Communication method and apparatus

Abstract

A communication method and apparatus, relating to the technical field of communications, for use in improving the efficiency of objective optimization. The method comprises: acquiring information of an optimization area and a target indicator, the information of the optimization area indicating the optimization area; and on the basis of the information of the optimization area, sending to a management service consumer an optimization result corresponding to the optimization area, the optimization result comprising a policy item, and the policy item comprising a recommendation value of a network parameter corresponding to the target indicator and an indicator value corresponding to the recommendation value.

Description

A communication method and apparatus

[0001] Cross-references to related applications

[0002] This application claims priority to Chinese Patent Application No. 202410667235.X, filed on May 27, 2024, entitled "A Communication Method and Apparatus", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of communication technology, and in particular to a communication method and apparatus. Background Technology

[0004] When planning wireless communication network systems, network operators adjust network system configuration strategies to maximize optimization across one or more network performance metrics, thereby achieving target optimization. Taking energy-saving optimization of wireless communication network systems as an example, optimization can be performed on energy consumption metrics and other metrics (such as terminal experience rate) to achieve the lowest energy consumption within the maximum optimization space for terminal experience rate.

[0005] For wireless communication network systems, improving the efficiency of target optimization is a problem that needs to be solved. Summary of the Invention

[0006] This application provides a communication method and apparatus to improve the efficiency of target optimization.

[0007] The method provided in this application can be applied to a system for implementing network optimization and the functional entities included in that system. The system may include a performance analysis functional entity and an optimization functional entity. The performance analysis functional entity can be used to determine an optimization region, and the optimization functional entity can be used to determine the optimization result corresponding to that optimization region. Alternatively, the performance analysis function and the optimization function can be combined into a single functional entity.

[0008] Firstly, a communication method is provided, which can be applied to an optimization function entity, or to an entity that includes performance analysis and optimization functions. The method may include: acquiring information about an optimization region and a target indicator, wherein the information about the optimization region indicates the optimization region; and, based on the information about the optimization region, sending optimization results corresponding to the optimization region to a management service consumer, wherein the optimization results include policy items, and the policy items include recommended values ​​for network parameters corresponding to the target indicator and indicator values ​​corresponding to the recommended values.

[0009] In one possible implementation, the optimization area is a portion of the area covered by the operator's network.

[0010] In the above implementation method, since network optimization is performed based on the optimization area, the optimization range can be narrowed and the efficiency of target optimization can be improved compared with network optimization across the entire network.

[0011] In one possible implementation method, the target index includes at least two indicators, thereby enabling multi-objective collaborative optimization based on multiple objectives.

[0012] In one possible implementation, the strategy item includes at least two strategy items, the number of the at least two strategy items is the same as the number of the at least two indicators, each of the at least two strategy items corresponds to a main indicator, the main indicator is one of the at least two indicators, and the main indicators corresponding to different strategy items are different from each other; the recommended value of the network parameter included in each of the at least two strategy items is obtained by using the main indicator corresponding to the strategy item as the key indicator.

[0013] In the above implementation, since the strategy terms are obtained based on specified target metrics and network parameters, optimization overhead can be reduced and optimization efficiency improved. Furthermore, by using one of multiple target metrics as the key metric for optimization, multi-target collaborative optimization is achieved.

[0014] In one possible implementation, the at least two policy items are statistically obtained from the policy items in the optimization results obtained from at least two iteration rounds, thereby making it convenient for the management service consumer to select a policy item from the received policy items for network configuration.

[0015] In one possible implementation, the optimization result further includes one or more of the following: the number of iterations, the total duration of the iterations, and the average duration of the iterations. The average duration of the iterations is equal to the total duration of the iterations divided by the number of iterations. By including the above information in the optimization result, the optimization process can be evaluated based on this information.

[0016] In one possible implementation, the optimization result includes the optimization result obtained in each iteration round, wherein the optimization result obtained in the Nth iteration round includes the strategy term obtained in the Nth iteration round, where N is an integer greater than or equal to 1.

[0017] In one possible implementation, the optimization result obtained in the Nth iteration also includes the index change rate, which is the ratio of the first difference to the index value in the strategy item obtained in the (N-1)th or Nth iteration, where the first difference is the difference between the index value in the strategy item obtained in the Nth iteration and the index value in the strategy item obtained in the (N-1)th iteration.

[0018] In the above implementation, the optimization results obtained in each iteration round include the rate of change of indicators, which can be used as the basis for the management service consumer to select the optimal strategy item, so as to facilitate the management service consumer to select the better strategy item from the multiple received strategy items for network configuration.

[0019] In one possible implementation, the optimization results obtained from the N iterations also include the duration of the Nth iteration, which is used to evaluate the optimization process.

[0020] In one possible implementation, the optimization area includes: an area selected from the candidate areas based on the index values ​​corresponding to the collected candidate areas. The candidate areas are areas within the coverage area of ​​the operator's network.

[0021] In one possible implementation, the optimization region further includes regions adjacent to the regions selected from the candidate regions. This implementation can appropriately expand the scope of the optimization region, thereby improving the rationality and reliability of the optimization results.

[0022] In one possible implementation, the optimization region is a region whose index value satisfies the constraint conditions selected from the candidate regions.

[0023] Optionally, the region in the candidate region whose index value meets the restriction condition is: the region in the candidate region whose index value meets the tolerance requirement among the K candidate regions with the worst index value; where K is an integer greater than 1.

[0024] Optionally, the region where the index value meets the tolerance requirement is: the region where the index value is worse than a first value, where the first value is equal to the product of the average of the index values ​​corresponding to the K candidate regions and the first percentage of the tolerance indication.

[0025] One possible implementation further includes: obtaining the optimization result corresponding to the optimization region based on the network model, wherein the coverage area corresponding to the network model matches the optimization region. For example, after receiving a request message from a management service consumer, the implementation may further include the following steps: determining a network model based on the optimization region, wherein the coverage area corresponding to the network model matches the optimization region; and obtaining the optimization result based on the network model.

[0026] Optionally, the network model is a digital twin network model.

[0027] In the above implementation, since optimization is performed using a network model (such as a digital twin network model), the impact on the existing network can be reduced compared to optimization within the existing network. Furthermore, using a network model for optimization, especially multi-objective collaborative optimization based on a network model, can improve optimization efficiency.

[0028] In one possible implementation, obtaining the information of the optimization region includes: receiving a first request message from the management service consumer, the first request message including the information of the optimization region, the first request message being used to request the optimization result corresponding to the optimization region.

[0029] The above implementation method allows the optimization process to be triggered by the management service consumer.

[0030] Optionally, the first request message may also include the target metric (such as the name of the target metric). This implementation allows the management service consumer to specify the target metric according to its own needs.

[0031] Optionally, the first request message may also include indication information for the network parameters, which indicates the name, type, or threshold of the network parameters. This implementation allows management service consumers to specify network parameters according to their needs.

[0032] In one possible implementation, before receiving the first request message from the management service consumer, the method further includes: receiving a second request message from the management service consumer, the second request message being used to request information on the optimization region; and in response to the second request message, selecting an optimization region from the candidate regions based on the index values ​​corresponding to the collected candidate regions.

[0033] The above implementation method enables the optimization process to be triggered by the management service consumer.

[0034] Optionally, the second request message may also include the target metric (such as the name of the target metric).

[0035] Optionally, the second request message may also include indication information of the network parameters, which is used to indicate the name, type, or threshold of the network parameters.

[0036] Optionally, the second request message may also include region count indication information and / or tolerance, the region count indication information and the tolerance being used to select an optimal region from the candidate regions.

[0037] In one possible implementation, obtaining the information of the optimization region includes: receiving a third request message from the management service consumer, the third request message being used to request the determination of the optimization region and to obtain the optimization result corresponding to the optimization region; in response to the third request message, selecting the optimization region from the candidate regions according to the index values ​​corresponding to the collected candidate regions.

[0038] The above implementation method enables the performance analysis function entity to autonomously trigger the optimization function entity to perform optimization.

[0039] Optionally, the third request message may include the target metric (such as the name of the target metric).

[0040] Optionally, the third request message may also include indication information of the network parameters, which is used to indicate the name, type, or threshold of the network parameters.

[0041] Optionally, the third request message may also include region count indication information and / or tolerance, the region count indication information and the tolerance being used to select the optimal region from the candidate regions.

[0042] In one possible implementation, obtaining the information of the optimization region includes: selecting the optimization region from the candidate regions according to the index values ​​corresponding to the collected candidate regions, based on the optimization cycle.

[0043] One possible implementation further includes obtaining indication information of the network parameters, the indication information of the network parameters including one or more of the following: a threshold of at least one network parameter; or a modeling parameter type associated with the at least one network parameter; or, indication information of an optimization algorithm associated with at least one modeling parameter type, each of the at least one modeling parameter type being associated with at least one network parameter.

[0044] Secondly, a communication method is provided, which can be applied to a performance analysis function entity, or to an entity that includes both performance analysis and optimization functions. The method includes: receiving a second request message from a service management consumer, the second request message requesting information about an optimization region; and responding to the second request message by sending the optimization region information to the management service consumer, the optimization region including regions selected from the candidate regions based on collected indicator values ​​corresponding to candidate regions.

