An optimization method, related devices, media, and products

Abstract

The application discloses an optimization method, related equipment, medium and product; wherein the method comprises: sending a first message to a second network device; the first message comprises an optimization strategy of an optimization object, and the optimization strategy instructs the second network device to process an optimization problem of the optimization object; and the optimization strategy comprises multiple optimization objectives and / or multiple optimization constraint conditions of the optimization object.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to an optimization method, related equipment, medium, and product. Background Technology

[0002] In actual network operation, wireless network optimization needs to consider multiple optimization objectives at the same time. These optimization objectives often have mutual constraints, and the constraints also take various forms.

[0003] Related technologies mainly focus on the optimization of a single objective, and have limited ability to handle constraints, making them unable to cope with the optimization of multiple objectives and multiple constraints. Summary of the Invention

[0004] This application provides an optimization method, related equipment, media, and product.

[0005] The technical solution of this application embodiment is implemented as follows:

[0006] An optimization method, applied to a first network device, the method comprising:

[0007] Send a first message to a second network device; the first message includes an optimization strategy for the optimization object, the optimization strategy instructing the second network device to process the optimization problem of the optimization object; the optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object.

[0008] In the above scheme, the optimization strategy includes one or more of the following:

[0009] A first field indicates the relationship between the plurality of optimization objectives;

[0010] The second field indicates the priority of different optimization objectives among the plurality of optimization objectives;

[0011] The third field indicates the weight of the different optimization objectives.

[0012] The method in the above scheme further includes:

[0013] Adjust the priority of the different optimization objectives based on network conditions and / or service requirements; or,

[0014] Configure the priorities of the different optimization objectives based on the range of priority values ​​and / or default values.

[0015] The method in the above scheme further includes:

[0016] Adjust the weights of the different optimization objectives based on network conditions and / or service requirements; or,

[0017] Configure the weights of the different optimization objectives, and the sum of the weights of the different optimization objectives is 1.

[0018] In the above scheme, the condition expression of each of the multiple optimization constraints includes one or more of the following elements: attribute, condition, value corresponding to the attribute, and range of value corresponding to the attribute.

[0019] An optimization method, applied to a second network device, the method comprising:

[0020] The first message sent by the first network device is received; the first message includes an optimization strategy for the optimization object, and the optimization strategy instructs the second network device to process the optimization problem of the optimization object; the optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object.

[0021] The solution to the optimization problem is obtained.

[0022] In the above scheme, obtaining the solution to the optimization problem includes:

[0023] The optimization problem is input into an optimization solver to obtain the objective solution of the optimization problem; the objective solution includes the target wireless access network parameter configuration and / or the target control strategy; the solution settings of the optimization solver include one or more of the following:

[0024] The optimization function and the termination condition for iterative solution are set according to the multiple optimization objectives;

[0025] The initial values ​​of the wireless access network functional characteristic parameters and the optimization problem constraints are set according to the multiple optimization constraints.

[0026] In the above scheme, the step of inputting the optimization problem into an optimization solver to obtain the solution to the optimization problem includes:

[0027] The optimization problem is input into an optimization solver to obtain a first solution to the optimization problem; the first solution includes wireless access network parameter configuration and / or control strategy.

[0028] Based on the first solution, the network performance of the wireless network access network is evaluated. Before the network performance reaches the target performance, the optimization parameters of the optimization solver are iteratively adjusted until the target wireless access network parameter configuration and / or the target control strategy are determined.

[0029] In the above scheme, after obtaining the solution to the optimization problem, the method further includes:

[0030] Send a second message to the base station; the second message includes target radio access network parameter configuration and / or target control policy.

[0031] An optimization device is applied to a first network device, the optimization device comprising:

[0032] A first sending unit is configured to send a first message to a second network device; the first message includes an optimization strategy for an optimization object, the optimization strategy instructing the second network device to process the optimization problem of the optimization object; the optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object.

[0033] An optimization device is applied to a second network device, the optimization device comprising:

[0034] A first receiving unit is configured to receive a first message sent by a first network device; the first message includes an optimization strategy for an optimization object, the optimization strategy instructing the second network device to process the optimization problem of the optimization object; the optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object.

[0035] The first processing unit is used to obtain the solution to the optimization problem.

[0036] A first network device includes a first communication interface and a first processor; wherein,

[0037] The first communication interface is used to send a first message to the second network device; the first message includes an optimization strategy for the optimization object, the optimization strategy instructing the second network device to process the optimization problem of the optimization object; the optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object.

