various prediction results. The model reasoning module tests the time or accuracy indication information of the prediction results on the most recent test set.
[0117] In some embodiments, the time or accuracy indication information of the prediction result includes one or more of the following information: accuracy; confidence; validity time. For related explanations, please refer to the above and will not be repeated here. The following lists some examples of prediction results and their time or accuracy indication information.
[0118] 1) Prediction result 1, predicted traffic volume / load volume / resource status of the fourth network node;
[0119] Precision, value range {0,100};
[0120] Confidence, value range {0,100};
[0121] Valid time {T0, T0+T}.
[0122] 2) Prediction result 2, predicted UE trajectory information;
[0123] Precision, value range {0,100};
[0124] Confidence, value range {0,100};
[0125] Valid time {T0, T0+T}.
[0126] 3) Prediction result 3, predicted UE traffic;
[0127] Precision, value range {0,100};
[0128] Confidence, value range {0,100};
[0129] Valid time {T0, T0+T}.
[0130] 4) Prediction result 4, other prediction results, such as predicted UE QoE (Quality of Experience, service experience), etc.
[0131] Precision, value range {0,100};
[0132] Confidence, value range {0,100};
[0133] Valid time {T0, T0+T}.
[0134] In step 420, the fourth network node adjusts the weight of the prediction result in the network decision according to the received prediction result and the time or accuracy indication information of the prediction result, and determines the network decision related to the switching control according to the prediction result and its weight.
[0135] The network decision related to the switching control includes, for example, whether to accept the switching of the user equipment, the time and frequency resources used for the switching of the user equipment, and other information.
[0136] When the third network node intends to switch the user equipment to the fourth network node, by adding a prediction result and time or accuracy indication information of the prediction result in the switching request signaling, the reference of the prediction result is improved, the fourth network node is assisted in adjusting the weight of the prediction result in the network decision, and the fourth network node is helped to make a more accurate and reliable switching decision.
[0137] Figure 5A schematic diagram showing a flow chart of a method for requesting information between network nodes according to some embodiments of the present disclosure.
[0138] like Figure 5 As shown, the method of this embodiment includes one or more of the following steps.
[0139] In step 510, the user equipment is connected to the third network node, the third network node intends to configure dual connectivity for the user equipment, performs prediction, and sends a secondary node addition request (SN Addition Request) signaling to a fourth network node as a candidate target secondary node (SN), which includes a prediction result and time or accuracy indication information of the prediction result.
[0140] In some embodiments, the time or accuracy indication information of the prediction result includes one or more of the following information: accuracy; confidence; validity time. For related explanations, please refer to the above and will not be repeated here. The following lists some examples of prediction results and their time or accuracy indication information.
[0141] 1) Prediction result 1, predicted traffic volume / load volume / resource status of the fourth network node;
[0142] Precision, value range {0,100};
[0143] Confidence, value range {0,100};
[0144] Valid time {T0, T0+T}.
[0145] 2) Prediction result 2, predicted UE trajectory information;
[0146] Precision, value range {0,100};
[0147] Confidence, value range {0,100};
[0148] Valid time {T0, T0+T}.
[0149] 3) Prediction result 3, predicted UE traffic;
[0150] Precision, value range {0,100};
[0151] Confidence, value range {0,100};
[0152] Valid time {T0, T0+T}.
[0153] 4) Prediction result 4, other prediction results, such as predicted UE QoE (Quality of Experience, service experience), etc.
[0154] Precision, value range {0,100};
[0155] Confidence, value range {0,100};
[0156] Valid time {T0, T0+T}.
[0157] In step 520, the fourth network node adjusts the weight of the prediction result in the network decision according to the received prediction result and the time or accuracy indication information of the prediction result, and determines the auxiliary node to add the relevant RRM (Radio Resource Management) network decision according to the prediction result and its weight.
[0158] The RRM network decision related to the secondary node addition includes, for example, whether to accept the secondary node addition request, time and frequency resources related to the secondary node addition, and other information.
[0159] When the third network node intends to configure dual connectivity for the user equipment, it sends a secondary node addition request to the fourth network node, which is a candidate target secondary node, wherein a new prediction result and time or accuracy indication information of the prediction result are added, so as to improve the reference value of the prediction result, assist the fourth network node in adjusting the weight of the prediction result in the network decision, and help the fourth network node to make a more accurate and reliable secondary node addition RRM decision.
[0160] Figure 6 A schematic diagram showing a flow chart of a method for requesting information between network nodes according to some embodiments of the present disclosure.
