Coverage and capacity optimization processing method and device and medium

Abstract

The embodiment of the invention relates to a processing method and device for coverage and capacity optimization and a medium. The method comprises the steps that coverage and capacity optimization CCO information is determined based on an artificial intelligence model; the CCO information is sent to a distribution unit DU, and the CCO information is used for indicating the DU to execute a CCO optimization behavior; receiving CCO optimization behavior indication information or CCO optimization behavior sent by the DU; sending the CCO optimization behavior to other nodes; and receiving feedback information about the CCO optimization behavior sent by the DU and other nodes. In the technical scheme, the accuracy of the coverage and capacity optimization decision is ensured, and a guarantee is provided for the communication quality.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technologies, and in particular, to a processing method, apparatus, and medium for coverage and capacity optimization. Background Art

[0002] Coverage and Capacity Optimization (CCO) decision-making, as a common communication mechanism, is of great significance for ensuring the communication quality of cellular networks.

[0003] In the related art, CCO decision-making is performed based on the separation scenario of a Centralized Unit / Distributed Unit (CU / DU). That is, the CU obtains a measurement report, determines the current CCO problem according to the measurement report, and then sends the CCO problem to the DU. The DU calculates the underlying resource status information of a fixed type according to a preset algorithm to determine the CCO decision.

[0004] However, in the above CU / DU separation scenario, the DCU only instructs the DU to make a CCO decision when it determines that a CCO problem occurs according to the measurement report. When the DU makes a CCO decision, it does not understand the global underlying resource status information associated with the CCO problem. The DU makes a CCO decision based on the fixed type of underlying resource status information and a fixed algorithm, resulting in low accuracy in determining the CCO decision. Therefore, the communication quality cannot be guaranteed. Summary of the Invention

[0005] To solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides a processing method, apparatus, and medium for coverage and capacity optimization.

[0006] An embodiment of the present disclosure provides a processing method for coverage and capacity optimization. The method is applied to a Centralized Unit (CU) and includes: determining coverage and capacity optimization (CCO) information based on an artificial intelligence model; sending the CCO information to a Distributed Unit (DU), where the CCO information is used to instruct the DU to perform a CCO optimization behavior; receiving the CCO optimization behavior indication information or the CCO optimization behavior sent by the DU; sending the CCO optimization behavior to other nodes; and receiving feedback information about the CCO optimization behavior sent by the DU and the other nodes.

[0007] An embodiment of the present disclosure further provides a method for processing coverage and capacity optimization. The method is applied to a distributed unit (DU) and includes: receiving coverage and capacity optimization (CCO) information determined based on an artificial intelligence model sent by a central unit (CU), and performing a CCO optimization behavior according to the CCO information; sending the CCO optimization behavior indication information or the CCO optimization behavior to the CU; and sending feedback information about the CCO optimization behavior to the CU.

[0008] An embodiment of the present disclosure further provides a processing device for coverage and capacity optimization. The device is applied to a central unit (CU) and includes: a memory, a transceiver, and a processor. The memory is used for storing programs; the transceiver is used for transceiving data under the control of the processor; and the processor is used for reading the programs in the memory and performing the following operations: determining coverage and capacity optimization (CCO) information based on an artificial intelligence model; sending the CCO information to a distributed unit (DU), where the CCO information is used to instruct the DU to perform a CCO optimization behavior; receiving the CCO optimization behavior indication information or the CCO optimization behavior sent by the DU; sending the CCO optimization behavior to other nodes; and receiving feedback information about the CCO optimization behavior sent by the DU and the other nodes.

[0009] An embodiment of the present disclosure further provides a processing device for coverage and capacity optimization. The device is applied to a distributed unit (DU) and includes: a memory, a transceiver, and a processor. The memory is used for storing programs; the transceiver is used for transceiving data under the control of the processor; and the processor is used for reading the programs in the memory and performing the following operations: receiving coverage and capacity optimization (CCO) information determined based on an artificial intelligence model sent by a central unit (CU), and performing a CCO optimization behavior according to the CCO information; sending the CCO optimization behavior indication information or the CCO optimization behavior to the CU; and sending feedback information about the CCO optimization behavior to the CU.

[0010] An embodiment of the present disclosure further provides a processing device for coverage and capacity optimization. The device is applied to a central unit (CU) and includes: a determination module, configured to determine coverage and capacity optimization (CCO) information based on an artificial intelligence model; a first sending module, configured to send the CCO information to a distributed unit (DU), where the CCO information is used to instruct the DU to perform a CCO optimization behavior; a first receiving module, configured to receive the CCO optimization behavior indication information or the CCO optimization behavior sent by the DU; a second sending module, configured to send the CCO optimization behavior to other nodes; and a second receiving module, configured to receive feedback information about the CCO optimization behavior sent by the DU and the other nodes.

[0011] An embodiment of the present disclosure also provides a processing device for coverage and capacity optimization. The device is applied to a distributed unit (DU) and includes: a third receiving module, configured to receive coverage and capacity optimization (CCO) information determined based on an artificial intelligence model sent by a central unit (CU); an execution processing module, configured to perform CCO optimization actions according to the CCO information; a third sending module, configured to send CCO optimization action indication information or the CCO optimization actions to the CU; and a fourth sending module, configured to send feedback information about the CCO optimization actions to the CU.

[0012] An embodiment of the present disclosure also provides a processor-readable storage medium storing a program for executing the processing method for coverage and capacity optimization provided by the embodiment of the present disclosure.