[0038] A second network device includes a second communication interface and a second processor; wherein,

[0039] The second communication interface is used to receive a first message sent by the first network device; the first message includes an optimization strategy for an optimization object, the optimization strategy instructing the second network device to process the optimization problem of the optimization object; the optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object;

[0040] The second processor is used to obtain a solution to the optimization problem.

[0041] A storage medium storing a computer program thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of any of the methods described above on the first network device side, or implements the steps of any of the methods described above on the second network device side.

[0042] A computer product includes a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of any of the methods described above on the first network device side, or implements the steps of any of the methods described above on the second network device side.

[0043] This invention provides an optimization method, related equipment, medium, and product that sends a first message to a second network device. The first message includes an optimization strategy for the optimized object, instructing the second network device to handle the optimization problem of the optimized object. The optimization strategy includes multiple optimization objectives and / or multiple optimization constraints for the optimized object. In other words, this application sends a first message from a first network device to a second network device. The first message includes an optimization strategy for the optimized object, instructing the second network device to handle the optimization problem of the optimized object. The optimization strategy includes multiple optimization objectives and / or multiple optimization constraints for the optimized object, enabling the second network device to handle multiple optimization objectives and diverse constraints in wireless network optimization, improving network performance, and solving the problem of related technologies being unable to handle multi-objective and multi-constraint optimization. Attached Figure Description

[0044] Figure 1 A flowchart illustrating an optimization method provided in an embodiment of this application;

[0045] Figure 2 A schematic diagram of a wireless access network framework provided in an embodiment of this application;

[0046] Figure 3 A flowchart illustrating another optimization method provided in an embodiment of this application;

[0047] Figure 4 A schematic diagram illustrating a solution process for a multi-objective, multi-constraint optimization problem provided in an embodiment of this application;

[0048] Figure 5 A flowchart illustrating the third optimization method provided in this application embodiment;

[0049] Figure 6 This is a schematic diagram of the structure of an optimization device provided in an embodiment of this application;

[0050] Figure 7 This is a schematic diagram of another optimized device provided in an embodiment of this application;

[0051] Figure 8 This is a schematic diagram of the structure of a first network device provided in an embodiment of this application;

[0052] Figure 9 This is a schematic diagram of the structure of a second network device provided in an embodiment of this application. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application are further described in detail below with reference to the accompanying drawings and embodiments. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0054] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0055] The terms "first / second / third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first / second / third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0057] An embodiment of this application provides an optimization method applied to a first network device, referring to... Figure 1 As shown, the method includes the following steps:

[0058] Step S101: Send the first message to the second network device.

[0059] The first message includes the optimization strategy of the optimization object, and the optimization strategy instructs the second network device to handle the optimization problem of the optimization object; the optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object.

[0060] Understandably, wireless network optimization can be performed on the basis of the Open Radio Access Network (O-RAN) architecture, which may include Service Management and Orchestration (SMO) consumers, Non-Real Time RAN Intelligence Controller (Non-RT RIC), and Near-Real Time RAN Intelligent Controller (Near-RT RIC).

[0061] In practical applications, refer to Figure 2 As shown, an SMO consumer can act as a RAN Management Intent Owner (RMIO). SMO consumers include, but are not limited to, End-to-End Management Systems (E2E) or Business Support Systems (BSS). SMO consumers generate optimization requests regarding RAN performance, coverage, and capacity, and translate these into specific RAN optimization intents, such as RAN optimization areas. SMO consumers can send these optimization intents to the SMO through the Service Management Orchestration Function Platform's Network Business Intelligence (SMO NBI) interface.

[0062] The Non-RT RIC can reside within the SMO (Service Management Orchestration Platform) and is used to parse and generate relevant optimization objects, optimization objectives, and optimization constraints. The Non-RT RIC performs in-depth analysis of RAN optimization intentions, identifying specific policy objects that need optimization and setting corresponding optimization policies. These policies can include policy objectives and / or policy resources. A policy object can be understood as an optimization object, and a policy objective as an optimization goal. Optimization policies can also be understood as optimization conditions. Optimization objects can include, but are not limited to, radio cells, frequency bands, and users. After parsing and generation, the Non-RT RIC sends the optimization policy to the Near-RT RIC via the A1 interface. In this embodiment, the optimization policy can also be understood as an A1 policy. The A1 policy can be generated from an A1 policy model and includes, but is not limited to, a policy objective, a policy resource, and a scope identifier. The A1 policy instructs the Near-RT RIC to handle RAN optimization issues to improve RAN performance and efficiency. The first network device can be understood as the Non-RT RIC.