[0045] Thirdly, a communication method is provided, which can be applied to a performance analysis function entity, or to an entity that includes both performance analysis and optimization functions. The method includes: receiving a third request message from a service management consumer, the third request message being used to request the acquisition of an optimization region and the corresponding optimization result; and responding to the third request message by sending information about the optimization region to the management service consumer, the optimization region including a region selected from the candidate regions based on the collected indicator values ​​corresponding to the candidate regions.

[0046] In one possible implementation of the second or third aspect described above, the optimization region further includes: a candidate region adjacent to the region selected from the candidate regions.

[0047] One possible implementation of the second or third aspect mentioned above further includes: selecting regions whose index values ​​meet the constraints from the candidate regions based on the index values ​​corresponding to the collected candidate regions.

[0048] In one possible implementation of the second or third aspect above, the step of selecting regions whose index values ​​meet the constraints from the candidate regions based on the index values ​​corresponding to the collected candidate regions includes: selecting regions whose index values ​​meet the tolerance requirements from the K candidate regions with the worst index values ​​based on the index values ​​corresponding to the collected candidate regions; where K is an integer greater than 1.

[0049] In one possible implementation of the second or third aspect above, selecting the region whose index value meets the tolerance requirement from the K candidate regions with the worst index values ​​includes: selecting the region whose index value is worse than a first value from the K candidate regions, where the first value is equal to the product of the average index value corresponding to the K candidate regions and the first percentage of the tolerance indication.

[0050] In one possible implementation of the second or third aspect described above, the second request message or the third request message includes region number indication information and / or tolerance, the region number indication information and the tolerance being used to select an optimal region from the candidate regions.

[0051] In one possible implementation of the second or third aspect described above, the optimization area is a portion of the area covered by the first operator's network.

[0052] Fourthly, a communication method is provided that can be applied to managing service consumers. The method includes: receiving optimization results corresponding to an optimization region, the optimization results including a strategy item, the strategy item including recommended values ​​for network parameters corresponding to the target indicator and indicator values ​​corresponding to the recommended values; and configuring the network according to the strategy item.

[0053] In one possible implementation, the target metric includes at least two metrics, and the policy item includes at least two policy items; the network configuration based on the policy item includes: selecting a policy item from the at least two policy items based on a preference metric, wherein the preference metric is one of the at least two target metrics; and configuring the network based on the selected policy item.

[0054] In one possible implementation, before receiving the optimization result corresponding to the optimization region, the method further includes: sending a first request message, wherein the first request message is used to request the acquisition of the optimization result corresponding to the optimization region.

[0055] In one possible implementation, before sending the first request message, the method further includes: sending a second request message, the second request message being used to request information about the optimization region; and receiving a response message corresponding to the second request message, the response message including the information about the optimization region.

[0056] In one possible implementation, before receiving the optimization result corresponding to the optimization region, the method further includes: sending a third request message, the third request message being used to request the acquisition of the optimization result corresponding to the optimization region based on the optimization region.

[0057] Fifthly, a communication system is provided, comprising a performance analysis function entity and an optimization function entity, wherein the performance analysis function entity is used to implement the method as described in any one of the second or third aspects, and the optimization function entity is used to implement the method as described in any one of the first aspects.

[0058] A sixth aspect provides a communication apparatus comprising a unit or module for performing the method as described in any of the first aspects, or comprising a unit or module for performing the method as described in any of the second aspects, or comprising a unit or module for performing the method as described in any of the third aspects, or comprising a unit or module for performing the method as described in any of the fourth aspects.

[0059] A seventh aspect provides a communication apparatus comprising: one or more processors configured to perform the method as described in any one of the first aspects, or to perform the method as described in any one of the second aspects, or to perform the method as described in any one of the third aspects, or to perform the method as described in any one of the fourth aspects.

[0060] Eighthly, a readable storage medium is provided, the readable storage medium storing a program or instructions that, when executed on a communication device, cause the communication device to perform the method as described in any one of the first aspects, or the method as described in any one of the second aspects, or the method as described in any one of the third aspects, or the method as described in any one of the fourth aspects.

[0061] A ninth aspect provides a chip system including a processor for supporting a computer device in implementing the method as described in any one of the first aspects, or the method as described in any one of the second aspects, or the method as described in any one of the third aspects, or the method as described in any one of the fourth aspects.

[0062] In a tenth aspect, a program product is provided, the program product comprising a program; when the program is run on a computer, the computer performs the method as described in any one of the first aspects, or performs the method as described in any one of the second aspects, or performs the method as described in any one of the third aspects, or performs the method as described in any one of the fourth aspects. Attached Figure Description

[0063] Figure 1 is a schematic diagram of a system architecture provided in an embodiment of this application;

[0064] Figure 2 is a schematic diagram of another system architecture provided in an embodiment of this application;

[0065] Figure 3 is a schematic diagram of another system architecture provided in an embodiment of this application;

[0066] Figure 4 is a flowchart illustrating a communication method provided in an embodiment of this application;

[0067] Figure 5 is a flowchart illustrating another communication method provided in an embodiment of this application;

[0068] Figure 6 is a flowchart illustrating another communication method provided in an embodiment of this application;

[0069] Figure 7 is a flowchart illustrating another communication method provided in an embodiment of this application;

[0070] Figure 8 is a flowchart illustrating another communication method provided in an embodiment of this application;

[0071] Figure 9 is a schematic diagram of the structure of a communication device provided in an embodiment of this application;

[0072] Figure 10 is a schematic diagram of another communication device provided in an embodiment of this application;

[0073] Figure 11 is a schematic diagram of another communication device provided in an embodiment of this application. Detailed Implementation

[0074] When planning wireless communication network systems, it is necessary to achieve maximum optimization of target indicators to achieve target optimization. The current target optimization method is to iterate the configuration strategy for target indicators in the existing network.

[0075] Taking the coordinated optimization of terminal experience rate and energy consumption metrics as an example, the traditional method is as follows: First, optimize the network system for the terminal experience rate metric, generate Strategy 1, and distribute it to the live network, waiting for performance feedback from the live network to determine the optimization effect; then, based on the optimization effect of the live network, generate Strategy 2 and distribute it to the live network, iterating in this way until the network system has no room for optimization in the terminal experience rate metric. Next, optimize the network system for the energy consumption metric, generate Strategy 3, and distribute it to the live network, waiting for performance feedback from the live network to determine the optimization effect; then, based on the optimization effect of the live network, generate Strategy 4 and distribute it to the live network, stopping the iteration when the terminal experience rate metric is affected by degradation. This achieves coordinated optimization of terminal experience rate and energy consumption.

[0076] The above process requires iteration across the entire operator's network, resulting in long processing times and low optimization efficiency. When there are multiple optimization targets (such as energy consumption and speed metrics), the time consumption is even longer and the optimization efficiency is even lower due to the network-wide iteration and the sequential iteration of multiple optimization targets.

[0077] Therefore, embodiments of this application provide a communication method and related apparatus for implementing the method, in order to improve the efficiency of target optimization. Embodiments of this application are described below.

[0078] To better understand the embodiments of this application, some terms and technologies involved in the embodiments of this application will be explained below so that those skilled in the art can understand them.

[0079] (I) Performance Indicators of Wireless Communication Network Systems

[0080] Performance metrics (hereinafter referred to as metrics) of wireless communication network systems can be used to evaluate or measure the performance and quality of wireless communication network systems.

[0081] The metrics of a wireless communication network system can be categorized from multiple dimensions of network planning objectives. For example, they can be categorized from multiple dimensions such as energy saving, coverage, capacity, quality, and data service capabilities.

[0082] (1) Indicators related to energy conservation targets

[0083] Indicators related to energy conservation targets can include communication network energy efficiency indicators (also simply called energy efficiency indicators). Energy efficiency indicators are used to evaluate the energy efficiency of wireless communication networks. They can be expressed as the amount of energy consumed per unit of time, such as kilowatt-hours per day. The energy consumption in energy efficiency indicators refers to total energy consumption, which includes the energy consumption of communication equipment and infrastructure. Accordingly, energy efficiency indicators can be broken down into communication equipment energy efficiency indicators and infrastructure energy efficiency indicators. For example, the energy consumption of communication equipment can be quantified as the energy consumed per unit of time, and can be used to assess the energy utilization efficiency and environmental impact of the equipment.

[0084] The method for calculating energy consumption can be adjusted according to the specific communication equipment and application scenario, and typically includes the following aspects:

[0085] a) Energy consumption of communication equipment

[0086] Communication equipment energy consumption refers to the energy consumed by communication equipment during operation, which can be calculated by measuring the equipment's power and operating time. For example, the formula for calculating communication equipment energy consumption can be:

[0087] Energy consumption (kWh) = Power (W) × Operating time (hours) ÷ 1000

[0088] b) System energy consumption

[0089] System energy consumption refers to the energy consumed by the entire communication system during operation. It is related to the interconnection between communication devices and the topology of the communication network. System energy consumption can be calculated by summing the energy consumption of each device in the system.

[0090] c) Communication energy consumption

[0091] Communication energy consumption refers to the energy consumed during communication, which is related to the transmission power, transmission distance, and transmission time of the communication equipment.

[0092] For example, the formula for calculating communication energy consumption can be:

[0093] Energy consumption (kWh) = Transmission power (W) × Transmission time (hours) ÷ 1000

[0094] It should be understood that the above only exemplarily illustrates the indicators related to energy-saving targets and their calculation methods, and this application does not impose any limitations on them.