[0013] The technical solution provided by the embodiment of the present disclosure has the following advantages compared with the prior art:

[0014] In the processing solution for coverage and capacity optimization provided by the embodiment of the present disclosure, the central unit (CU) determines coverage and capacity optimization (CCO) information based on an artificial intelligence model, and then sends the CCO information to the distributed unit (DU). The CCO information is used to instruct the DU to perform CCO optimization actions, receive the CCO optimization action indication information or the CCO optimization actions sent by the DU, and then send the CCO optimization actions to other nodes, and receive the feedback information about the CCO optimization actions sent by the DU and other nodes. In this technical solution, the underlying resource information related to the actual scenario can be flexibly and comprehensively considered based on the artificial intelligence model to determine the CCO information, which ensures the accuracy of the determined CCO information. Furthermore, it ensures the accuracy and flexibility of the CCO decision-making, providing a guarantee for the communication quality.

[0015] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present disclosure and used together with the specification to explain the principles of the present disclosure.

[0017] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings required for describing the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1It is a schematic diagram of a communication architecture according to an embodiment of the present disclosure;

[0019] Figure 2 It is a schematic flowchart of a method for optimizing coverage and capacity provided according to an embodiment of the present disclosure;

[0020] Figure 3 It is a schematic flowchart of a process for optimizing coverage and capacity according to an embodiment of the present disclosure;

[0021] Figure 4 It is a schematic flowchart of a process for optimizing coverage and capacity according to another embodiment of the present disclosure;

[0022] Figure 5 It is a schematic flowchart of a process for optimizing coverage and capacity according to another embodiment of the present disclosure;

[0023] Figure 6 It is a schematic flowchart of another method for optimizing coverage and capacity provided according to an embodiment of the present disclosure;

[0024] Figure 7 It is a block diagram of the entity where the processing device for optimizing coverage and capacity according to an embodiment of the present disclosure is located;

[0025] Figure 8 It is a schematic diagram of the structure of the processing device for optimizing coverage and capacity according to an embodiment of the present disclosure;

[0026] Figure 9 It is a schematic diagram of the structure of the processing device for optimizing coverage and capacity according to another embodiment of the present disclosure. Detailed implementation manners

[0027] In the embodiments of the present invention, the term "and / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0028] In the embodiments of the present disclosure, the term "plurality" refers to two or more, and other quantifiers are similar thereto.

[0029] Next, the communication structure involved in the present disclosure will be described in conjunction with the accompanying drawings in the embodiments of the present disclosure. It is obvious that the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts shall fall within the scope of protection of the present disclosure.

[0030] Next, the communication structure related to the present disclosure will be described first, asFigure 1 As shown in the figure, the communication structure of the embodiments of the present disclosure includes two logical units: a Centralized Unit (CU) and a Distributed Unit (DU). Among them, the CU mainly includes non-real-time radio high-layer protocol stack functions, and also supports the sinking of some core network functions and the deployment of edge application services. Multiple DUs are attached to one CU. The DU mainly processes physical layer functions and layers with real-time requirements. In this embodiment, the DU is used to control the coverage and capacity of the corresponding cell, etc.

[0031] The following describes the method for optimizing coverage and capacity in the embodiments of the present disclosure with reference to the accompanying drawings. For the convenience of description, it is first described on the side of the Centralized Unit (CU) as follows:

[0032] Figure 2 is a schematic flowchart of a method for optimizing coverage and capacity provided according to an embodiment of the present disclosure. This method can be executed by the Centralized Unit (CU), as Figure 2 shown, this method includes:

[0033] Step 201, determining Coverage and Capacity Optimization (CCO) information based on an artificial intelligence model.

[0034] Step 202, sending the CCO information to the Distributed Unit (DU), where the CCO information is used to instruct the DU to perform CCO optimization actions.

[0035] Step 203, receiving the CCO optimization action indication information or CCO optimization actions sent by the DU.

[0036] Step 204, sending the CCO optimization actions to other nodes.

[0037] It should be noted that the CCO information in the embodiments of the present disclosure may include different contents in different application scenarios. The above steps 201 - 204 are described and explained in combination with different CCO information as follows:

[0038] In an embodiment of the present disclosure, the CCO information includes: CCO decisions.

[0039] The CCO decisions in this embodiment may include one or more of cell-level CCO decisions or beam-level CCO decisions. That is, in this embodiment, the CCO decisions can be based on the cell level or the beam level, and the granularity of the CCO decisions is more diversified, which helps to control the communication quality at a smaller granularity and ensures the communication quality.

[0040] Among them, the cell-level CCO decision includes one or more of the following: one or more cell identities to be modified, and the corresponding configuration status to be changed for the cell; indicating the cell information whose coverage is to be changed during the next reconfiguration. Among them, the one or more cell identities to be modified can be represented by Global NG-RAN Cell Identity, the corresponding configuration status to be changed for the cell can be represented by Cell Coverage State. In addition, indicating the cell information whose coverage is to be changed during the next reconfiguration can be represented by Cell Deployment Status Indicator. Among them, Global NG-RAN Cell Identity identifies the cell whose coverage is to be changed, and Cell Coverage State is used to indicate the specific configuration type of the corresponding covered cell (for example, the movement of the cell coverage range configuration, etc.). For example, when Cell Coverage State is 0, it indicates that the corresponding covered cell is in an inactive state, and other values indicate the specific coverage configuration type of the cell in the active state.

[0041] In some possible examples, the cell information whose coverage is to be changed during the next reconfiguration is indicated by Cell Deployment Status Indicator. For example, the coverage of the cell to be changed during the next reconfiguration is indicated by Cell Deployment Status Indicator, or the replacement of some or all of the covered cells to be replaced during the next reconfiguration is indicated by Cell Deployment Status Indicator.