[0063] Near-RT RICs can perform corresponding optimization operations based on the received A1 policy. A Near-RT RIC may include a multi-objective, multi-constraint optimization problem solver and Radio Access Network Digital Twin (RANDT) functionality. The multi-objective, multi-constraint optimization problem solver can generate optimized parameters or controls based on the A1 policy and the performance feedback output by the RANDT after one iteration. For example, the multi-objective solution algorithm may include the penalty function method or the Pareto front method. In each iteration, the parameters or controls generated by the optimization problem solver are input into the RANDT. The RANDT verifies the parameters or controls and outputs RAN performance feedback. Through multiple iterations by the optimization problem solver and the RANDT, the optimal configuration or control for RAN performance is found. The second network device can be understood as a Near-RT RIC.

[0064] The E2 Node acts as a base station, executing RAN parameter configuration or control commands issued by the Near-RT RIC through the E2 interface.

[0065] As can be seen from the above, in this embodiment of the application, a first message is sent from a first network device to a second network device. The first message includes an optimization strategy for the optimization object. The optimization strategy instructs the second network device to handle the optimization problem of the optimization object. The optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object, so as to enable the second network device to handle multiple optimization objectives and diverse constraints in wireless network optimization, improve network performance, and solve the problem that related technologies cannot cope with multi-objective and multi-constraint optimization.

[0066] In some embodiments of this application, the optimization strategy includes one or more of the following:

[0067] The first field indicates the relationship between multiple optimization objectives;

[0068] The second field indicates the priority of different optimization objectives among multiple optimization objectives;

[0069] The third field indicates the weights of different optimization objectives.

[0070] Understandably, optimization goals represent specific outcomes or performance metrics that need to be achieved, such as network throughput, latency, or coverage.

[0071] For example, the regional energy-saving target in the optimization strategy can be configured as follows:

[0072]

[0073] The uplink packet delay target for User Equipment (UE) in the optimization strategy can be configured as follows:

[0074]

[0075]

[0076] In practical applications, A1 policies can express multiple network optimization objectives for the same policy object (a specific ScopeIdenditier), such as network energy saving objectives, network load balancing objectives, and data rate objectives at the cell / user / service / 5G QoS Identifier / slice levels. The A1 policy model can set the relationships between multiple objectives indicated by the first field, and the relationships between multiple objectives can be "AND" or "OR".

[0077] The A1 strategy model allows you to assign a priority value to each objective in the second field, indicating the priority of different optimization objectives. For example, the priorities of multiple optimization objectives can be set as follows:

[0078]

[0079]

[0080] The A1 strategy model allows assigning a weight value to each objective in a third field, indicating the weight of different optimization objectives. For example, the weights of multiple optimization objectives can be set as follows:

[0081]

[0082] Priorities and / or weights can allow certain objectives to have higher priority than others. This way, when conflicts arise or resources are insufficient to satisfy all objectives, prioritizing and fully supporting the achievement of higher-priority objectives is crucial.

[0083] In some embodiments of this application, the method further includes:

[0084] Adjust the priority of different optimization objectives based on network conditions and / or business needs; or,

[0085] Configure the priorities of different optimization targets based on the range of priority values ​​and / or the default values.

[0086] In practical applications, Non-RT RICs can dynamically adjust priorities based on network conditions or business needs. For example, if the importance of a certain service suddenly increases, the priority of policy objectives related to that service can be raised in the A1 policy.

[0087] The A1 strategy model allows defining a range of priority values, where each priority is an integer, with smaller numbers representing higher priorities. For example, a range from 1 to 100, where 1 is the highest priority. If no priority is specified for a target, a default value can be assigned to it.

[0088] In some embodiments of this application, the method further includes:

[0089] Adjust the weights of different optimization objectives based on network conditions and / or business needs; or,

[0090] Configure weights for different optimization objectives, with the sum of the weights for different optimization objectives being 1.

[0091] In practical applications, Non-RT RIC can dynamically adjust weights based on network conditions or service requirements. The A1 policy model can assign a weight value to each objective, and the sum of the weights of all optimization objectives is 1.

[0092] In some embodiments of this application, the condition expression of each constraint in a plurality of optimization constraints includes one or more of the following elements: attribute, condition, value corresponding to the attribute, and range of value corresponding to the attribute.