[0095] (2) Indicators related to data service capability objectives

[0096] The data service capabilities provided by wireless communication networks directly impact user experience. User experience rate is a key metric for measuring data service capabilities and directly influences users' evaluation of network capabilities. The user experience rate metric (hereinafter referred to as rate metric) can be further subdivided into various indicators, such as the cell edge user rate. The cell edge user rate refers to the user experience rate guaranteed within the cell edge area.

[0097] In network planning, different data service capability targets can be defined based on different business scenarios and regional needs. For example, different rate targets can be set for enhanced mobile broadband (eMBB) service scenarios, massive machine-type communication (mMTC) service scenarios, and Internet of Things (IoT) service scenarios.

[0098] (3) Indicators related to capacity targets

[0099] In network planning, the capacity objective is the data throughput capability that the network will achieve after its completion. Network capacity is divided into uplink throughput and downlink throughput. In the capacity calculation of wireless communication networks, uplink throughput and downlink throughput can be combined.

[0100] (4) Indicators related to quality objectives

[0101] From a quality objective perspective, services can be categorized into voice services and data services. For voice services, quality-related metrics may include connection latency and access success rate. For data services, quality-related metrics may include the service channel's frame error rate, bit error rate, throughput, and latency.

[0102] Service connection quality characterizes the speed and ease with which a user can be connected, and can be measured by connection latency and access success rate. Transmission quality reflects the accuracy of the data services received by the user, and can be measured by the frame error rate and bit error rate of the service channel. For data services, throughput and latency can be used to measure service quality.

[0103] (5) Indicators related to coverage targets

[0104] Metrics related to coverage targets can include area coverage, line coverage, and point coverage. Area coverage is the value obtained by dividing the covered area (square kilometers) by the target coverage area (square kilometers). Line coverage is the value obtained by dividing the length of covered roads (kilometers) by the total length of roads (kilometers). Point coverage is the value obtained by dividing the number of covered points by the total number of points. Here, "point" can be understood as a site, such as a base station.

[0105] Based on the above indicators related to the coverage target, the values ​​of the above indicators can be determined by measuring the following parameters: reference signal receiving power (RSRP), reference signal-signal to interference plus noise ratio (RS-SINR), signal to interference ratio (SIR), terminal transmit power, etc.

[0106] RSRP is the received power of the downlink common reference signal, used to reflect the signal strength. RS-SINR represents the ratio of the useful signal to interference and noise floor. RS-SINR can be divided into reference signal RS-SINR and traffic channel SINR. SIR reflects the user channel environment and is correlated with the user experience rate.

[0107] It should be understood that the indicators listed above are merely possible examples, and this application does not limit the types of indicators related to wireless communication network system planning.

[0108] It should also be understood that in the embodiments of this application, the term "indicator" refers to the name of the indicator, and the term "indicator value" refers to the value of the indicator. For example, in "energy efficiency indicator = xx kWh / day", "energy efficiency indicator" is the name of the indicator, and "xx kWh / day" is the value of the indicator. In some embodiments, the indicator and the indicator value have the same meaning and can be used interchangeably.

[0109] (II) Digital Twin Network

[0110] Digital twin networks are used to construct real-time mirrors of physical networks. They are network systems with both physical network entities and virtual twins, allowing for real-time interactive mapping between the two. Within this system, various network management and applications can leverage the virtual twins built using digital twin technology to efficiently analyze, diagnose, simulate, and control the physical network based on data and models. Digital twin networks can enhance the systematic simulation, optimization, verification, and control capabilities lacking in physical networks.

[0111] Network twins can leverage optimization algorithms, management methods, and expert knowledge to analyze, diagnose, simulate, and control physical networks throughout their entire lifecycle. This enables real-time interactive mapping between the physical network and the twin network, helping to deploy various network applications at a lower cost, higher efficiency, and with less impact on the existing network, thus facilitating simplified and intelligent network operation and maintenance.

[0112] In some embodiments of this application, network planning optimization can be performed based on digital twin networks.

[0113] The embodiments of this application will now be described with reference to the accompanying drawings.

[0114] Referring to Figure 1, this is a system architecture provided in an embodiment of this application.

[0115] As shown in Figure 1, the system architecture includes a network management system (NMS) and an element management system (EMS).

[0116] NMS is typically a network management system for mobile operators. NMS is a cross-domain management system, or in other words, NMS can be replaced by a cross-domain management system. NMS can manage multiple EMS. Figure 1 only shows one EMS as an example. This application does not limit the number of EMS managed by NMS.

[0117] EMS is typically a management system for equipment manufacturers. EMS stands for domain management system, or in other words, EMS can be replaced by a domain management system. Generally, one EMS corresponds to one equipment manufacturer, or one equipment manufacturer owns one EMS.

[0118] The NMS and the EMS managed by the NMS can reside in the same operator's operations, administration and maintenance (OAM) system.

[0119] EMS can include performance analysis functional entities and optimization functional entities, or in other words, performance analysis functional entities and optimization functional entities can be deployed in EMS.

[0120] NMS can provide management service interfaces for management service consumers to call. NMS and EMS store communication interfaces. Through these interfaces, management service consumers can interact with the performance analysis and optimization entities within EMS. Optionally, the performance analysis and optimization entities within EMS can communicate with each other through the communication interfaces.

[0121] The aforementioned management service consumers can be located in NMS or vertical industry systems. Management service consumers can be third-party applications or NMS applications; this application does not impose any restrictions in this regard.

[0122] The aforementioned performance analysis function entity can be understood as a service or application. The performance analysis function entity can be implemented in software or a combination of software and hardware; this application does not impose any restrictions on this. The performance analysis function entity can be an independent logical function, implemented through a separate hardware entity (such as a network element), or it can be co-located with other functional entities (such as an optimization function entity) within the same hardware entity.

[0123] The aforementioned optimization function entity can be understood as a service. The optimization function entity can be implemented in software or a combination of software and hardware; this application does not impose any restrictions on this. The optimization function entity can be an independent logical function, which can be implemented through a separate hardware entity (such as a network element) or co-located in the same hardware entity with other functional entities (such as a performance analysis function entity).

[0124] The above division of performance analysis and optimization functions is only one possible example, and this application does not limit it. For example, in another example, the performance analysis and optimization functions in EMS can be implemented through a single entity.

[0125] In the above system architecture, the management service consumer can send a request message to the performance analysis management function entity to request the optimization area. The management service consumer can also send a request message to the optimization function entity to request the optimization results corresponding to the optimization area. The performance analysis function entity can initiate a process to determine the optimization area based on the management consumer's request or independently. In this process, the performance analysis function entity can statistically analyze the network performance indicators of candidate areas based on the requirements of the management service consumer (e.g., indicators specified by the management service consumer), thereby determining the optimization area to be optimized. The optimization function entity can initiate an optimization process based on the request from the management consumer or the performance analysis function entity to optimize the configuration strategy of the wireless communication network system in a simulation environment, obtaining optimization results, which include the network configuration strategy. The optimized configuration strategy can be applied to network elements in the radio access network, thereby optimizing the network. For example, the configuration strategy can be applied to radio access network (RAN) equipment or to network elements in the core network, such as access and mobility management function (AMF) and session management function (SMF).

[0126] Based on the architecture shown in Figure 1, both the performance analysis and optimization functions are deployed in the EMS, meaning that both performance analysis and optimization functions are implemented by the EMS. Since the performance analysis and optimization functions are deployed in an EMS controlled by the equipment manufacturer, the optimization process can be controlled by the equipment manufacturer.

[0127] Referring to Figure 2, another system architecture is provided in an embodiment of this application.

[0128] As shown in Figure 2, this system architecture includes a Network Management System (NMS) and an Element Management System (EMS). For details regarding the NMS, EMS, and service management consumers, please refer to the description of the system architecture shown in Figure 1.

[0129] Unlike the system architecture shown in Figure 1, the system architecture shown in Figure 2 can include performance analysis and optimization functional entities in the NMS, or in other words, performance analysis and optimization functional entities can be deployed in the NMS.

[0130] The above division of performance analysis and optimization functions is only one possible example, and this application does not limit it. For example, in another example, the performance analysis and optimization functions in NMS can be implemented through a single entity.

[0131] Based on the architecture shown in Figure 2, both the performance analysis and optimization functions are deployed in the NMS, meaning that both performance analysis and optimization functions are implemented by the NMS. Since the performance analysis and optimization functions are deployed in an operator-controlled NMS, optimization can be performed under operator control.

[0132] Referring to Figure 3, another system architecture is provided in the embodiments of this application.

[0133] As shown in Figure 3, this system architecture includes a Network Management System (NMS) and a Radio Access Network, or it includes a Network Management System (NMS) and a Core Network, or it includes a Network Management System (NMS), a Radio Access Network, and a Core Network. For details regarding the NMS, EMS, and service management consumers, please refer to the description of the system architecture shown in Figure 1.

[0134] Unlike the system architectures shown in Figures 1 and 2, the system architecture shown in Figure 3 includes a performance analysis function entity within the NMS; in other words, the performance analysis function entity and the optimization function entity can be deployed within the NMS. The NMS can reside within the operator's OAM system.

[0135] The radio access network or core network may include optimization function entities, or optimization function entities may be deployed in network elements in the radio access network, such as base stations, or in network elements in the core network, such as user plane functions (UPFs).