[0161] like Figure 6 As shown, the method of this embodiment includes one or more of the following steps.
[0162] In step 610, the user equipment is dual-connected to the third network node (primary node) and the fifth network node (current secondary node). When the third network node, as the primary node, triggers a secondary node change, it performs prediction and sends a secondary node addition request (SN Addition Request) signaling to the fourth network node, which is a candidate target secondary node, including the prediction result and the time or accuracy indication information of the prediction result.
[0163] In some embodiments, the time or accuracy indication information of the prediction result includes one or more of the following information: accuracy; confidence; validity time. For related explanations, please refer to the above and will not be repeated here. The following lists some examples of prediction results and their time or accuracy indication information.
[0164] 1) Prediction result 1, predicted traffic volume / load volume / resource status of the fourth network node;
[0165] Precision, value range {0,100};
[0166] Confidence, value range {0,100};
[0167] Valid time {T0, T0+T}.
[0168] 2) Prediction result 2, predicted UE trajectory information;
[0169] Precision, value range {0,100};
[0170] Confidence, value range {0,100};
[0171] Valid time {T0, T0+T}.
[0172] 3) Prediction result 3, predicted UE traffic;
[0173] Precision, value range {0,100};
[0174] Confidence, value range {0,100};
[0175] Valid time {T0, T0+T}.
[0176] 4) Prediction result 4, other prediction results, such as predicted UE QoE (Quality of Experience, service experience), etc.
[0177] Precision, value range {0,100};
[0178] Confidence, value range {0,100};
[0179] Valid time {T0, T0+T}.
[0180] In step 620, the fourth network node adjusts the weight of the prediction result in the network decision according to the received prediction result and the time or accuracy indication information of the prediction result, and determines the RRM network decision related to the auxiliary node addition according to the prediction result and its weight.
[0181] The RRM network decision related to the secondary node addition includes, for example, whether to accept the secondary node addition request, time and frequency resources related to the secondary node addition, and other information.
[0182] When the third network node acts as the main node to trigger the change of the secondary node, a secondary node addition request signaling is sent to the fourth network node as the candidate target secondary node, in which the newly added prediction result and the time or accuracy indication information of the prediction result are added, so as to improve the reference value of the prediction result, assist the fourth network node to adjust the weight of the prediction result in the network decision, and help the fourth network node to make more accurate and reliable secondary node addition RRM decision.
[0183] Figure 7 A schematic diagram showing a flow chart of a method for requesting information between network nodes according to some embodiments of the present disclosure.
[0184] like Figure 7 As shown, the method of this embodiment includes one or more of the following steps.
[0185] In step 710, the user equipment is dual-connected to the fourth network node (primary node) and the third network node (current secondary node). When the third network node, as a secondary node, triggers a secondary node change, it performs prediction and sends a secondary node change requirement (SN Change Required) signaling to the fourth network node, which is the primary node, including the prediction result and the time or accuracy indication information of the prediction result.
[0186] In some embodiments, the time or accuracy indication information of the prediction result includes one or more of the following information: accuracy; confidence; validity time. For related explanations, please refer to the above and will not be repeated here. The following lists some examples of prediction results and their time or accuracy indication information.
[0187] 1) Prediction result 1, predicted traffic volume / load volume / resource status of the candidate target auxiliary node (fifth network node);
[0188] Precision, value range {0,100};
[0189] Confidence, value range {0,100};
[0190] Valid time {T0, T0+T}.
[0191] 2) Prediction result 2, predicted UE trajectory information;
[0192] Precision, value range {0,100};
[0193] Confidence, value range {0,100};
[0194] Valid time {T0, T0+T}.
[0195] 3) Prediction result 3, predicted UE traffic;
[0196] Precision, value range {0,100};
[0197] Confidence, value range {0,100};
[0198] Valid time {T0, T0+T}.
[0199] 4) Prediction result 4, other prediction results, such as predicted UE QoE (Quality of Experience, service experience), etc.
[0200] Precision, value range {0,100};
[0201] Confidence, value range {0,100};
[0202] Valid time {T0, T0+T}.
[0203] In step 720, the fourth network node sends a secondary node addition request (SN Addition Request) signaling to the candidate target secondary node (fifth network node), including the prediction result in step 710 and time or accuracy indication information of the prediction result.
[0204] In step 730, the candidate target auxiliary node (fifth network node) adjusts the weight of the prediction result in the network decision according to the received prediction result and the time or accuracy indication information of the prediction result, and determines the RRM network decision related to the auxiliary node addition according to the prediction result and its weight.