[0093] Understandably, strategy resources define specific conditions or environmental factors that must be met during the optimization process, which may limit or guide the achievement of objectives.

[0094] For example, the A1 traffic optimization strategy (which includes resource restrictions on candidate cell availability preferences during traffic offloading) can be configured as follows:

[0095]

[0096]

[0097] In practical applications, conditional expressions can be introduced to enhance the representation of constraints. A conditional expression includes the following elements:

[0098] Attribute: Describes a specific property associated with an object or its characteristics, such as "Load" or "Coveragearea".

[0099] Condition: Specifies the expected conditions for the attribute, such as "IS_LESS_THAN", "IS_GREATER_THAN", "IS_EQUAL_TO", or "IS_WITHIN_RANGE".

[0100] ValueRange: Defines the expected value or range of values ​​for a property. It can be a specific number, a range of numbers, or an enumeration set.

[0101] For example, a constraint can be expressed as the data rate decrease not exceeding 30%. The optimization condition can be expressed as PolicyResource = ["Reduced_UE_data_rate","IS_LESS_THAN","30%"].

[0102] Optimization objectives and optimization conditions can be used together to construct more complex decision-making logic for optimization problems.

[0103] In a feasible scenario, the A1 policy configuration for Near-RT RIC needs to simultaneously optimize the energy-saving and load-balancing targets of cell 31. The constraint for traffic offloading in cell 31 is that candidate cells can only be cells 32, 33, and 34, and the cell overlap coverage at the candidate UE's location cannot be less than 3 cells. This can be configured as follows:

[0104]

[0105]

[0106] An embodiment of this application provides an optimization method applied to a second network device, referring to... Figure 4 As shown, the method includes the following steps:

[0107] Step S301: Receive the first message sent by the first network device.

[0108] The first message includes the optimization strategy of the optimization object, and the optimization strategy instructs the second network device to handle the optimization problem of the optimization object; the optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object.

[0109] In practical applications, refer to Figure 2 As shown, SMO consumers can act as RMIOs, and SMO consumers include, but are not limited to, E2E or BSS. SMO consumers generate optimization requests regarding RAN performance, coverage, capacity, etc., and translate these into specific RAN optimization intentions, such as RAN optimization areas. SMO consumers can send these optimization intentions to the SMO via SMO NBI.

[0110] The Non-RT RIC can reside within the SMO (Service Management Orchestration Platform) and is used to parse and generate relevant optimization objects, optimization objectives, and optimization constraints. The Non-RT RIC performs in-depth analysis of RAN optimization intentions, identifying specific policy objects that need optimization and setting corresponding optimization policies. These policies can include policy objectives and / or policy resources. A policy object can be understood as an optimization object, and a policy objective as an optimization goal. Optimization policies can also be understood as optimization conditions. Optimization objects can include, but are not limited to, radio cells, frequency bands, and users. After parsing and generation, the Non-RT RIC sends the optimization policy to the Near-RT RIC via the A1 interface. In this embodiment, the optimization policy can also be understood as an A1 policy. The A1 policy can be generated from an A1 policy model and includes, but is not limited to, a policy objective, a policy resource, and a scope identifier. The A1 policy can instruct the Near-RT RIC to handle RAN optimization issues to improve RAN performance and efficiency. The first network device can be understood as the Non-RT RIC.

[0111] Step S302: Obtain the solution to the optimization problem.

[0112] Near-RT RICs can perform corresponding optimization operations based on the received A1 policy. A Near-RT RIC may include a multi-objective, multi-constraint optimization problem solver and Radio Access Network Digital Twin (RANDT) functionality. The multi-objective, multi-constraint optimization problem solver can generate optimized parameters or controls based on the A1 policy and the performance feedback output by the RANDT after one iteration. For example, the multi-objective solution algorithm may include the penalty function method or the Pareto front method. In each iteration, the parameters or controls generated by the optimization problem solver are input into the RANDT. The RANDT verifies the parameters or controls and outputs RAN performance feedback. Through multiple iterations by the optimization problem solver and the RANDT, the optimal configuration or control for RAN performance is found. The second network device can be understood as a Near-RT RIC.

[0113] The E2 Node acts as a base station, executing RAN parameter configuration or control commands issued by the Near-RT RIC through the E2 interface.

[0114] As can be seen from the above, in this embodiment of the application, a first message is sent from a first network device to a second network device. The first message includes an optimization strategy for the optimization object. The optimization strategy instructs the second network device to handle the optimization problem of the optimization object. The optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object, so as to enable the second network device to handle multiple optimization objectives and diverse constraints in wireless network optimization, improve network performance, and solve the problem that related technologies cannot cope with multi-objective and multi-constraint optimization.