[0136] The NMS can provide a management service interface for management service consumers to call. Through this interface, management service consumers can interact with the performance analysis function entity in the NMS. The optimization function entity in the radio access network (RAN) and the performance analysis function entity in the NMS can communicate via the interface between OAM and RAN network elements (e.g., RAN). The optimization function entity in the core network and the performance analysis function entity in the NMS can communicate via the interface between OAM and core network elements (e.g., UPF). This application does not limit the specific implementation of the above interfaces in its embodiments.

[0137] Based on the architecture shown in Figure 3, the performance analysis function entity is deployed in the NMS, while the optimization function entity is deployed in the radio access network or core network. In other words, both performance metric collection and performance analysis are implemented by the NMS. Since the optimization function entity is deployed in the radio access network or core network, optimization can be controlled by network elements within the radio access network or core network.

[0138] It should be understood that the names of the management service consumer, performance analysis functional entity, and optimization functional entity in the embodiments of this application are only possible examples, and this application does not limit the names of the above functional entities.

[0139] Based on the system architecture shown in Figures 1, 2, or 3 above, Figure 4 shows a flowchart of a communication method provided in an embodiment of this application. Through this process, service management consumers can obtain network configuration policies and apply the network configuration policies to the actual network.

[0140] As shown in Figure 4, the method may include the following steps:

[0141] Step 401: The optimization function entity obtains information about the optimization region and target indicators. The optimization region information is used to indicate the optimization region.

[0142] The optimization area in this application embodiment can be a geographical area, such as a commercial area, residential area, industrial area, etc.

[0143] The optimization area can be a portion of the area covered by the operator's network.

[0144] In one possible implementation, the optimization area is pre-defined. For example, hotspot areas within the operator's network coverage area can be designated as optimization areas. Hotspot areas can include areas with high communication traffic or areas where communication performance needs to be guaranteed, such as business centers or industrial zones. This allows for periodic or ad-hoc optimization of the optimization area to improve its network performance.

[0145] Another possible implementation involves dividing the area covered by the operator's network into multiple candidate areas, collecting metrics from these candidate areas, analyzing the network performance and quality of these candidate areas based on their metric values, and selecting one or more candidate areas for network optimization based on the analysis results. These selected candidate areas are then used as the optimization areas. This allows for network optimization targeting areas within the operator's network with currently poor network performance and quality.

[0146] Optionally, after analyzing the network performance and quality of multiple candidate regions based on the index values ​​corresponding to multiple candidate regions, and selecting one or more candidate regions from multiple candidate regions based on the analysis results, it is also possible to determine the regions adjacent to the selected one or more candidate regions, and use the selected candidate regions and their adjacent regions as optimization regions.

[0147] Optionally, for a candidate region selected from the candidate regions, if there may be related handover operations between the first base station in the candidate region and the second base station in the adjacent region, such as the traffic of the first base station being migrated to the second base station, or the terminal device being switched from the first base station to the second base station, then both the candidate region where the first base station is located and the region where the second base station is located can be used as the optimization region.

[0148] In this embodiment, the candidate area can be obtained by dividing the physical area, such as commercial area, residential area, industrial area, etc. Each area may contain at least one site, which may include network access equipment, such as base station or remote radio unit (RRU), or other equipment, such as building baseband unit (BBU), etc. This application does not limit this.

[0149] Optionally, information about candidate regions can be stored in OAM.

[0150] The optimization function entity can obtain information about the optimization region in the following ways:

[0151] Method 1: The optimization area is pre-defined. In this case, the optimization function entity can obtain the optimization area information from a device or system (e.g., OAM) that stores the optimization area information.

[0152] Method 2: The management service consumer sends the optimization region information to the optimization function entity. The management service consumer can trigger the optimization function entity to execute the optimization process through a request message, which may contain the optimization region information. An example of this implementation can be found in the flowchart shown in Figure 5 or Figure 6.

[0153] Method 3: The performance analysis function entity provides the optimization region information to the optimization function entity. The performance analysis function entity can select the optimization region from multiple candidate regions based on the corresponding indicator values, and then send the optimization region information to the performance analysis function entity. An example of this implementation method can be found in the flowchart shown in Figure 7 or Figure 8.

[0154] It should be understood that when the performance analysis function and the optimization function are combined into one entity, the entity can select the optimization region from multiple candidate regions based on the index values ​​corresponding to multiple candidate regions.

[0155] The aforementioned target indicators may include one or more indicators. For example, target indicators may include energy consumption indicators, or may include both energy consumption indicators and rate indicators. This application does not impose any restrictions on this.

[0156] The aforementioned target metrics can be preset or sent by the management service consumer to the optimization function entity. The management service consumer can trigger the optimization function entity to execute the optimization process through a request message, which may contain the target metrics. An example of this implementation can be found in the flowcharts shown in Figure 5 or Figure 6.

[0157] Step 402: The optimization function entity determines the optimization result corresponding to the optimization region. The optimization result includes a strategy item, which includes the recommended value of the network parameter corresponding to the target indicator and the indicator value corresponding to the recommended value.

[0158] In some embodiments of this application, to improve optimization efficiency and reduce the impact on the existing network, the optimization function entity can determine a network model based on the optimization area, target indicators, and network parameters that need to be adjusted. An iterative optimization process is then performed based on this network model to obtain the optimization result. The coverage area corresponding to this network model matches the optimization area. For example, this network model can be used to simulate the network environment, network topology, and network element configuration within the optimization area.

[0159] The implementation methods for determining the network model for the optimization function entity may include: selecting a suitable network model from existing network models, such as selecting a network model whose coverage matches the coverage corresponding to the optimization area mentioned above. For example, selecting a network model whose coverage is equal to, slightly smaller than, or close to the optimization area mentioned above; or selecting a network model from existing network models that is closest to the optimization requirements (such as including optimization requirements and target indicators) and modifying the network model to suit the current optimization requirements; or creating a suitable network model based on the optimization area and target indicators.

[0160] Optionally, the network model may be a digital twin network model, a management data analysis service (MDAS) model, a self-organizing network (SON) model, or an artificial intelligence / machine learning model; this application does not limit the specific model.

[0161] When optimization is based on a network model, multiple iterations can be executed until a termination condition is met. In each iteration, the network model can output the optimization result for that iteration, which may include a policy term. Each policy term corresponds to a network configuration policy.

[0162] A strategy item can include recommended values ​​for network parameters corresponding to a target metric, as well as the corresponding metric values. The recommended network parameter values ​​can be understood as the specific content of the network configuration strategy recommended or suggested by the network model, such as configuration parameters for network elements. The metric value indicates the potential performance of the network system if the network is configured according to the recommended values ​​of the network parameters. The metric value can be used to evaluate the network configuration strategy.

[0163] In one possible implementation, relevant information about the network parameters (e.g., type, name, or threshold) can be pre-set. The optimization function entity can iterate using the network model based on the pre-set type or name of the network parameters to obtain the recommended value of the network parameter output by the network model.

[0164] In another possible implementation, information about the network parameters (e.g., type, name, or threshold) can be provided to the optimization function entity by the management service consumer. For example, the management service consumer can send a request message to the optimization function entity to trigger the optimization process. This request message can include indication information about the network parameters, indicating that the network model needs to adjust and output the corresponding network parameters.

[0165] In another possible implementation, information related to the network parameters (e.g., type, name, or threshold) can be provided to the optimization entity by the performance analysis entity. For example, the performance analysis entity can send a request message to the optimization entity to trigger the optimization process. This request message can include indications of the network parameters, indicating that the network model needs to adjust and output the corresponding network parameters.

[0166] Optionally, the aforementioned network parameter indication information can be used to indicate the type, name, or threshold of the network parameter, etc. In one possible implementation, the network parameter indication information may include one or more of the following:

[0167] (1) Thresholds for one or more network parameters. For example, maximum antenna tilt angle, RSRP threshold, maximum transmission power, etc.

[0168] (2) Modeling parameter type, which is associated with one or more network parameters. In other words, network parameters can be indicated by the modeling parameter type. For example, radio frequency modeling parameters, L3 mobility modeling parameters, L2 scheduling modeling parameters, etc.

[0169] (3) Indication information of the optimization algorithm, which is associated with one or more modeling parameter types, and each modeling parameter type is associated with one or more network parameters. In other words, the network parameters can be indicated by the optimization algorithm. The modeling parameters may be preset or determined for different optimization algorithms.

[0170] In this embodiment of the application, the strategy item also includes an indicator value. This indicator value can be the indicator value corresponding to the target indicator.

[0171] In one possible implementation, the target indicator (here referring to the indicator name) can be preset, and the optimization function entity can use the network model to iterate according to the preset target indicator to obtain the indicator value of the target indicator output by the network model.

[0172] In another possible implementation, the target metric can be provided to the optimization function entity by the management service consumer. For example, the management service consumer can send a request message to the optimization function entity to trigger the optimization process. This request message can include the target metric, indicating the goal of the network model optimization and the metric value to be output.

[0173] In another possible implementation, the target metric can be provided to the optimization metric by the performance analysis entity. For example, the performance analysis entity can send a request message to the optimization metric to trigger the optimization process. This request message can include the target metric, indicating the goal of the network model optimization and the metric value to be output.