[0042] The beam-level CCO decision includes one or more pieces of Synchronized Signal Block (SSB) information (such as SSB index) whose time, frequency, and phase have been synchronized and are to be modified, and the corresponding configuration status to be changed for the SSB (such as SSB Coverage State), one or more pieces of CSI-RS information to be modified, and the corresponding configuration status to be changed for the CSI-RS. Generally, the data that can be measured by CSI-RS includes three items: Channel Quality Indicator (CQI), Rank Indicator (RI), and Precoder Matrix Indicator (PMI). These three items together are called Channel State Information (CSI), which is the meaning of the existence of CSI-RS.

[0043] The CSI-RS is mainly used in the following aspects:

[0044] Obtain channel state information for measuring the channel between the base station and the UE and obtaining the channel state information required for scheduling and link adaptation, such as precoding matrix, channel quality information, etc.

[0045] Beam management for obtaining the shaping weights of the beams on the user equipment (UE) and base station sides, supporting beam measurement during the beam management process.

[0046] Time-frequency tracking for precise time-frequency synchronization tracking.

[0047] Mobility management for completing mobility management-related measurements.

[0048] In this embodiment, as Figure 3 shown, the process of determining the CCO decision in step 201 may include: receiving the underlying resource status information sent by the DU, and then determining the CCO decision according to the underlying resource status information and the artificial intelligence model, that is, inputting the underlying resource status information into the corresponding artificial intelligence model to obtain the CCO decision output by the artificial intelligence model.

[0049] Among them, the underlying resource status information in this embodiment includes, but is not limited to, Random Access Channel (RACH) reports, Radio Link Failure (RLF) reports, serving cell coverage status, Power Headroom Report (PHR), packet loss rate, average / maximum value of the number of active UEs, Reference Signal Receiving Power (RSRP), Reference Signal Receiving Quality (RSRQ), Signal to Interference plus Noise Ratio (SINR), bearer-related information (such as at least one of txDirection, configuredMaxTxPower, configuredMaxTxEIRP, arfcnDL, arfcnUL, bSChannelBwDL, bSChannelBwUL, sectorEquipmentFunctionRef, etc.), antenna-related parameters (such as at least one of coverageShape, digitalTilt, digitalAzimuth), beam-related information (such as at least one of beamIndex, beamHorizWidth, beamTilt, BeamVertWidth, beamAzimuth, beamType), etc. One or more of them. The specific underlying resource status information transmitted is related to the actual application scenario. In this embodiment, the artificial intelligence model can output different CCO decisions based on different transmitted underlying resource status information to ensure the consistency between the CCO decision and the actual scenario, and improve the flexibility of the CCO decision.

[0050] It should be noted that in different application scenarios, the CU receives the underlying resource status information sent by the DU in different ways:

[0051] In some possible embodiments, the CU sends a request message to the DU and receives the underlying resource status information sent by the DU in response to the request message. That is, in this embodiment, the CU actively requests the corresponding underlying resource status information from the DU.

[0052] In some possible embodiments, the CU receives the underlying resource status information sent by the DU based on a preset period. That is, in this embodiment, the DU actively sends the underlying resource status information periodically according to the preset period.

[0053] In some possible embodiments, the receiving DU sends the underlying resource status information based on a preset reporting condition or reporting time, where the reporting condition or reporting time is pre-configured by the CU or the DU. For example, the reporting time can be the time when there is a significant change in the number of mobile terminals carried by the DU corresponding cell. For example, the reporting condition can be that the difference between the number of mobile terminals in the DU corresponding cell and a preset standard number is greater than a preset quantity threshold. Thus, according to the reporting time and reporting condition, the reporting time of the underlying resource status information can be flexibly selected to achieve "reporting on demand".

[0054] It should be emphasized that the CCO decision in the embodiments of the present disclosure is determined based on an artificial intelligence model, not based on a preset fixed algorithm. Based on the self-learning nature of the artificial intelligence model, the flexibility and accuracy of the determined CCO decision can be guaranteed.

[0055] Further, in this embodiment, when the CCO information includes a CCO decision, continue to refer to Figure 3 , in step 202, the CCO decision is sent to the distribution unit DU, and the distribution unit DU executes the corresponding CCO decision. The CCO decision of this embodiment can be regarded as the corresponding CCO optimization behavior.

[0056] After the DU executes the corresponding CCO optimization behavior, it can send the corresponding CCO optimization behavior indication information to the CU. Thus, in step 203, the CU receives the CCO optimization behavior indication information sent by the DU. This CCO optimization behavior indication information is feedback by the DU based on the CCO information sent by the CU. In this embodiment, the CCO information includes the CCO decision determined by the CU, and the CCO decision is the CCO optimization behavior mentioned in this embodiment. Thus, the CCO optimization behavior indication information feedback by the DU to the CU based on the CCO decision sent by the CU can be an indication information on whether to execute the CCO decision, etc.

[0057] Even further, continue to refer to Figure 3 , in step 204, the CU can determine whether the DU executes the corresponding CCO decision according to the CCO optimization behavior indication information. When the CU learns from the CCO optimization behavior indication information that the DU executes the CCO optimization behavior, the CCO optimization behavior and the CCO decision are sent to other nodes, so that other nodes can perform relevant CCO adjustments based on the CCO decision.