[0115] In some embodiments of this application, solutions to the optimization problem are obtained, including:

[0116] The optimization problem is input into the optimization solver to obtain the objective solution; the objective solution includes the target wireless access network parameter configuration and / or the target control strategy; the optimization solver's solution settings include one or more of the following:

[0117] The optimization function and the termination condition for iterative solution are set according to multiple optimization objectives;

[0118] The initial values ​​of the parameters related to the functional characteristics of the wireless access network are set based on multiple optimization constraints, as well as the constraints of the optimization problem.

[0119] In practical applications, refer to Figure 4As shown, Near-RT RIC maps multiple objectives in PolicyObjective to optimization functions, and sets the achievement of specific numerical values ​​of the objectives as the termination condition for iterative solution of the optimization problem; Near-RT RIC configures PolicyResource as the constraint condition for the optimization problem, as shown below:

[0120] Min / MaxF(x) = weight1*f rate(x) + weight2*f energy consumption(x) + weight3*f coverage(x)

[0121] stg call drop rate (x) <= 0

[0122] g_overlap_cover(x) <= 0

[0123] …

[0124] The optimization solver uses a multi-objective solution algorithm to iteratively find the optimal RAN performance optimization parameters, i.e., the objective solution. The objective solution may include radio access network parameter configuration and / or target control strategies. Radio access network parameters may include cell switching, power control parameters, radio frequency (RF) parameters, and interoperability parameters, etc.

[0125] In some embodiments of this application, the optimization problem is input into an optimization solver to obtain a solution to the optimization problem, including:

[0126] The optimization problem is input into the optimization solver to obtain the first solution to the optimization problem; the first solution includes the wireless access network parameter configuration and / or control strategy.

[0127] The network performance of the wireless network access network is evaluated based on the first solution. Before the network performance reaches the target performance, the optimization parameters of the optimization solver are iteratively adjusted until the target wireless access network parameter configuration and / or target control strategy are determined.

[0128] In practical applications, refer to Figure 4 As shown, during the optimization process, RAN DT calculation and simulation technology can also be used to evaluate and predict the performance of different configuration schemes through model function orchestration and model prediction, and iteratively adjust the optimization parameters of the optimization solver to ensure that the final output configuration can achieve the optimal RAN performance, such as the optimization of user data rate and cell energy consumption.

[0129] In some embodiments of this application, after obtaining a solution to the optimization problem, the method further includes:

[0130] Send a second message to the base station; the second message includes target radio access network parameter configuration and / or target control policy.

[0131] In practical applications, refer to Figure 2 As shown, the E2 Node acts as a base station, executing RAN parameter configuration or control commands issued by the Near-RT RIC through the E2 interface.

[0132] In a feasible scenario, refer to Figure 5 As shown, the optimization method of this application embodiment can be implemented in the following way:

[0133] 1. SMO consumers send RAN optimization intentions, such as RAN optimization areas, to the SMO / Non-RT RIC.

[0134] 2. SMO / Non-RT RIC performs RAN optimization intent parsing:

[0135] 2.1 Determine the specific optimization object based on the area indicated by the intent, such as the cell ID list or UE ID.

[0136] 2.2 SMO collects / retrieves relevant data such as performance, configuration, measurement report (MR) and other network status data of the identified optimization targets.

[0137] 2.3. Through data analysis, determine the optimization objectives, available network resources, and relevant constraints.

[0138] 2.4 Generate specific A1 strategy objectives and A1 strategy resources.

[0139] 3. The Non-RT RIC distributes the A1 strategy to the Near-RT RIC.

[0140] 4. Near-RT RIC settings for solving multi-objective, multi-constraint optimization problems:

[0141] 4.1 Set the optimization function and the termination condition for iterative solution according to PolicyObjective.

[0142] 4.2 Set the initial values ​​of RAN functional characteristic parameters and optimization problem constraints according to PolicyResource.

[0143] 5. Optimize the solver to solve the optimization problem, and adjust the RAN DT related configurations based on the optimized parameters.

[0144] 6. RAN DT calculates and simulates performance indicators under corresponding parameter configurations based on multiple prediction models.

[0145] 7. RAN DT feeds back the output performance metrics to the optimizer.