[0205] The RRM network decision related to the secondary node addition includes, for example, whether to accept the secondary node addition request, time and frequency resources related to the secondary node addition, and other information.
[0206] When the third network node acts as a secondary node to trigger a secondary node change, the fourth network node sends a secondary node addition request signaling to the candidate target secondary node by sending a secondary node change request signaling to the fourth network node as the primary node, wherein the newly added prediction result and the time or accuracy indication information of the prediction result are added to improve the reference of the prediction result, assist the candidate target secondary node to adjust the weight of the prediction result in the network decision, and help the candidate target secondary node to make a more accurate and reliable secondary node addition RRM decision.
[0207] The disclosed embodiments support the transmission of prediction results and time or accuracy indication information of the prediction results between Xn interfaces, assist network nodes in adjusting the weight of the prediction results in network decisions, and help network nodes make more accurate and reliable network decisions.
[0208] Figure 8 A schematic diagram showing the structure of a network node according to some embodiments of the present disclosure is shown. The network node includes a first network node, a second network node, a third network node, a fourth network node, and a fifth network node.
[0209] like Figure 8 As shown, the network node 800 of this embodiment includes: a memory 810 and a processor 820 coupled to the memory 810 , and the processor 820 is configured to execute the method in any of the aforementioned embodiments based on the instructions stored in the memory 810 .
[0210] The network node 800 may further include an input / output interface 830 , a network interface 840 , a storage interface 850 , etc. These interfaces 830 , 840 , 850 , the memory 810 , and the processor 820 may be connected via a bus 860 , for example.
[0211] The memory 810 may include, for example, a system memory, a fixed non-volatile storage medium, etc. The system memory may store, for example, an operating system, an application program, a boot loader, and other programs.
[0212] The processor 820 may be implemented using a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistors and other discrete hardware components.
[0213] Among them, the input and output interface 830 provides a connection interface for input and output devices such as a display, a mouse, a keyboard, and a touch screen. The network interface 840 provides a connection interface for various networked devices. The storage interface 850 provides a connection interface for external storage devices such as SD cards and USB flash drives. The bus 860 can use any bus structure among a variety of bus structures. For example, the bus structure includes but is not limited to the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MCA) bus, and the Peripheral Component Interconnect (PCI) bus.
[0214] A network node, such as a second network node, includes a sending module and a receiving module.
[0215] A receiving module, configured to receive a first signaling sent by a first network node, used to request a second network node to provide prediction information, wherein the first signaling carries time indication information related to the prediction information;
[0216] The sending module is configured to send a second signaling to the first network node in response to the first signaling, indicating acceptance of the request for the prediction information and carrying the available prediction parameters; and send a fourth signaling to the first network node, including the prediction result and the time or accuracy indication information of the prediction result.
[0217] The sending module is configured to send a third signaling to the first network node to indicate that the request for prediction information fails.
[0218] The explanation of signaling and prediction parameters is as mentioned above and will not be repeated here.
[0219] A network node, for example a third network node, includes a sending module.
[0220] The sending module is configured to send fifth signaling to the fourth network node, wherein the fifth signaling includes the prediction result and time or accuracy indication information of the prediction result.
[0221] The sending module is configured as:
[0222] When the third network node intends to hand over the user equipment to the fourth network node, the third network node sends a handover request signaling to the fourth network node; or,
[0223] The third network node intends to configure dual connectivity for the user equipment, and sends a secondary node adding request signaling to a fourth network node that is a candidate target secondary node; or,
[0224] When the third network node, as the primary node, triggers a secondary node change, sends a secondary node adding request signaling to a fourth network node, as a candidate target secondary node; or,
[0225] When the third network node, as a secondary node, triggers a secondary node change, it sends a secondary node change requirement signaling to the fourth network node, which is the primary node, and the fourth network node sends a secondary node add request signaling to the candidate target secondary node.
[0226] The explanation of signaling and prediction parameters is as mentioned above and will not be repeated here.
[0227] The embodiments of the present disclosure provide a non-transitory computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method of each embodiment are implemented.
[0228] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more non-transient computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer program code.
[0229] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0230] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0231] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0232] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.
Claims
1. A method for requesting information between network nodes, characterized in that: include: The second network node receives a first signaling sent by the first network node, which is used to request the second network node to provide prediction information, where the first signaling carries time indication information related to the prediction information; In response to the first signaling, the second network node sends a second signaling to the first network node, indicating acceptance of the request for the prediction information and carrying the prediction parameters that can be provided; The second network node sends a fourth signaling to the first network node, wherein the fourth signaling includes the prediction result and time or accuracy indication information of the prediction result.