[0058] Among them, other nodes in the embodiments of the present disclosure can be understood as control nodes for controlling other cells, and the control nodes include DUs, base stations, etc. After the capacity or coverage at the cell level or beam level of the current distributed unit DU is adjusted, the communication quality of the controlled objects corresponding to other nodes (which can be at the cell level or beam level, etc.) also changes accordingly. For example, when the controlled object of other nodes is at the cell level, if the communication quality of other cells A and B also changes after the current distributed unit DU performs CCO adjustment, the control nodes corresponding to A and B belong to the "other nodes" corresponding to the current distributed unit DU.

[0059] In an embodiment of the present disclosure, the CCO decision includes CCO prediction. Among them, the CCO prediction is determined according to an artificial intelligence model. The artificial intelligence model for determining the CCO prediction in this embodiment is different from the artificial intelligence model for determining the CCO decision in the above embodiment. The artificial intelligence model in this embodiment can perform CCO prediction based on the underlying resource status information sent by the DU.

[0060] Among them, the CCO prediction can be divided into two types of predictions:

[0061] The first type: The CCO prediction is a CCO decision prediction. The CCO information in this embodiment includes the CCO decision, as well as the time information and confidence information for executing the CCO decision. That is, the CCO prediction indicates that the corresponding CCO decision may be executed at the corresponding time information in the future, and the possibility of execution is reflected by the confidence information.

[0062] Among them, the time information for executing the CCO decision may include the time point information for executing the CCO decision or a time period information. The confidence information includes the execution probability of the CCO decision. The confidence information can be defined as an enumerated type or an integer type. The larger the value corresponding to the confidence information, the greater the probability of indicating the execution of the CCO decision. In other possible embodiments, the confidence information may also be just an identifier indicating a high execution probability of the CCO decision.

[0063] In this embodiment, as Figure 4 shown, the determination process for performing CCO decision prediction in step 201 may include: receiving the underlying resource status information sent by the DU, and determining the CCO decision prediction according to the underlying resource status information and the artificial intelligence model. Among them, the CCO decision prediction includes: the CCO decision, as well as the time information and confidence information for executing the CCO decision.

[0064] It should be emphasized that the CCO decision in the embodiments of the present disclosure, as well as the time information and confidence information for the execution of the CCO decision, are determined based on an artificial intelligence model. In this embodiment, by inputting the underlying resource status information into the artificial intelligence model, the CCO decision output by the artificial intelligence model, as well as the time information and confidence information for the execution of the CCO decision, can be obtained. Thus, the output CCO decision, as well as the time information and confidence information for the execution of the CCO decision, are not based on a preset fixed algorithm. Based on the self-learning nature of the artificial intelligence model, it can ensure the accuracy of the determined CCO decision, as well as the time information and confidence information for the execution of the CCO decision.

[0065] Further, continue to refer to Figure 4 , in step 202, the CCO decision, as well as the time information and confidence information for the execution of the CCO decision, are sent to the distributed unit DU. In step 203, the CU receives the CCO optimization behavior indication information fed back by the DU based on the CCO decision, as well as the time information and confidence information for the execution of the CCO decision sent by the CU. In this embodiment, the CCO decision, as well as the time information and confidence information for the execution of the CCO decision sent by the CU, can be regarded as a CCO optimization behavior. The indication information fed back by the DU may include feedback information on whether to execute the CCO optimization behavior. Thus, if it is known from the indication information that the DU executes the CCO optimization behavior, continue to refer to Figure 4 , in step 204, the CU sends the CCO decision, as well as the time information and confidence information for the execution of the CCO decision, to other nodes so that other nodes can perform relevant CCO adjustments, etc.

[0066] The second type: The CCO decision is a CCO problem prediction.

[0067] Among them, the CCO information in this embodiment includes: the CCO problem, and the time information and confidence information for the occurrence of the CCO problem. Similarly, the CCO problems in this embodiment include: cell-level coverage and capacity problems, and / or, beam-level coverage and capacity problems. Among them, the beam level includes: SSB and / or CSI-RS. That is, in this embodiment, future possible CCO problems, as well as the confidence information for the occurrence of the CCO problems, can be predicted based on the underlying resource status information.

[0068] Among them, the time information for the occurrence of the CCO problem includes the time point or time range for the occurrence of the CCO problem, and the confidence information indicates the possibility of the occurrence of the CCO problem, etc. The confidence information includes the occurrence probability of the CCO problem. The confidence information can be defined as an enumeration type or an integer type. The larger the value corresponding to the confidence information, the greater the indication of the occurrence probability of the CCO problem. In other possible embodiments, the confidence information may also be just an identifier indicating a high occurrence probability.

[0069] In this embodiment, as Figure 5 shown, the determination process of CCO problem prediction in step 201 may include: receiving the underlying resource status information sent by the DU, and determining the CCO problem prediction according to the underlying resource status information and the artificial intelligence model, where the CCO problem prediction includes: the CCO problem, and the time information and confidence information for executing the CCO problem. In this embodiment, the artificial intelligence model for generating the CCO problem prediction is different from the artificial intelligence model for CCO decision prediction.

[0070] It should be emphasized that the CCO problem in the embodiments of the present disclosure, as well as the time information and confidence information for executing the CCO problem, are determined based on the artificial intelligence model. In this embodiment, by inputting the underlying resource status information into the artificial intelligence model, the CCO problem output by the artificial intelligence model, as well as the time information and confidence information for executing the CCO problem, can be obtained. Thus, the output CCO problem, as well as the time information and confidence information for executing the CCO problem, are not based on a preset fixed algorithm. Based on the self-learning nature of the artificial intelligence model, the accuracy of the determined CCO problem, as well as the time information and confidence information for executing the CCO problem, can be guaranteed.