[0146] 8. Optimize the solver to evaluate whether the performance index has reached the target. If not, recalculate the relevant parameters according to the optimization algorithm and return to step 5. Repeat steps 5, 6, and 7 until the target is met.

[0147] 9. Optimize the solver output to obtain the best performance configuration or control results, and submit them to Near-RT RIC.

[0148] 10. Near-RT RIC sends the generated network configuration or control to the E2 Node via the E2 interface.

[0149] 11. The E2 Node configures parameters or executes actions based on E2 control, thereby completing RAN performance optimization.

[0150] Based on the same inventive concept as described above Figure 6 This is a schematic diagram of an optimization device provided in an embodiment of the present invention, applied to a first network device. The optimization device includes:

[0151] The first sending unit 601 is used to send a first message to the second network device; the first message includes an optimization strategy for the optimization object, and the optimization strategy instructs the second network device to process the optimization problem of the optimization object; the optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object.

[0152] In some embodiments of this application, the optimization strategy includes one or more of the following:

[0153] The first field indicates the relationship between multiple optimization objectives;

[0154] The second field indicates the priority of different optimization objectives among multiple optimization objectives;

[0155] The third field indicates the weights of different optimization objectives.

[0156] In some embodiments of this application, the optimization apparatus further includes: a second processing unit, configured to adjust the priority of different optimization objectives according to network status and / or service requirements; or,

[0157] Configure the priorities of different optimization targets based on the range of priority values ​​and / or the default values.

[0158] In some embodiments of this application, the second processing unit is further configured to adjust the weights of different optimization objectives based on network status and / or service requirements; or,

[0159] Configure weights for different optimization objectives, with the sum of the weights for different optimization objectives being 1.

[0160] In some embodiments of this application, the condition expression of each constraint in a plurality of optimization constraints includes one or more of the following elements: attribute, condition, value corresponding to the attribute, and range of value corresponding to the attribute.

[0161] Based on the same inventive concept as described above Figure 7 This is a schematic diagram of an optimization device provided in an embodiment of the present invention, applied to two network devices. The optimization device includes:

[0162] The first receiving unit 701 is configured to receive a first message sent by the first network device; the first message includes an optimization strategy for the optimization object, and the optimization strategy instructs the second network device to process the optimization problem of the optimization object; the optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object.

[0163] The first processing unit 702 is used to obtain the solution to the optimization problem.

[0164] In some embodiments of this application, the first processing unit 702 is further configured to input the optimization problem into an optimization solver to obtain the target solution of the optimization problem; the target solution includes target wireless access network parameter configuration and / or target control strategy; the solution settings of the optimization solver include one or more of the following:

[0165] The optimization function and the termination condition for iterative solution are set according to multiple optimization objectives;

[0166] The initial values ​​of the parameters related to the functional characteristics of the wireless access network are set based on multiple optimization constraints, as well as the constraints of the optimization problem.

[0167] In some embodiments of this application, the first processing unit 702 is further configured to input the optimization problem into an optimization solver to obtain a first solution to the optimization problem; the first solution includes wireless access network parameter configuration and / or control strategy;

[0168] The network performance of the wireless network access network is evaluated based on the first solution. Before the network performance reaches the target performance, the optimization parameters of the optimization solver are iteratively adjusted until the target wireless access network parameter configuration and / or target control strategy are determined.

[0169] In some embodiments of this application, the optimization apparatus further includes: a second sending unit, configured to send a second message to the base station; the second message includes target radio access network parameter configuration and / or target control strategy.

[0170] Based on the hardware implementation of the above program modules, and in order to implement the method on the first network device side of the embodiments of this application, the embodiments of this application also provide a first network device, such as... Figure 8 As shown, the first network device 800 includes:

[0171] The first communication interface 801 is capable of exchanging information with the second network device;

[0172] The first processor 802 is connected to the first communication interface 801 to enable information interaction with the second network device, and when running a computer program, executes the methods provided by one or more technical solutions on the first network device side.

[0173] The first memory 803 is where the computer program is stored.

[0174] Specifically, the first communication interface 801 is used to send a first message to the second network device; the first message includes the optimization strategy of the optimization object, and the optimization strategy instructs the second network device to process the optimization problem of the optimization object; the optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object.

[0175] In some embodiments of this application, the optimization strategy includes one or more of the following:

[0176] The first field indicates the relationship between multiple optimization objectives;

[0177] The second field indicates the priority of different optimization objectives among multiple optimization objectives;

[0178] The third field indicates the weights of different optimization objectives.