[0174] The number of target indicators can be one or more.

[0175] If there is only one target metric, then one example of a strategy item would be:

[0176] [Index of policy item, target metric and its value, list of network parameters].

[0177] If there are N target metrics (N is an integer greater than 1), then one example of a strategy term would be:

[0178] [Index of strategy item, target metric 1 and its value, target metric 2 and its value, ..., target metric N and its value, network parameter list].

[0179] The index of a policy item is used to uniquely identify a policy item; the network parameter list may include recommended values ​​for one or more network parameters.

[0180] When there are N target metrics, the strategy term output by the network model includes the values ​​of these N target metrics. Among these N target metrics, there is one primary metric, and the others are supporting metrics. Any one of these N target metrics can be used as the primary metric.

[0181] It's understandable that this strategy term is derived by using the primary metric as the key metric and other metrics as complementary metrics in the network model. Accordingly, if the recommended values ​​of the network parameters included in this strategy term are used for network configuration, the performance optimization effect on the primary metric will generally be higher than the optimization effect on the complementary metrics.

[0182] Taking the target indicators, including energy efficiency and rate, as an example, when the network model iterates with energy efficiency as the primary indicator and rate as a secondary indicator, the output strategy term is [Energy Efficiency = x1, Rate = y1, Network Parameter List 1]; when the network model iterates with rate as the primary indicator and energy efficiency as a secondary indicator, the output strategy term is [Rate = y2, Energy Efficiency = x2, Network Parameter List 2]. Here, "x1" and "x2" represent different energy efficiency values, and "y1" and "y2" represent different rate values. The network parameters in Network Parameter List 1 and Network Parameter List 2 have the same names and types, but the recommended value for at least one network parameter in each list may differ.

[0183] In one possible implementation, for iterations other than the first iteration, the optimization result (e.g., the strategy term) may also include the rate of change of the indicator. Taking the Mth iteration as an example (M > 1), the rate of change of the indicator in the optimization result of the Mth iteration is the ratio of the first difference to the indicator value in the strategy term obtained in the (M-1)th or Mth iteration, where the first difference is the difference between the indicator value in the strategy term obtained in the Mth iteration and the indicator value in the strategy term obtained in the (M-1)th iteration.

[0184] For example, the strategy item for the (M-1)th iteration is: [Energy efficiency index = x1, energy efficiency index change rate = 2%, rate index = y1, rate index change rate = 3%, network parameter list 1].

[0185] The strategy term for the Mth iteration is: [Energy efficiency index = x2, energy efficiency index change rate = 5%, rate index = y2, rate index change rate = -1%, network parameter list 2]

[0186] The formula for calculating "energy efficiency index change rate = 5%" is (x2-x1) / x1*100%, which means that the energy efficiency index value in the Mth iteration is 5% higher than that in the (M-1)th iteration. The formula for calculating "rate index change rate = -1%" is (y2-y1) / y1*100%, which means that the rate index value in the Mth iteration is 1% lower than that in the (M-1)th iteration.

[0187] In one possible implementation, during the optimization process in each iteration, the network model uses one of the target metrics as the key metric and the other metrics as coordinating metrics for optimization, thereby obtaining the strategy term corresponding to that iteration. The key metric can be called the primary metric corresponding to the strategy term, and the other metrics can be called coordinating metrics. For example, when optimizing using energy consumption as the key metric and rate as a coordinating metric within the target metrics, the network model can set network parameters with meeting energy consumption requirements as the primary objective and meeting rate requirements as the secondary objective when adjusting network parameters.

[0188] Step 403: The optimization function entity sends the optimization results corresponding to the optimization area to the management service consumer.

[0189] In general, multiple iterations are performed in step 402, and each iteration yields an optimization result (which includes a strategy term).

[0190] In one possible implementation, in step 403, the optimization function entity can send the optimization results obtained in each iteration to the management service consumer. For example, whenever the optimization function entity completes an iteration and obtains the optimization results for that iteration, it can send the optimization results for that iteration to the management service consumer; alternatively, the optimization function entity can wait until all iterations are completed before sending the optimization results for all iterations to the management service consumer. An example of this implementation can be found in the flow shown in Figure 7 or Figure 8.

[0191] Optionally, the optimization results sent by the optimization function entity to the management service consumer in each iteration round may also include the rate of change of indicators, as a basis for the management service consumer to select the optimal strategy.

[0192] Optionally, the optimization results sent by the optimization function entity to the management service consumer for each iteration round may also include an index or sequence number of the iteration round to indicate the order of the iteration rounds. For example, the index of the iteration round is incremented by 1 each time according to the time order of the iteration rounds.

[0193] Optionally, the optimization results sent by the optimization function entity to the management service consumer for each iteration round may also include the duration of each iteration round, so that the management service consumer can understand the execution status of each iteration round and evaluate the execution status (such as the time consumption) of this optimization process.

[0194] In another possible implementation, the optimization function entity can statistically analyze the optimization results of all iteration rounds and send the statistical results to the management service consumer. After statistically analyzing the optimization results of all rounds, at least two strategy items can be obtained. The number of these at least two strategy items is the same as the number of target indicators. Each strategy item corresponds to a primary indicator, and the primary indicators corresponding to different strategy items are different from each other.

[0195] Taking N target indicators as an example, the optimization result sent by the optimization function entity to the management service consumer includes N strategy items. The first strategy item uses the first indicator among the N target indicators as the primary indicator, the second strategy item uses the second indicator among the N target indicators as the primary indicator, the third strategy item uses the third indicator among the N target indicators as the primary indicator, and so on.

[0196] Optionally, among the strategy items obtained in all iteration rounds, for strategy items that use the same metric as the primary metric, the optimization function entity can select the strategy item with the best metric value for that primary metric. For example, it can select the strategy item with the highest metric value improvement based on the rate of change of the primary metric. In this way, the optimal strategy item can be sent to the management service consumer for each target metric.

[0197] Optionally, in the optimization results sent by the optimization function entity to the management service consumer, the first indicator value in each strategy item is the indicator value of the primary indicator, and the subsequent indicator values ​​are the indicator values ​​of the collaborative indicators. Alternatively, other methods can be used to distinguish or identify the primary and collaborative indicators, and this application does not impose any restrictions on this.

[0198] Optionally, the optimization results sent to the management service consumer by the optimization function may also include one or more of the following:

[0199] (1) Number of iterations: The total number of iterations from the start of the optimization iteration to the end of the optimization iteration when the termination condition is met;

[0200] (2) Total iteration duration: The total time from the start of the optimization iteration to the end of the optimization iteration when the termination condition is met;

[0201] (3) Average iteration duration: The total iteration duration divided by the number of iterations.

[0202] By including the above information in the optimization results, the optimization process can be evaluated based on this information.

[0203] Step 404: The management service consumer configures the network based on the strategy items in the optimization results.

[0204] In one possible implementation, if the policy item in the optimization result received by the management service consumer is unique, the network can be configured according to the recommended value of the network parameter in the policy item, such as configuring the network elements in the optimization area.

[0205] In another possible implementation, if the policy items in the optimization results received by the management consumer are statistically derived from policy items across all iteration rounds by the optimization function entity, the management service consumer can select its preferred indicator as the primary indicator from the received policy items based on its own preference for the target indicator, and configure the network system according to the recommended values ​​of the network parameters in that policy item. For example, among the multiple policy items sent by the optimization function entity to the management service consumer, there may be a policy item with energy consumption as the primary indicator and a policy item with rate as the primary indicator. If the management service consumer prefers the energy consumption indicator, it can select the policy item with energy consumption as the primary indicator and configure the network based on that policy item.

[0206] In another possible implementation, if the optimization results received by the management consumer include policy items for each iteration round, the management service consumer can select the policy item with the highest improvement in that metric from these iteration rounds based on its own preference for the target metric. For example, if the management service consumer prefers energy consumption metrics, it can select the policy item with the highest improvement in energy consumption metrics from all iteration rounds based on the rate of change of the metrics included in the policy items. If the management service consumer's preferred metrics include both energy consumption and speed metrics, and energy consumption metrics have the highest priority, the management service consumer can select the policy item with both a high improvement in energy consumption metrics and a high improvement in speed metrics from all iteration rounds based on the rate of change of the metrics included in the policy items, and configure the network based on that policy item.

[0207] It is understood that the above are merely examples of several implementation methods for managing service consumers to select policy items, and the embodiments of this application do not limit this.

[0208] In the process shown in Figure 4 above, since network optimization is performed based on the optimization area, the optimization range can be narrowed and the efficiency of target optimization can be improved compared to network optimization across the entire network.

[0209] Figure 5 is an example of the process shown in Figure 4. In the process shown in Figure 5, the management service consumer can first request information such as the optimization area from the performance analysis function entity, and then request the optimization result from the optimization function entity. Based on the management service consumer's request, the optimization function entity executes the optimization iterative process and returns the statistically analyzed strategy items to the management service consumer.

[0210] Among them, the number of strategy items returned by the optimization function entity to the management service consumer is the same as the number of target indicators. The recommended value of the network parameters included in each strategy item is obtained by using the main indicator corresponding to the strategy item as the key indicator.

[0211] As shown in Figure 5, the process may include the following steps:

[0212] Step 501: The management service consumer sends a second request message to the performance analysis function entity. The second request message is used to request information about the optimization area.