[0071] Further, continuing to refer to Figure 5 , after the CCO problem, as well as the time information and confidence information for executing the CCO problem, are sent to the DU as CCO information in step 202, a CCO decision is generated on the DU side according to the CCO problem, as well as the time information and confidence information for executing the CCO problem, and the CCO decision is sent to the CU as an optimization action. Thus, in step 203, the CCO optimization action sent by the DU is received. The CCO optimization action in this embodiment is the CCO decision sent by the DU. Furthermore, in step 204, continuing to refer to Figure 5 , the CU sends the CCO decision to other nodes to facilitate relevant CCO adjustments by other nodes.

[0072] It should be noted that referring to the above embodiments, in the disclosed embodiments, the CCO optimization action sent by the CU to other nodes in step 204 may include the CCO decision generated by the CU, or the CCO decision generated by the DU, etc.

[0073] Step 205, receiving the feedback information on the CCO optimization action sent by the DU and other nodes.

[0074] In an embodiment of the present disclosure, regardless of which of the above embodiments, referring to Figures 3 - 5 , the CU will receive the feedback information on the CCO adjustment executed according to the CCO decision sent by the DU and other nodes.

[0075] In some possible embodiments, the feedback information received by the DU from other nodes regarding the CCO optimization behavior includes: the CU receiving the key performance indicator (KPI) information of the DU's execution of the CCO optimization behavior feedback, and the CCO optimization behavior indication information sent by other nodes (i.e., indications such as whether other nodes perform CCO adjustments according to the corresponding CCO optimization behavior), as well as the key performance indicator (KPI) information of the execution of the CCO optimization behavior feedback.

[0076] Among them, in some possible embodiments, the key performance indicator (KPI) information may include any indicator information for indicating the communication network performance after CCO adjustment, including but not limited to access capability indicators (connection success rate), retention indicators (call drop rate), availability indicators (uplink and downlink throughput), mobility indicators (handover success rate), etc.

[0077] In summary, for the coverage and capacity optimization processing method provided by the embodiments of the present disclosure, the central unit CU determines the coverage and capacity optimization CCO information based on the artificial intelligence model, and then sends the CCO information to the distributed unit DU. Among them, the CCO information is used to instruct the DU to perform the CCO optimization behavior, receive the CCO optimization behavior indication information or CCO optimization behavior sent by the DU, and then send the CCO optimization behavior to other nodes, and receive the feedback information about the CCO optimization behavior sent by the DU and other nodes. In this technical solution, the artificial intelligence model can flexibly and comprehensively consider the underlying resource information related to the actual scenario to determine the CCO information, ensuring the accuracy of the CCO information determination. Furthermore, it ensures the accuracy and flexibility of the CCO decision-making, providing a guarantee for the communication quality.

[0078] Next, the coverage and capacity optimization processing method of the embodiments of the present disclosure will be mainly described on the distributed unit DU side.

[0079] Figure 6 is a flowchart of a coverage and capacity optimization processing method provided by the embodiments of the present disclosure. This method can be executed by the central unit DU, as Figure 6 shown, the method includes:

[0080] Step 601, receiving the coverage and capacity optimization CCO information determined by the central unit CU based on the artificial intelligence model.

[0081] Step 602, performing the CCO optimization behavior according to the CCO information.

[0082] Step 603, sending the CCO optimization behavior indication information or CCO optimization behavior to the CU.

[0083] In an embodiment of the present disclosure, the DU receives CCO information for coverage and capacity optimization determined based on an artificial intelligence model sent by the CU, and performs CCO optimization actions according to the CCO information. Among them, in some possible embodiments, the CCO information may include: a CCO decision, where the CCO decision is determined by the CU based on the underlying resource status information sent by the DU and the artificial intelligence model;

[0084] In this embodiment, in response to a request message sent by the CU, the DU sends the underlying resource status information to the CU, or sends the underlying resource status information to the CU based on a preset period; or,

[0085] The DU sends the underlying resource status information to the CU based on a preset reporting condition or reporting time, where the reporting condition or reporting time is pre-configured by the CU or the DU.

[0086] In this embodiment, the CCO decision is a CCO optimization action, and the DU can perform the corresponding CCO optimization action. Furthermore, in step 603, the DU sends CCO optimization action indication information to the CU to feedback to the CU whether the CCO decision sent by the CU is executed on the DU side.

[0087] In some possible embodiments, the CCO information may include: a CCO prediction, where the CCO prediction may include: a CCO decision prediction, and the CCO decision prediction is determined by the CU based on the underlying resource status information sent by the DU and the artificial intelligence model, where the CCO decision prediction includes: a CCO decision, and time information and confidence information for executing the CCO decision.

[0088] In this embodiment, the CCO decision prediction is a CCO optimization action, and the DU can perform the corresponding CCO optimization action. Furthermore, in step 603, the DU sends CCO optimization action indication information to the CU to indicate whether to execute the CCO decision corresponding to the CCO decision prediction.

[0089] In some possible embodiments, the CCO information may further include: a CCO problem prediction, and the CCO problem prediction is determined by the CU based on the artificial intelligence model, where the CCO problem prediction includes: a CCO problem, and time information and confidence information for the occurrence of the CCO problem. In this embodiment, the DU generates a CCO optimization action according to the CCO problem prediction, where the CCO optimization action may include a CCO decision predicted by the DU, etc. When performing the CCO optimization action, in step 603, the DU can send the CCO decision to the CU as the CCO optimization action.

[0090] Step 604, send feedback information about the CCO optimization action to the CU.