2. The method according to claim 1, characterized in that Also includes: In response to the first signaling, the second network node sends a third signaling to the first network node, indicating that the request for prediction information fails.
3. The method according to claim 1, characterized in that The time indication information carried by the first signaling includes at least one of the following information: Start time; Duration; cycle; End time; Predict the time required for feedback of results.
4. The method according to claim 1, characterized in that: The time or accuracy indication information of the prediction result carried in the fourth signaling includes one or more of the following information: Accuracy; Confidence; Validity period.
5. The method according to claim 1, characterized in that The first signaling includes accuracy requirements required for prediction information requested by the first network node.
6. The method according to claim 1, characterized in that The second signaling includes: indication information capable of providing all prediction information or part of the prediction information.
7. The method according to any one of claims 1 to 6, characterized in that: The first signaling is one of the following signalings: Data collection request signaling; Information request signaling; Artificial intelligence or machine learning information request signaling.
8. The method according to any one of claims 1 to 6, characterized in that: The second signaling is one of the following signalings: Data collection response signaling; Data collection feedback signaling; Information request response signaling; Information request feedback signaling; Artificial intelligence or machine learning information feedback signaling.
9. The method according to any one of claims 1 to 6, characterized in that: The fourth signaling is one of the following signalings: Data reporting; Data collection reports; Data collection updates; Artificial intelligence or machine learning information updates; Artificial intelligence or machine learning information report.
10. The method according to claim 2, characterized in that The third signaling is one of the following signalings: Data collection failure signaling; Information request failure signaling; Artificial intelligence or machine learning information failure signaling.
11. The method according to claim 4, characterized in that The accuracy is the degree of closeness between the predicted result and the actual result; or, The confidence level is the probability that the prediction result is within a certain error range; or, The effective time is the time during which the prediction result is greater than the preset value; or, The effective time is the time during which the confidence level is greater than a preset value.
12. The method according to claim 1, characterized in that Prediction parameters include one or more of the following: Predicted network node traffic; Predicted network node resource status; The predicted number of users; Predicted user device trajectory; predicted user equipment traffic; Predicted user experience rate; Predicted user experience QoE of user devices.
13. The method according to any one of claims 1 to 12, characterized in that: Also includes: The first network node adjusts the weight of the prediction result in the network decision according to the received prediction result and the time or accuracy indication information of the prediction result, and determines the network decision according to the prediction result and its weight.
14. A method for requesting information between network nodes, characterized in that: include: The third network node sends a fifth signaling to the fourth network node, wherein the fifth signaling includes the prediction result and time or accuracy indication information of the prediction result.
15. The method according to claim 14, characterized in that The fifth signaling is at least one of the following signalings: Handover request signaling; Secondary node adding request signaling; Secondary node change demand signaling.
16. The method according to claim 14, characterized in that The time or accuracy indication information of the prediction result carried in the fifth signaling includes one or more of the following information: Accuracy; Confidence; Validity period.
17. The method according to claim 16, characterized in that The accuracy is the degree of closeness between the predicted result and the actual result; or, The confidence level is the probability that the prediction result is within a certain error range; or, The effective time is the time during which the prediction result is greater than the preset value; or, The effective time is the time during which the prediction confidence is greater than the preset value.
18. The method according to claim 15, characterized in that The third network node sends a fifth signaling to the fourth network node, including: When the third network node intends to hand over the user equipment to the fourth network node, the third network node sends a handover request signaling to the fourth network node; or, The third network node intends to configure dual connectivity for the user equipment, and sends a secondary node adding request signaling to a fourth network node that is a candidate target secondary node; or, When the third network node, as the primary node, triggers a secondary node change, sends a secondary node adding request signaling to a fourth network node, as a candidate target secondary node; or, When the third network node, as a secondary node, triggers a secondary node change, it sends a secondary node change requirement signaling to the fourth network node, which is the primary node, and the fourth network node sends a secondary node add request signaling to the candidate target secondary node.
19. The method according to any one of claims 14 to 18, characterized in that: Also includes: The fourth network node adjusts the weight of the prediction result in the network decision according to the received prediction result and the time or accuracy indication information of the prediction result, and determines the network decision according to the prediction result and its weight.
20. A network node, comprising: Memory; as well as A processor coupled to the memory, the processor being configured to execute the method of any one of claims 1-19 based on instructions stored in the memory.
21. A network node, comprising: A module for executing the method of any one of claims 1-19.
22. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the steps of the method according to any one of claims 1 to 19 are implemented.