[0213] Optionally, the second request message may include a target metric (such as the name of the target metric). There may be one or more target metrics. This target metric can be used to statistically analyze candidate regions to determine the optimal region.

[0214] Optionally, the second request message may also include indication information of network parameters, which is used to indicate the name, type, or threshold of the network parameters.

[0215] Optionally, the second request message may also include information on constraints, which are used to indicate the conditions that the optimization region must meet.

[0216] In one possible implementation, the constraint information may include the number of regions indication information. Accordingly, the condition that the optimization region should meet is: the K regions with the worst indicators among the candidate regions, where K is the number of regions indicated by the region number indication information.

[0217] In another possible implementation, the constraint information may include tolerance. Accordingly, the optimization region should satisfy the condition that the indicator value in the candidate region meets the tolerance requirement. For example, the tolerance could be a percentage, such as 10%, and the optimization region should satisfy the condition that the indicator value in the candidate region is within 10% of the average of the first indicator value.

[0218] In another possible implementation, the constraint information includes the number of regions and the tolerance. Accordingly, the condition that the optimization region should meet is: among the K candidate regions with the worst index values, the region whose index value meets the tolerance requirement; where K is an integer greater than 1.

[0219] Optionally, the region where the index value meets the tolerance requirement is: the region where the index value is worse than a first value, which is equal to the product of the average of the index values ​​corresponding to the K candidate regions with the worst index values ​​and the first percentage indicated by the tolerance.

[0220] It should be understood that the meaning of "the indicator value is worse than the first value" varies depending on the indicator. For example, if the indicator is rate, then "the indicator value is worse than the first value" means that the rate value is less than the first value; as another example, if the indicator is energy consumption, then "the indicator value is worse than the first value" means that the energy consumption is greater than the first value.

[0221] It should also be understood that the above limitations and information regarding the limitations are merely a few possible examples, and the embodiments of this application do not impose any limitations on them.

[0222] Step 502: After receiving the second request message, the performance analysis function entity selects the optimization region from the candidate regions based on the index values ​​corresponding to the collected candidate regions.

[0223] In this step, the performance analysis function entity can select the optimal region from the candidate regions based on the collected indicator values ​​corresponding to the candidate regions. For example, if the second request message includes a rate indicator, the performance analysis function entity can collect the rate indicator values ​​of the candidate regions and select the optimal region from the candidate regions based on the rate of the candidate regions.

[0224] In one possible implementation, the optimization region is the region whose index value meets the constraints selected from the candidate regions. Taking the constraint information including the number of regions and the tolerance as an example, where the number of regions indicates 10 and the tolerance is 10%, the performance analysis function entity can determine the optimization region in the following way: collect the energy consumption of each candidate region, select the 10 candidate regions with the highest energy consumption, calculate the average energy consumption of these 10 candidate regions, and select the regions among these 10 candidate regions whose energy consumption is 10% higher than the average as the optimization region.

[0225] Optionally, the performance analysis function entity can also include neighboring regions of the selected optimization region from the candidate regions as optimization regions. For example, in the example above, after selecting regions with energy consumption 10% higher than the average from 10 candidate regions, considering the possibility of resource coordination between neighboring regions in region optimization, in addition to including the regions with energy consumption 10% higher than the average from these 10 candidate regions as optimization regions, the neighboring regions of these regions with energy consumption 10% higher than the average from these 10 candidate regions are also included as optimization regions. This implementation method can appropriately expand the scope of the optimization region, thereby improving the rationality and reliability of the optimization results.

[0226] Step 503: The performance analysis function entity sends a second response message to the management service consumer, which includes information about the optimization area.

[0227] Optionally, the information for the optimization area may include geographical indication information, a list of communities, area identifiers, etc., which can be used to indicate the area.

[0228] Optionally, the second response message may also include optimization instruction information, which indicates that optimization needs to be performed based on the optimization area.

[0229] Step 504: The management service consumer sends a first request message to the optimization function entity. The first request message includes information about the optimization area and is used to request the optimization results corresponding to the optimization area.

[0230] In this step, after receiving the second response message, the management service consumer can determine that it needs to initiate an optimization request based on the optimization instruction information carried therein or the optimization area information included in the second response message, and therefore send a first request message to the optimization function entity.

[0231] Optionally, the first request message may also include a target metric (such as the name of the target metric). The target metric can be pre-set or specified by the management service consumer. If there are multiple target metrics, these multiple target metrics form a metric group.

[0232] Optionally, the first request message may also include indication information for network parameters, which indicates the name, type, or threshold of the network parameters. The network parameters may be pre-set or specified by the management service consumer.

[0233] For details on how to implement this step, please refer to the relevant content in Figure 4.

[0234] Step 505: The optimization function entity determines the optimization result corresponding to the optimization region, which includes strategy items. Each strategy item includes the recommended value of the network parameter and the index value corresponding to the recommended value.

[0235] In this process, the optimization result determined by the optimization function entity includes multiple strategy items, which form a strategy item list. The strategy items in this list are obtained by the optimization function entity through statistical analysis of the strategy items obtained in all iteration rounds. The number of strategy items in this list is the same as the number of indicators in the indicator group in the first request message. Each strategy item can be identified as:

[0236] [Primary indicator values, coordinating indicator values, network parameter list].

[0237] The primary and secondary indicators are both from the aforementioned indicator groups. The network parameter list includes recommended values ​​for the network parameters.

[0238] For details on how to implement this step, please refer to the relevant content in Figure 4.

[0239] Step 506: The optimization function entity sends a first response message to the management service consumer entity, which includes the optimization result corresponding to the optimization area obtained in step 505.

[0240] Optionally, the optimization results may also include information such as the number of iterations, the total duration of the iterations, or the average duration of the iterations.

[0241] For details on how to implement this step, please refer to the relevant content in Figure 4.

[0242] Step 507: The management service consumer selects a policy item from the policy item list and configures the network according to the selected policy item.

[0243] For details on how to implement this step, please refer to the relevant content in Figure 4.

[0244] In the process shown in Figure 5 above, steps 501 to 503 are optional steps. Besides obtaining the optimization region information through steps 501 to 503, in other embodiments, the performance analysis function entity can also select an optimization region from the candidate regions according to a set period and trigger the optimization function entity to determine the optimization result corresponding to the optimization region. In another embodiment, the optimization function entity can optimize a pre-specified region (e.g., a business center, industrial zone, etc.) according to a set period to obtain the optimization result corresponding to that region.

[0245] Figure 6 is an example of the process shown in Figure 4. In the process shown in Figure 6, the management service consumer can first request information such as the optimization area from the performance analysis function entity, and then request the optimization result from the optimization function entity (see steps 601 to 603). Based on the management service consumer's request, the optimization function entity executes the optimization iteration process and returns the statistically analyzed strategy items to the management service consumer (see steps 604 to 607).

[0246] Unlike Figure 5, the optimization function entity returns the optimization results obtained in each iteration round to the management service consumer. For each iteration round, the optimization result includes a strategy item for that iteration round. This strategy item may include recommended values ​​for the network parameters corresponding to the target metric and the metric value corresponding to the recommended values ​​(see step 606). Optionally, the strategy item may also include the metric change rate. Accordingly, the management service consumer can select a strategy item from all the strategy items obtained in all iteration rounds based on the metric change rate for network configuration (see step 607). For specific implementation details, please refer to the relevant content in Figure 4.

[0247] Figure 7 is an example of the process shown in Figure 4. In the process shown in Figure 7, the management service consumer can request the performance analysis function entity to determine the optimization region and obtain the corresponding optimization results. After the performance analysis function entity determines the optimization region, it can independently request the optimization results from the optimization function entity based on the above request from the management service entity. Based on the request from the performance analysis function entity, the optimization function entity executes the optimization iteration process and returns the optimization results corresponding to the optimization region to the management service consumer through the performance analysis entity, or directly returns the optimization results corresponding to the optimization region to the management service consumer.

[0248] As shown in Figure 7, the process may include the following steps:

[0249] Step 701: The management service consumer sends a third request message to the performance analysis function entity. The third request message is used to request the determination of the optimization area and to obtain the optimization results corresponding to the optimization area.

[0250] Optionally, the third request message may include a target metric (such as the name of the target metric). There may be one or more target metrics. This target metric can be used to statistically analyze candidate regions to determine the optimal region.

[0251] Optionally, the third request message may also include indication information of network parameters, which is used to indicate the name, type, or threshold of the network parameters.

[0252] Optionally, the third request message may also include information on constraints, which indicates the conditions that the optimization region must meet. For details regarding the "information on constraints," please refer to step 501 in the flowchart shown in Figure 5.

[0253] Optionally, the third request message may also include first indication information, which is used to instruct the performance analysis function entity to request the optimization result corresponding to the optimization region from the optimization function entity after determining the optimization region.

[0254] In one possible implementation, if the third request message contains first indication information, it indicates that the management service consumer requests the performance analysis function entity to independently request the optimization result corresponding to the optimization region after determining the optimization region. In another possible implementation, the third request message contains first indication information. When the value of the first indication information equals a first value, it indicates that the management service consumer requests the performance analysis function entity to independently request the optimization result corresponding to the optimization region after determining the optimization region; when the value of the first indication information equals a second value, it indicates that the management service consumer instructs the performance analysis function entity to return the optimization region information to the management service consumer after determining the optimization region, and the management service consumer determines whether to request the optimization result corresponding to the optimization region from the optimization function entity. For example, the first value equals 1 and the second value equals 0; or the first value equals 0 and the second value equals 1.