[0091] In an embodiment of the present disclosure, the DU sends feedback information on the CCO optimization behavior to the CU. In some possible embodiments, the feedback information may include key metric information, so that the CU can learn the key metric information after the DU executes the corresponding CCO optimization behavior based on the feedback information. The CU can learn whether the performance of the communication network has been improved after the DU executes the CCO decision based on the key metric information, etc.

[0092] It should be noted that the present disclosure focuses on the processing method for coverage and capacity optimization on the distributed unit DU side, which corresponds to the above-mentioned processing method for coverage and capacity optimization on the CU side. For the implementation details of the relevant embodiments of the processing method for coverage and capacity optimization on the DU side, reference can be made to the embodiments of the processing method for coverage and capacity optimization on the CU side above, and will not be elaborated here.

[0093] In summary, the processing method for coverage and capacity optimization in the embodiments of the present disclosure receives the coverage and capacity optimization CCO information determined based on the artificial intelligence model sent by the central unit CU. Furthermore, according to the CCO information, it executes the CCO optimization behavior, sends the CCO optimization behavior indication information or the CCO optimization behavior to the CU, and sends the feedback information on the CCO optimization behavior to the CU. In this technical solution, the CCO decision is made based on the artificial intelligence model, which ensures the accuracy of the CCO decision and provides a guarantee for the communication quality.

[0094] To implement the above-mentioned processing method for coverage and capacity optimization, the present disclosure also proposes a processing device for coverage and capacity optimization, as Figure 7 shown in the block diagram of the entity structure of a processing device for coverage and capacity optimization. Among them, the processing device for coverage and capacity optimization can be applied to the central unit CU and includes: a memory 710, a transceiver 720, and a processor 730. Among them,

[0095] The memory 710 is used to store programs; the transceiver 720 is used to send and receive data under the control of the processor 710; the processor 730 is used to read the programs in the memory and perform the following operations:

[0096] Determine the coverage and capacity optimization CCO information based on the artificial intelligence model;

[0097] Send the CCO information to the distributed unit DU, where the CCO information is used to instruct the DU to execute the CCO optimization behavior;

[0098] Receive the CCO optimization behavior indication information or the CCO optimization behavior sent by the DU;

[0099] Send the CCO optimization behavior to other nodes;

[0100] Receive the feedback information on the CCO optimization behavior sent by the DU and other nodes.

[0101] Among them, in Figure 7 , the bus architecture may include any number of interconnected buses and bridges, specifically linking together various circuits of one or more processors represented by the processor and the memory represented by the memory. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and thus will not be further described herein. The bus interface provides an interface. The transceiver may be multiple components, that is, including a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium, and these transmission mediums include wireless channels, wired channels, optical fiber cables, and other transmission mediums. For different user devices, the user interface may also be an interface capable of externally and internally connecting required devices, and the connected devices include but are not limited to a keypad, a display, a speaker, a microphone, a joystick, etc.

[0102] The processor is responsible for managing the bus architecture and general processing, and the memory may store data used by the processor when executing operations.

[0103] Optionally, the processor may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field - Programmable Gate Array), or a CPLD (Complex Programmable Logic Device), and the processor may also adopt a multi - core architecture.

[0104] The processor is used to execute any method provided by the embodiments of the present disclosure according to the obtained executable instructions by calling the program stored in the memory. The processor and the memory may also be physically separated.

[0105] It should be noted here that the above - mentioned device provided by the embodiments of the present disclosure can implement all the method steps of coverage and capacity optimization concentrated on the CU side implemented by the above - mentioned method embodiments, and can achieve the same technical effects. The same parts and beneficial effects as those in the method embodiments will not be specifically described herein.

[0106] In order to implement the above - mentioned processing method for coverage and capacity optimization, the present disclosure also proposes a processing device for coverage and capacity optimization, and the structure of the processing device for coverage and capacity optimization may refer to the above Figure 7, wherein the coverage and capacity optimization processing device in this embodiment can be applied to a distributed unit DU, and includes: a memory, a transceiver, and a processor: The memory is used to store programs; the transceiver is used to transmit and receive data under the control of the processor; the processor is used to read the programs in the memory and perform the following operations:

[0107] Receive the coverage and capacity optimization CCO information determined based on the artificial intelligence model sent by the central unit CU,

[0108] Execute CCO optimization actions according to the CCO information;

[0109] Send CCO optimization action indication information or CCO optimization actions to the CU;

[0110] Send feedback information about the CCO optimization actions to the CU.

[0111] It should be noted here that the above device provided by the embodiments of the present disclosure can implement all the method steps of the coverage and capacity optimization centralized on the DU side in the above method embodiments, and can achieve the same technical effects. The same parts and beneficial effects as those in the method embodiments in this embodiment will not be specifically described here.

[0112] To implement the above embodiments, the present disclosure also proposes a coverage and capacity optimization processing device. This coverage and capacity optimization processing device is applied to the central unit CU. Figure 8 It is a schematic structural diagram of a coverage and capacity optimization processing device according to an embodiment of the present disclosure. As Figure 8 shown, this coverage and capacity optimization processing device includes a determination module 810, a first sending module 820, a first receiving module 830, a second sending module 840, and a second receiving module 850, wherein,

[0113] The determination module 810 is used to determine coverage and capacity optimization CCO information based on the artificial intelligence model;

[0114] The first sending module 820 is used to send the CCO information to the distributed unit DU, where the CCO information is used to instruct the DU to execute CCO optimization actions;

[0115] The first receiving module 830 is used to receive the CCO optimization action indication information or CCO optimization actions sent by the DU;

[0116] The second sending module 840 is used to send CCO optimization actions to other nodes;

[0117] The second receiving module 850 is used to receive feedback information about the CCO optimization actions sent by the DU and other nodes.