[0179] In some embodiments of this application, the first processor 802 is configured to adjust the priority of different optimization objectives based on network status and / or service requirements; or,

[0180] Configure the priorities of different optimization targets based on the range of priority values ​​and / or the default values.

[0181] In some embodiments of this application, the first processor 802 is configured to adjust the weights of different optimization objectives based on network conditions and / or service requirements; or,

[0182] Configure weights for different optimization objectives, with the sum of the weights for different optimization objectives being 1.

[0183] In some embodiments of this application, the condition expression of each constraint in a plurality of optimization constraints includes one or more of the following elements: attribute, condition, value corresponding to the attribute, and range of value corresponding to the attribute.

[0184] Of course, in practical applications, the various components in the first network device 800 are coupled together through the bus system 804. It can be understood that the bus system 804 is used to implement communication between these components. In addition to a data bus, the bus system 804 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 8 The general labeled all buses as Bus System 804.

[0185] The first memory 803 in this embodiment is used to store various types of data to support the operation of the first network device 800. Examples of such data include any computer program used to operate on the first network device 800.

[0186] The methods disclosed in the above embodiments of this application can be applied to or implemented by the first processor 802. The first processor 802 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware or by instructions in the form of software in the first processor 802. The first processor 802 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The first processor 802 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this application can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium, which is located in the first memory 803. The first processor 802 reads the information in the first memory 803 and completes the steps of the aforementioned method in combination with its hardware.

[0187] In an exemplary embodiment, the first network device 800 may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to perform the aforementioned method.

[0188] Based on the hardware implementation of the above program modules, and in order to implement the method on the second network device side of the embodiments of this application, the embodiments of this application also provide a second network device, such as... Figure 9 As shown, the second network device 900 includes:

[0189] The second communication interface 901 is capable of exchanging information with the first network device;

[0190] The second processor 902 is connected to the second communication interface 901 to enable information interaction with the first network device and to execute the methods provided by one or more technical solutions on the second network device side when running a computer program.

[0191] The computer program is stored in the second memory 903.

[0192] Specifically, the second communication interface 901 is used to receive a first message sent by the first network device; the first message includes an optimization strategy for the optimization object, and the optimization strategy instructs the second network device to process the optimization problem of the optimization object; the optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object;

[0193] The second processor 902 is used to obtain the solution to the optimization problem.

[0194] In some embodiments of this application, the second processor 902 is used to input the optimization problem into an optimization solver to obtain the objective solution of the optimization problem; the objective solution includes the target wireless access network parameter configuration and / or the target control strategy; the solution settings of the optimization solver include one or more of the following:

[0195] The optimization function and the termination condition for iterative solution are set according to multiple optimization objectives;

[0196] The initial values ​​of the parameters related to the functional characteristics of the wireless access network are set based on multiple optimization constraints, as well as the constraints of the optimization problem.

[0197] In some embodiments of this application, the second processor 902 is used to input the optimization problem into an optimization solver to obtain a first solution to the optimization problem; the first solution includes wireless access network parameter configuration and / or control strategy;

[0198] The network performance of the wireless network access network is evaluated based on the first solution. Before the network performance reaches the target performance, the optimization parameters of the optimization solver are iteratively adjusted until the target wireless access network parameter configuration and / or target control strategy are determined.

[0199] In some embodiments of this application, the second processor 902 is used to send a second message to the base station; the second message includes target radio access network parameter configuration and / or target control policy.

[0200] Of course, in practical applications, the various components in the second network device 900 are coupled together via a bus system 904. It can be understood that the bus system 904 is used to implement communication between these components. In addition to a data bus, the bus system 904 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 9 The general labeled all buses as Bus System 904.

[0201] The second memory 903 in this embodiment is used to store various types of data to support the operation of the second network device 900. Examples of such data include any computer program used to operate on the second network device 900.

[0202] The methods disclosed in the embodiments of this application can be applied to, or implemented by, the second processor 902. The second processor 902 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by the integrated logic circuitry of the hardware or by instructions in the form of software within the second processor 902. The second processor 902 can be a general-purpose processor, a DSP, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The second processor 902 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules can be located in a storage medium, specifically a second memory 903. The second processor 902 reads information from the second memory 903 and, in conjunction with its hardware, completes the steps of the aforementioned method.

[0203] In an exemplary embodiment, the second network device 900 may be implemented by one or more ASICs, DSPs, PLDs, CPLDs, FPGAs, general-purpose processors, controllers, MCUs, microprocessors, or other electronic components to perform the aforementioned method.