[0255] Since the first instruction information can instruct the performance analysis function entity to autonomously request the optimization result corresponding to the optimization region from the optimization function entity, or in other words, instruct the performance analysis function entity to request the optimization result corresponding to the optimization region from the optimization function entity on behalf of the management service consumer, the first instruction information can also be called autonomous optimization instruction information. This application embodiment does not limit the naming of this instruction information.

[0256] Step 702: After receiving the third request message, the performance analysis function entity selects the optimization region from the candidate regions based on the index values ​​corresponding to the collected candidate regions.

[0257] For details on how to implement this step, please refer to the relevant content in Figure 4, or step 502 in the process shown in Figure 5.

[0258] Step 703: The performance analysis function entity sends a fourth request message to the optimization function entity. The fourth request message includes information about the optimization region and is used to request the optimization results corresponding to the optimization region.

[0259] In this step, after the performance analysis function entity determines the optimization area, it can send a fourth request message to the optimization function entity, which includes information about the optimization area.

[0260] Optionally, the fourth request message may also include a target metric (such as the name of the target metric). The target metric can be pre-set or specified by the management service consumer, such as being obtained from the third request message sent by the management service consumer. If there are multiple target metrics, these multiple target metrics form a metric group.

[0261] Optionally, the fourth request message may also include indication information for network parameters, which indicates the name, type, or threshold of the network parameters. The network parameters may be pre-set or specified by the management service consumer, for example, obtained from a third request message sent by the management service consumer.

[0262] For details on how to implement this step, please refer to the relevant content in Figure 4, or step 504 in the process shown in Figure 5.

[0263] Step 704: The optimization function entity determines the optimization result corresponding to the optimization region, which includes strategy items. Each strategy item includes the recommended value of the network parameter and the index value corresponding to the recommended value.

[0264] In this process, the optimization result determined by the optimization function entity includes multiple strategy items, which form a strategy item list. The strategy items in this list are obtained by the optimization function entity through statistical analysis of the strategy items obtained in all iteration rounds. The number of strategy items in this list is the same as the number of indicators in the indicator group in the first request message. Each strategy item can be identified as:

[0265] [Primary indicator values, coordinating indicator values, network parameter list].

[0266] The primary and secondary indicators are both from the aforementioned indicator groups. The network parameter list includes recommended values ​​for the network parameters.

[0267] For details on how to implement this step, please refer to the relevant content in Figure 4.

[0268] Step 705: The optimization function entity sends the optimization results corresponding to the optimization region to the performance analysis function entity.

[0269] Optionally, the optimization results may also include information such as the number of iterations, the total duration of iterations, or the average duration of iterations.

[0270] For details on how to implement this step, please refer to the relevant content in Figure 4.

[0271] Step 706: The performance analysis function entity sends the optimization results corresponding to the optimization area to the management service consumer.

[0272] Step 707: The service consumer selects a policy item from the policy item list and configures the network according to the selected policy item.

[0273] For details on how to implement this step, please refer to the relevant content in Figure 4.

[0274] Similar to the process shown in Figure 7 above, another possible implementation differs from the process shown in Figure 7 in that: the management service consumer or performance analysis function entity can instruct the optimization function entity to directly send the optimization results corresponding to the optimization region to the management service consumer after obtaining them. Accordingly, the optimization function entity can send the optimization results corresponding to the optimization region to the management service consumer without needing to forward them through the performance analysis function entity.

[0275] Figure 8 is an example of the process shown in Figure 4. In the process shown in Figure 8, the management service consumer can request the performance analysis function entity to determine the optimization region and obtain the optimization result corresponding to the optimization region. After the performance analysis function entity determines the optimization region, it can independently request the optimization function entity to obtain the optimization result according to the above request from the management consumer function entity (see steps 801 to 803). Based on the request from the performance analysis function entity, the optimization function entity executes the optimization iteration process and returns the optimization result corresponding to the optimization region to the performance analysis function entity through the performance analysis entity (see steps 804 to 805), or directly returns the optimization result corresponding to the optimization region to the management service consumer.

[0276] Unlike the process shown in Figure 7, the optimization function entity returns the optimization results obtained in each iteration round (see step 805). For each iteration round, the optimization results include a strategy item for that iteration round. This strategy item may include the recommended value of the network parameters corresponding to the target metric and the metric value corresponding to the recommended value. Optionally, the strategy item may also include the metric change rate. Accordingly, the management service consumer can select a strategy item from all the strategy items obtained in all iteration rounds based on the metric change rate for network configuration (see step 806). For specific implementation details, please refer to the relevant content in Figure 4.

[0277] It is understood that, in order to achieve the functions in the above embodiments, the network device and terminal device include hardware structures and / or software modules corresponding to perform each function. Those skilled in the art should readily recognize that, based on the units and method steps of the various examples described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application scenario and design constraints of the technical solution.

[0278] Figures 9 and 10 are schematic diagrams illustrating possible communication devices provided in embodiments of this application. These communication devices can be used to implement the functions of related devices (e.g., performance analysis functional entities, optimization functional entities, or service management consumers) in the above method embodiments, and thus can also achieve the beneficial effects of the above method embodiments. In the embodiments of this application, the communication device can be the above-mentioned device or a module (such as a chip) within the above-mentioned device.

[0279] As shown in Figure 9, the communication device 900 includes a processing unit 910 and a transceiver unit 920. The communication device 900 is used to implement the functions of the related devices in the method embodiments shown in any of the figures 4, 5, 6, 7 or 8 above.

[0280] When the communication device 900 is used to implement the function of the optimization function entity in the method embodiment shown in the above figures: the processing unit 910 is used to obtain information about the optimization area and the target index, wherein the information about the optimization area indicates the optimization area; the processing unit 910 can also send the optimization result corresponding to the optimization area to the management service consumer through the transceiver unit 920 based on the information about the optimization area, wherein the optimization result includes a strategy item, wherein the strategy item includes a recommended value of the network parameter corresponding to the target index and an index value corresponding to the recommended value.

[0281] When the communication device 900 is used to implement the function of the performance analysis function entity in the method embodiment shown in Figure 5 or Figure 6: the transceiver unit 920 is used to receive a second request message from the service management consumer, the second request message being used to request information on the optimization region; the processing unit 910 is used to respond to the second request message and send the information on the optimization region to the management service consumer through the transceiver unit 920, the optimization region including the region selected from the candidate regions based on the index values ​​corresponding to the collected candidate regions.

[0282] When the communication device 900 is used to implement the function of the performance analysis function entity in the method embodiment shown in FIG7 or FIG8: the transceiver unit 920 is used to receive a third request message from the service management consumer, the third request message being used to request the acquisition of the optimization region and the acquisition of the optimization result corresponding to the optimization region; the processing unit 910 is used to respond to the third request message and send the information of the optimization region to the management service consumer through the transceiver unit 920, the optimization region including the region selected from the candidate regions according to the index value corresponding to the collected candidate regions.

[0283] When the communication device 900 is used to implement the function of managing service consumers in the method embodiments shown in Figures 4, 5, 6, 7 or 8: the transceiver unit 920 is used to receive the optimization result corresponding to the optimization area, the optimization result including a strategy item, the strategy item including the recommended value of the network parameter corresponding to the target indicator and the indicator value corresponding to the recommended value; the processing unit 910 is used to perform network configuration according to the strategy item.

[0284] A more detailed description of the processing unit 910 and the transceiver unit 920 can be obtained directly from the relevant descriptions in the method embodiments shown in the above figures, and will not be repeated here.

[0285] Some embodiments of this application provide a communication device 1000, which includes a processing circuit 1010. The processing circuit 1010 may include one or more processors, or all or part of the circuitry in one or more processors for control or processing functions.

[0286] The communication device 1000 may also include a communication circuit 1020.

[0287] The communication circuit 1020 can be a transceiver, a transceiver circuit, or an interface circuit. When the communication device 1000 is a chip used in the device, the communication circuit 1020 can be a transceiver circuit or an interface circuit. Alternatively, the communication circuit can be a transceiver circuit or an interface circuit.

[0288] Optionally, the communication device 1000 may also include a memory 1030. The memory 1030 is used to store instructions executed by the processor, or to store input data required by the processor to run the instructions, or to store data generated after the processor runs the instructions.

[0289] When the communication device 1000 is used to implement the method shown in the above figures, the processor is used to implement the functions of the processing unit and the communication circuit is used to implement the functions of the transceiver unit.

[0290] When the communication device 1000 is a chip applied to the aforementioned device, the chip implements the functions of the corresponding device in the above method embodiments. The chip receives information from other modules (such as radio frequency modules or antennas) in the device, which is sent to the device by other devices; or, the chip sends information to other modules (such as radio frequency modules or antennas) in the device.

[0291] It is understood that the processor in the embodiments of this application may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor may be a microprocessor or any conventional processor.

[0292] This application provides another example of a communication device, which includes at least one processor and at least one memory coupled together. The at least one processor and the at least one memory are used to store instructions. When the instructions are executed by the at least one processor, the communication device performs the methods described in the above embodiments. Taking a communication device including a processor and a memory as an example, as shown in FIG11, communication device 1100 includes a processor 1110 and a memory 1130. The processor 1110 and the memory 1130 are coupled together. The memory 1130 stores instructions. When the instructions stored in the memory 1130 are executed by the processor 1110, the communication device 1100 performs the methods performed by the related devices in the above embodiments.