[0118] To implement the above embodiments, the present disclosure also proposes a processing device for coverage and capacity optimization. The processing device for coverage and capacity optimization is applied to a distributed unit (DU). Figure 9 It is a schematic structural diagram of a processing device for coverage and capacity optimization according to another embodiment of the present disclosure, as Figure 9 shown. The processing device for coverage and capacity optimization includes a third receiving module 910, an execution processing module 920, a third sending module 930, and a fourth sending module 940. Among them,

[0119] The third receiving module 910 is configured to receive coverage and capacity optimization (CCO) information determined based on an artificial intelligence model sent by a central unit (CU).

[0120] The execution processing module 920 is configured to perform CCO optimization actions according to the CCO information;

[0121] The third sending module 930 is configured to send CCO optimization action indication information or CCO optimization actions to the CU;

[0122] The fourth sending module 940 is configured to send feedback information about the CCO optimization actions to the CU.

[0123] It should be noted that the division of units in the embodiments of the present disclosure is illustrative. It is only a logical function division, and there may be other division methods in actual implementation. In addition, in each embodiment of the present disclosure, each functional unit may be integrated into a processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above integrated units may be implemented in the form of hardware or in the form of software functional units.

[0124] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a processor-readable storage medium. Based on such an understanding, the technical solution of the present disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in the embodiments of the present disclosure.

[0125] It should be noted here that the above device provided in the embodiments of the present disclosure can implement all the processing method steps of coverage and capacity optimization implemented in the above method embodiments, and can achieve the same technical effects. The same parts and beneficial effects as those in the processing method embodiments of coverage and capacity optimization in this embodiment will not be specifically described herein.

[0126] To implement the above embodiments, the present disclosure also provides a processor-readable storage medium storing a program for executing the above-described coverage and capacity optimization processing method.

[0127] The processor-readable storage medium can be any available medium or data storage device accessible by the processor, including but not limited to magnetic memories (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical memories (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor memories (such as ROMs, EPROMs, EEPROMs, non-volatile memories (NAND FLASH), solid-state drives (SSD)).

[0128] Those skilled in the art should understand that the embodiments of the present disclosure can be provided as a method, a system, or a computer program product. Therefore, the present disclosure can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories and optical memories, etc.) containing computer-usable program code.

[0129] The present disclosure is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0130] These processor-executable instructions can also be stored in a processor-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the processor-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0131] Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalent technologies, the present disclosure is also intended to include these modifications and variations.

Claims

1. A processing method for coverage and capacity optimization, characterized in that, The method is applied to a Central Unit (CU), and includes: Determining Coverage and Capacity Optimization (CCO) information based on an artificial intelligence model; Sending the CCO information to a Distributed Unit (DU), where the CCO information is used to instruct the DU to perform CCO optimization actions; Receiving the CCO optimization action indication information or the CCO optimization actions sent by the DU; Sending the CCO optimization actions to other nodes; Receiving feedback information about the CCO optimization actions sent by the DU and the other nodes.

2. The method according to claim 1, wherein The CCO information includes: A CCO decision, where the determination process of the CCO decision includes: Receiving the underlying resource status information sent by the DU; Determining the CCO decision according to the underlying resource status information and the artificial intelligence model.

3. The method according to claim 1, wherein The CCO information includes: A CCO prediction, where the CCO prediction is determined according to the artificial intelligence model.

4. The method according to claim 3, wherein The CCO prediction includes: a CCO decision prediction, and the determination process of the CCO decision prediction includes: Receiving the underlying resource status information sent by the DU; Determining the CCO decision prediction according to the underlying resource status information and the artificial intelligence model, where the CCO decision prediction includes: a CCO decision, and time information and confidence information for executing the CCO decision.

5. The method according to claim 3, wherein The CCO prediction includes: a CCO problem prediction, and the determination process of the CCO problem prediction includes: Determining the CCO problem prediction according to the artificial intelligence model, where the CCO problem prediction includes: a CCO problem, and time information and confidence information for the occurrence of the CCO problem; where The CCO problems include: cell-level CCO problems, and / or, beam-level CCO problems.

6. The method according to claim 2 or 4, characterized in that The CCO decision includes: Cell-level CCO decisions, and / or, beam-level CCO decisions; where The cell-level CCO decisions include one or more of the following: One or more cell identifiers to be modified, and the corresponding configuration status to be changed for the cells; Indicating cell information for which coverage is to be changed during the next reconfiguration; The beam-level CCO decisions include one or more of the following: One or more Synchronization Signal Block (SSB) information to be modified, and the corresponding configuration status to be changed for the SSBs; One or more Channel State Information - Reference Signals (CSI-RS) information to be modified, and the corresponding configuration status to be changed for the CSI-RSs.

7. The method according to claim 2 or 4, characterized in that, The receiving the underlying resource status information sent by the DU includes: Sending a request message to the DU and receiving the underlying resource status information sent by the DU in response to the request message; or, Receiving the underlying resource status information sent by the DU based on a preset period; or, Receiving the underlying resource status information sent by the DU based on preset reporting conditions or reporting times, where the reporting conditions or reporting times are pre-configured by the CU or the DU.

8. The method according to claim 2 or 4, characterized in that, The underlying resource status information includes one or more of the following: RACH reports, RLF reports, serving cell coverage status, PHR information, packet loss rate, average / maximum number of active UEs, RSRP, RSRQ, SINR, bearer-related information, antenna-related parameters, beam-related information.