[0204] It is understood that the memories (first memory 803, second memory 903) in the embodiments of this application can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memories described in the embodiments of this application are intended to include, but are not limited to, these and any other suitable types of memories.

[0205] In an exemplary embodiment, this application also provides a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, such as a first memory 803 storing a computer program, which can be executed by a first processor 802 of a first network device 800 to complete the aforementioned first network device-side method steps. Another example is a second memory 803 storing a computer program, which can be executed by a second processor 902 of a second network device 900 to complete the aforementioned second network device-side method steps. The computer-readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM.

[0206] It should be noted that the aforementioned computer storage media can be ROM, PROM, EPROM, EEPROM, FRAM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM, etc.; or it can be various electronic devices that include one or any combination of the above-mentioned storage media, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0207] Based on the foregoing embodiments, embodiments of this application also provide a computer product, including a computer program, which, when executed by a processor, implements... Figure 1 or Figure 3 The steps in the optimization method provided in the corresponding embodiment.

[0208] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0209] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0210] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0211] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0212] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0213] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0214] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. An optimization method, characterized in that, Applied to a first network device, the method includes: Send a first message to a second network device; the first message includes an optimization strategy for the optimization object, the optimization strategy instructing the second network device to process the optimization problem of the optimization object; the optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object.

2. The method according to claim 1, characterized in that, The optimization strategies include one or more of the following: A first field indicates the relationship between the plurality of optimization objectives; The second field indicates the priority of different optimization objectives among the plurality of optimization objectives; The third field indicates the weight of the different optimization objectives.

3. The method according to claim 2, characterized in that, The method further includes: Adjust the priority of the different optimization objectives based on network conditions and / or service requirements; or, Configure the priorities of the different optimization objectives based on the range of priority values ​​and / or default values.

4. The method according to claim 2, characterized in that, The method further includes: Adjust the weights of the different optimization objectives based on network conditions and / or service requirements; or, Configure the weights of the different optimization objectives, and the sum of the weights of the different optimization objectives is 1.

5. The method according to claim 1, characterized in that, The condition expression for each of the multiple optimization constraints includes one or more of the following elements: attribute, condition, value corresponding to the attribute, and range of the value corresponding to the attribute.

6. An optimization method, characterized in that, Applied to a second network device, the method includes: The first message sent by the first network device is received; the first message includes an optimization strategy for the optimization object, and the optimization strategy instructs the second network device to process the optimization problem of the optimization object; the optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object. The solution to the optimization problem is obtained.

7. The method according to claim 6, characterized in that, Obtaining the solution to the optimization problem includes: The optimization problem is input into an optimization solver to obtain the objective solution of the optimization problem; the objective solution includes the target wireless access network parameter configuration and / or the target control strategy; the solution settings of the optimization solver include one or more of the following: The optimization function and the termination condition for iterative solution are set according to the multiple optimization objectives; The initial values ​​of the wireless access network functional characteristic parameters and the optimization problem constraints are set according to the multiple optimization constraints.

8. The method according to claim 7, characterized in that, The step of inputting the optimization problem into an optimization solver to obtain the solution to the optimization problem includes: The optimization problem is input into an optimization solver to obtain a first solution to the optimization problem; the first solution includes wireless access network parameter configuration and / or control strategy. Based on the first solution, the network performance of the wireless network access network is evaluated. Before the network performance reaches the target performance, the optimization parameters of the optimization solver are iteratively adjusted until the target wireless access network parameter configuration and / or the target control strategy are determined.

9. The method according to claim 7, characterized in that, After obtaining the solution to the optimization problem, the method further includes: Send a second message to the base station; the second message includes target radio access network parameter configuration and / or target control policy.

10. A first network device, comprising a first communication interface and a first processor; wherein, The first communication interface is used to send a first message to the second network device; the first message includes an optimization strategy for the optimization object, the optimization strategy instructing the second network device to process the optimization problem of the optimization object; the optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object.

11. A second network device, comprising a second communication interface and a second processor; wherein, The second communication interface is used to receive a first message sent by the first network device; the first message includes an optimization strategy for an optimization object, the optimization strategy instructing the second network device to process the optimization problem of the optimization object; the optimization strategy includes multiple optimization objectives and / or multiple optimization constraints of the optimization object; The second processor is used to obtain a solution to the optimization problem.

12. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5 or 6 to 9.

13. A computer product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5 or 6 to 9.

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