[0293] It should be understood that the processor 1110 and the memory 1130 can also be integrated together, for example, integrated into a single chip.

[0294] The method steps in the embodiments of this application can be implemented in hardware or in software instructions executable by a processor. The software instructions can consist of corresponding software modules, which can be stored in random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. The storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Alternatively, the ASIC can reside in a network-side device or a terminal-side device. The processor and storage medium can also exist as discrete components in a network-side device or a terminal-side device.

[0295] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a server, a network device, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video optical disc; or it can be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both types of storage media.

[0296] In the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of different embodiments are consistent and can be referenced by each other. The technical features of different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0297] In this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. In the textual description of this application, the character " / " generally indicates an "or" relationship between the preceding and following related objects; in the formulas of this application, the character " / " indicates a "division" relationship between the preceding and following related objects. "Including at least one of A, B, and C" can mean: including A; including B; including C; including A and B; including A and C; including B and C; including A, B, and C.

[0298] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application. The order of the process numbers described above does not imply the order of execution; the execution order of each process should be determined by its function and internal logic.

Claims

1. A communication method, characterized in that, include: Obtain information about the optimization region and target indicators, wherein the information about the optimization region indicates the optimization region; Based on the information of the optimization region, the optimization results corresponding to the optimization region are sent to the management service consumer. The optimization results include strategy items, which include recommended values ​​for network parameters corresponding to the target indicator and indicator values ​​corresponding to the recommended values.

2. The method as described in claim 1, characterized in that, The target indicators include at least two indicators.

3. The method as described in claim 2, characterized in that, The strategy item includes at least two strategy items, the number of the at least two strategy items is the same as the number of the at least two indicators, each of the at least two strategy items corresponds to a main indicator, the main indicator is one of the at least two indicators, and the main indicators corresponding to different strategy items are different from each other; The recommended values ​​for network parameters included in each of the at least two strategy items are obtained by using the main indicator corresponding to the strategy item as the key indicator.

4. The method as described in claim 2, characterized in that, The at least two strategy terms are obtained by statistical analysis of the strategy terms in the optimization results obtained from at least two iteration rounds.

5. The method according to any one of claims 1-2, characterized in that, The optimization result includes the optimization result obtained in each iteration round, wherein the optimization result obtained in the Nth iteration round includes the strategy term obtained in the Nth iteration round, where N is an integer greater than or equal to 1.

6. The method as described in claim 5, characterized in that, The optimization result obtained in the Nth iteration also includes the index change rate, which is the ratio of the first difference to the index value in the strategy item obtained in the (N-1)th or Nth iteration. The first difference is the difference between the index value in the strategy item obtained in the Nth iteration and the index value in the strategy item obtained in the (N-1)th iteration.

7. The method according to any one of claims 1-6, characterized in that, The optimization area refers to a portion of the area covered by the operator's network.

8. The method according to any one of claims 1-7, characterized in that, The optimization region includes: the region selected from the candidate regions based on the index values ​​corresponding to the collected candidate regions.

9. The method as described in claim 8, characterized in that, The optimization region also includes regions adjacent to the regions selected from the candidate regions.

10. The method according to any one of claims 8-9, characterized in that, The optimization region is the region whose index value meets the constraint conditions selected from the candidate regions.

11. The method as described in claim 10, characterized in that, The regions in the candidate regions whose index values ​​meet the restriction conditions are: the regions in the K candidate regions with the worst index values ​​that meet the tolerance requirements; where K is an integer greater than 1.

12. The method as described in claim 11, characterized in that, The region where the index value meets the tolerance requirement is: the region where the index value is worse than a first value, where the first value is equal to the product of the average of the index values ​​corresponding to the K candidate regions and the first percentage of the tolerance indication.

13. The method according to any one of claims 1-12, characterized in that, Also includes: The optimization result corresponding to the optimization region is obtained based on the network model, wherein the coverage area corresponding to the network model matches the optimization region.

14. The method as described in claim 13, characterized in that, The network model is a digital twin network model.

15. The method according to any one of claims 1-14, characterized in that, The process of obtaining information about the optimization region includes: A first request message is received from the management service consumer. The first request message includes information about the optimization region and is used to request the optimization result corresponding to the optimization region.

16. The method as described in claim 15, characterized in that, Before receiving the first request message from the management service consumer, the method further includes: Receive a second request message from the management service consumer, the second request message being used to request information about the optimization area; In response to the second request message, an optimal region is selected from the candidate regions based on the index values ​​corresponding to the collected candidate regions.

17. The method according to any one of claims 1-14, characterized in that, The process of obtaining information about the optimization region includes: Receive a third request message from the management service consumer, the third request message being used to request the determination of the optimization region and to obtain the optimization result corresponding to the optimization region; In response to the third request message, an optimal region is selected from the candidate regions based on the index values ​​corresponding to the collected candidate regions.

18. The method according to any one of claims 1-14, characterized in that, The process of obtaining information about the optimization region includes: According to the optimization cycle, the optimization region is selected from the candidate regions based on the index values ​​corresponding to the collected candidate regions.

19. A communication method, characterized in that, include: Receive a second request message from the service management consumer, the second request message being used to request information about the optimization region; In response to the second request message, information about the optimization region is sent to the management service consumer. The optimization region includes regions selected from the candidate regions based on the index values ​​corresponding to the collected candidate regions.

20. A communication method, characterized in that, include: Receive a third request message from a service management consumer, the third request message being used to request the acquisition of the optimization region and the corresponding optimization result of the optimization region; In response to the third request message, information about the optimization region is sent to the management service consumer. The optimization region includes regions selected from the candidate regions based on the index values ​​corresponding to the collected candidate regions.

21. The method as described in claim 19 or 20, characterized in that, The optimization region also includes candidate regions that are adjacent to the region selected from the candidate regions.

22. The method according to any one of claims 19-21, characterized in that, Also includes: Based on the index values ​​corresponding to the collected candidate regions, regions whose index values ​​meet the constraints are selected from the candidate regions.

23. The method as described in claim 22, characterized in that, The step of selecting regions whose index values ​​meet the constraints from the candidate regions based on the collected candidate region index values ​​includes: Based on the index values ​​corresponding to the collected candidate regions, select the region whose index value meets the tolerance requirement from the K candidate regions with the worst index values; where K is an integer greater than 1.

24. The method as described in claim 23, characterized in that, The step of selecting regions whose index values ​​meet the tolerance requirements from the K candidate regions with the worst index values ​​includes: From the K candidate regions, select the region whose index value is worse than a first value, where the first value is equal to the product of the average index value corresponding to the K candidate regions and a first percentage of the tolerance indication.

25. The method according to any one of claims 23-24, characterized in that, The second request message or the third request message includes region number indication information and / or tolerance, the region number indication information and the tolerance are used to select the optimal region from the candidate regions.

26. The method according to any one of claims 19-25, characterized in that, The optimization area refers to a portion of the area covered by the first operator's network.

27. A communication method, characterized in that, include: Receive the optimization results corresponding to the optimization region. The optimization results include a strategy item, which includes a recommended value for the network parameter corresponding to the target indicator and an indicator value corresponding to the recommended value. Configure the network according to the policy items.

28. The method as described in claim 27, characterized in that, The target metric includes at least two metrics, and the strategy item includes at least two strategy items; The network configuration based on the policy item includes: Based on a preference indicator, select one of the at least two strategy items, wherein the preference indicator is one of the at least two target indicators; Configure the network according to the selected policy.

29. The method according to any one of claims 27-28, characterized in that, Before receiving the optimization result corresponding to the optimization region, the method further includes: Send a first request message, which is used to request the optimization result corresponding to the optimization region.

30. The method as described in claim 29, characterized in that, Before sending the first request message, the method further includes: Send a second request message, which is used to request information about the optimization region; Receive a response message corresponding to the second request message, the response message including information about the optimization region.

31. The method according to any one of claims 27-28, characterized in that, Before receiving the optimization result corresponding to the optimization region, the method further includes: Send a third request message, which is used to request the optimization result corresponding to the optimization region based on the optimization region.

32. A communication device, characterized in that, It includes units or modules for performing the method as described in any one of claims 1-18, or units or modules for performing the method as described in any one of claims 19-26, or units or modules for performing the method as described in any one of claims 27-31.

33. A communication device, characterized in that, include: One or more processors are configured to perform the method as described in any one of claims 1-18, or the method as described in any one of claims 19-26, or the method as described in any one of claims 27-31.

34. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed on the communication device, cause the communication device to perform the method as described in any one of claims 1-18, or the method as described in any one of claims 19-26, or the method as described in any one of claims 27-31.

35. A chip system, characterized in that, Includes a processor for supporting a computer device in implementing the method as claimed in any one of claims 1-18, or implementing the method as claimed in any one of claims 19-26, or performing the method as claimed in any one of claims 27-31.

36. A program product, characterized in that, The program product includes a program; when the program is run on a computer, it causes the computer to perform the method as described in any one of claims 1-18, or the method as described in any one of claims 19-26, or the method as described in any one of claims 27-31.

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