9. The method according to claim 2 or 4, characterized in that, Receiving the CCO optimization behavior indication information or the CCO optimization behavior sent by the DU includes: Receiving the CCO optimization behavior indication information fed back by the DU based on the CCO information sent by the CU, where the CCO information determined by the CU is used as the CCO optimization behavior; Sending the CCO optimization behavior to other nodes includes: When it is known from the CCO optimization behavior indication information that the DU executes the CCO optimization behavior, sending the CCO optimization behavior to other nodes.

10. The method according to claim 5, wherein Receiving the CCO optimization behavior indication information or the CCO optimization behavior sent by the DU includes: Receiving the CCO optimization behavior fed back by the DU based on the CCO information sent by the CU, where the CCO optimization behavior is determined by the DU based on the CCO information.

11. The method according to claim 1, wherein Receiving the feedback information about the CCO optimization behavior sent by the DU and the other node includes: Receiving the key performance indicator (KPI) information fed back by the DU after executing the CCO optimization behavior; and Receiving the CCO optimization behavior indication information sent by the other node and the key performance indicator (KPI) information fed back after executing the CCO optimization behavior.

12. A processing method for coverage and capacity optimization, characterized in that, The method is applied to a distributed unit (DU) and includes: Receiving the coverage and capacity optimization (CCO) information determined based on an artificial intelligence model sent by a central unit (CU); Performing a CCO optimization behavior according to the CCO information; Sending the CCO optimization behavior indication information or the CCO optimization behavior to the CU; Sending feedback information about the CCO optimization behavior to the CU.

13. The method according to claim 12, wherein The CCO information includes: A CCO decision, where the CCO decision is determined by the CU according to the underlying resource status information sent by the DU and the artificial intelligence model.

14. The method according to claim 12, wherein The CCO information includes a CCO decision prediction, The CCO decision prediction is determined by the CU according to the underlying resource status information sent by the DU and the artificial intelligence model, where the CCO decision prediction includes: a CCO decision, and time information and confidence information for executing the CCO decision.

15. The method according to claim 13 or 14, characterized in that It further includes: In response to a request message sent by the CU, sending the underlying resource status information to the CU; Or, Sending the underlying resource status information to the CU based on a preset period; or Sending the underlying resource status information to the CU based on preset reporting conditions or reporting times, where the reporting conditions or reporting times are pre-configured by the CU or the DU.

16. The method according to claim 13 or 14, characterized in that, Sending the CCO optimization behavior indication information or the CCO optimization behavior to the CU according to the CCO information includes: Performing a CCO optimization behavior according to the CCO information; Sending the CCO optimization behavior indication information to the CU.

17. The method according to claim 12, wherein The CCO information includes: CCO problem prediction, where the CCO problem prediction is determined by the CU based on an artificial intelligence model. The CCO problem prediction includes: a CCO problem, and time information and confidence information of the occurrence of the CCO problem.

18. The method according to claim 17, characterized in that Sending CCO optimization behavior indication information or a CCO optimization behavior to the CU according to the CCO information includes: Generating a CCO optimization behavior according to the CCO problem prediction; When the CCO optimization behavior is executed, sending the CCO optimization behavior to the CU.

19. A processing device for coverage and capacity optimization, characterized in that, The device is applied to a central unit CU and includes: a memory, a transceiver, and a processor: The memory is used for storing programs; the transceiver is used for transceiving data under the control of the processor; the processor is used for reading the programs in the memory and performing the following operations: Determining coverage and capacity optimization CCO information based on an artificial intelligence model; Sending the CCO information to a distributed unit DU, where the CCO information is used to instruct the DU to perform a CCO optimization behavior; Receiving the CCO optimization behavior indication information or the CCO optimization behavior sent by the DU; Sending the CCO optimization behavior to other nodes; Receiving feedback information about the CCO optimization behavior sent by the DU and the other nodes.

20. A processing device for coverage and capacity optimization, characterized in that, The device is applied to a distributed unit DU and includes: A memory, a transceiver, and a processor: The memory is used for storing programs; the transceiver is used for transceiving data under the control of the processor; the processor is used for reading the programs in the memory and performing the following operations: Receiving coverage and capacity optimization CCO information determined by the central unit CU based on an artificial intelligence model, Performing a CCO optimization behavior according to the CCO information; Sending the CCO optimization behavior indication information or the CCO optimization behavior to the CU; Sending feedback information about the CCO optimization behavior to the CU.

21. A processing device for coverage and capacity optimization, characterized in that, The device is applied to a central unit CU and includes: A determination module for determining coverage and capacity optimization CCO information based on an artificial intelligence model; A first sending module for sending the CCO information to a distributed unit DU, where the CCO information is used to instruct the DU to perform a CCO optimization behavior; A first receiving module for receiving the CCO optimization behavior indication information or the CCO optimization behavior sent by the DU; A second sending module for sending the CCO optimization behavior to other nodes; A second receiving module for receiving feedback information about the CCO optimization behavior sent by the DU and the other nodes.

22. A processing device for coverage and capacity optimization, characterized in that, The device is applied to a distributed unit DU and includes: A third receiving module for receiving coverage and capacity optimization CCO information determined by the central unit CU based on an artificial intelligence model, An execution processing module for performing a CCO optimization behavior according to the CCO information; A third sending module for sending the CCO optimization behavior indication information or the CCO optimization behavior to the CU; A fourth sending module for sending feedback information about the CCO optimization behavior to the CU.

23. A processor-readable storage medium, characterized in that, The storage medium stores a program, which is used to execute the coverage and capacity optimization processing method described in any one of claims 1-11 above, or to execute the coverage and capacity optimization processing method described in any one of claims 12-18 above.

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